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https://anclum.com/en/2026-10-20 https://anclum.com/en/[{'id': 1630, 'key': 'storybookjs-storybook-tree-next-code-addons-mcp', 'kind': 'mcp', 'name': 'storybookjs/storybook', 'one_line': '📇 🏠 - Helps agents write and test Storybook stories for UI components', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Storybook is the industry standard workshop for building, documenting, and testing UI components in isolation', 'url': 'https://github.com/storybookjs/storybook/tree/next/code/addons/mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/storybookjs/storybook/tree/next/code/addons/mcp', 'downloads': None, 'stars': 91338, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/storybookjs.png?size=64', 'official_desc': '📇 🏠 - Helps agents write and test Storybook stories for UI components.', 'stars_own': 91338, 'stars_creator': None, 'stars_own_repo': 'storybookjs/storybook', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Build bulletproof UI components faster \n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \nStorybook is a frontend workshop for building UI components and pages in isolation. Thousands of teams use it for UI development, testing, and documentation. Find out more at storybook.js.org !\n \n\n \n \n \n\n \n View README for: \n \n \n \n\nTable of contents\n\n- 🚀 Getting Started\n- 📒 Projects\n - 🛠 Supported Frameworks & Examples\n - 🔗Addons\n- 🏅 Badges & Presentation materials\n- 👥 Community\n- 👏 Contributing\n - 👨\u200d💻 Development scripts\n - 💸 Sponsors\n - 💵 Backers\n- :memo: License\n\nGetting Started\n\nVisit Storybook's website to learn more about Storybook and to get started.\n\nDocumentation\n\nDocumentation can be found on Storybook's docs site.\n\nExamples\n\nView Component Encyclopedia to see how leading teams use Storybook.\n\nUse storybook.new to quickly create an example project in Stackblitz.\n\nStorybook comes with a lot of addons for component design, documentation, testing, interactivity, and so on. Storybook's API makes it possible to configure and extend in various ways. It has even been extended to support React Native, Android, iOS, and Flutter development for mobile.\n\nCommunity\n\nFor additional help, share your issue in the repo's GitHub Discussions.\n\nProjects\n\nSupported Frameworks\n\n| Renderer | Demo | |\n| -------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |\n| React | | |\n| Angular | | |\n| Vue 3 | | |\n| Web components | | |\n| React Native | | |\n| HTML | | |\n| Ember | | |\n| Svelte | | |\n| Preact | | |\n| Qwik | | |\n| SolidJS | | |\n| Android, iOS, Flutter | | |\n\nAddons\n\n| Addons | |\n| ------------------------------------------------------------------------- | -------------------------------------------------------------------------- |\n| a11y | Test components for user accessibility in Storybook |\n| actions | Log actions as users interact with components in the Storybook UI |\n| backgrounds | Let users choose backgrounds in the Storybook UI |\n| cssresources | Dynamically add/remove CSS resources to the component iframe |\n| design assets | View images, videos, and weblinks alongside your story |\n| docs | Add high quality documentation to your components |\n| events | Interactively fire events to components that respond to EventEmitter |\n| google-analytics | Reports google analytics on stories |\n| graphql | Query a GraphQL server within Storybook stories |\n| jest | View the results of components' unit tests in Storybook |\n| links | Create links between stories |\n| measure | Visually inspect the layout and box model within the Storybook UI |\n| outline | Visually debug the CSS layout and alignment within the Storybook UI |\n| query params | Mock query params |\n| viewport | Change display sizes and layouts for responsive components using Storybook |\n\nSee Addon / Framework Support Table\n\nTo continue improving your experience, we have to eventually deprecate or remove certain addons in favor of new and better tools.\n\nIf you're using info/notes, we highly recommend you migrate to docs instead, and here is a guide to help you.\n\nIf you're using contexts, we highly recommend you migrate to toolbars and here is a guide to help you.\n\nIf you're using addon-storyshots, we highly recommend you migrate to the Storybook test-runner and here is a guide to help you.\n\nBadges & Presentation materials\n\nWe have a badge! Link it to your live Storybook example.\n\n \n\n \n\nIf you're looking for material to use in your Storybook presentation, such as logos, video material, and the colors we use, you can find it all on our brand repo.\n\nCommunity\n\n- Tweeting via @storybookjs\n- Blogging at storybook.js.org and Medium\n- Chatting on Discord\n- Videos and streams at YouTube\n\nContributing\n\nContributions to Storybook are always welcome!\n\n- 📥 Pull requests and 🌟 Stars are always welcome.\n- Read our contributing guide to get started,\n or find us on Discord, we will take the time to guide you.\n\nLooking for a first issue to tackle?\n\n- We tag issues with when we think they are well suited for people who are new to the codebase or OSS in general.\n- Talk to us, we'll find something that suits your skills and learning interests.\n\nDevelopment scripts\n\nStorybook is organized as a monorepo. Useful scripts include:\n\nyarn start\n\n> Runs a sandbox template storybook with test stories\n\nyarn task\n\n> As above, but gives you options to customize the sandbox (e.g. selecting other frameworks)\n\nyarn lint\n\n> boolean check if code conforms to linting rules - uses remark & eslint\n\n- yarn lint:js - will check js\n- yarn lint:md - will check markdown + code samples\n- yarn lint:js --fix - will automatically fix js\n\nyarn test\n\n> boolean check if unit tests all pass - uses jest\n\n- yarn run test --core --watch - will run core tests in watch-mode\n\nSponsors\n\nBecome a sponsor to have your logo and website URL on our README on Github. \\[Become a sponsor]\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\nBackers\n\nBy making a recurring donation, you can support us and our work. \\[Become a backer]\n\n \n\nLicense\n\nMIT\n\n-the end-", 'readme_raw_len': 5365, 'one_line_zh': '📇 🏠 - 帮助 Agent 为 UI 组件编写并测试 Storybook 故事', 'one_line_plain': 'MCP helps agents write and test Storybook stories for UI components'}, {'id': 8124, 'key': 'skillsmp-com-creators-outline-outline-claude-skills-verify-outline', 'kind': 'skill', 'name': 'verify-outline', 'one_line': 'Launch an isolated Outline instance (web UI + API, production build, own Postgres/Redis containers) from the…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/outline/outline/claude-skills-verify-outline', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/outline/outline/claude-skills-verify-outline', 'downloads': None, 'stars': 40915, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/outline.png?size=64', 'official_desc': 'Launch an isolated Outline instance (web UI + API, production build, own Postgres/Redis containers) from the current checkout and drive it in a headless browser to prove a user-facing change works, with screenshots, Playwright traces and database checks as evidence. Use when asked to verify, demo, r', 'stars_own': None, 'stars_creator': 40975, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'outline/outline', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '从…启动隔离的 Outline 实例（Web UI + API、生产构建、自有 Postgres/Redis 容器）', 'one_line_plain': 'Launch an isolated Outline instance with a web UI and API, using your own Postgres or Redis containers'}, {'id': 532, 'key': 'lightpanda-io-browser', 'kind': 'mcp', 'name': 'lightpanda-io/browser', 'one_line': '🏠 🍎 🐧 - Headless browser for AI and web automation with a built-in MCP server', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Lightpanda: the headless browser designed for AI and automation', 'url': 'https://github.com/lightpanda-io/browser', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/lightpanda-io/browser', 'downloads': None, 'stars': 36741, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/lightpanda-io.png?size=64', 'official_desc': '🏠 🍎 🐧 - Headless browser for AI and web automation with a built-in MCP server.', 'stars_own': 36741, 'stars_creator': None, 'stars_own_repo': 'lightpanda-io/browser', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Lightpanda Browser \n \n The headless browser built from scratch for AI agents and automation. \nNot a Chromium fork. Not a WebKit patch. A new browser, written in Zig. \n16x lighter and 9x faster than Chromium.\n \n\n \n \n\n \n \n \n \n\n \n \n\n \n\n \n \n\n \n\nBenchmarks\n\nRequesting 933 real web pages over the network on a AWS EC2 m5.large instance.\nSee benchmark details.\n\n| Metric | Lightpanda | Headless Chrome | Difference |\n| :---- | :---- | :---- | :---- |\n| Memory (peak, 100 pages) | 123MB | 2GB | ~16x less |\n| Execution time (100 pages) | 5s | 46s | ~9x faster |\n\nQuick start\n\nInstall\n\n**Package Managers**\n\nLatest nightly from Homebrew:\n \n\nLatest nightly from Arch Linux User Repository:\n \n\n**Download from the nightly builds**\n\nYou can download the last binary from the nightly\nbuilds for\nLinux and MacOS for both x86_64 and aarch64.\n\n*For Linux*\n \n\nVerify the binary before running anything:\n \n\nLinux aarch64 is also available\n\n> **Note:** The Linux release binaries are linked against glibc. On musl-based Linux distributions (Alpine, etc.) the binary fails with cannot execute: required file not found because the glibc dynamic linker is missing. Use a glibc-based base image (e.g., FROM debian:bookworm-slim or FROM ubuntu:24.04) or build from sources.\n>\n> **Android / Termux:** there is no native Android build. The Linux aarch64 binary needs the glibc loader (/lib/ld-linux-aarch64.so.1), which Android's Bionic libc does not provide, so it fails with the same cannot execute: required file not found error.\n\n*For MacOS*\n \n\nMacOS x86_64 is also available\n\n*For Windows + WSL2*\n\nLightpanda has no native Windows binary. Install it inside WSL following the Linux steps above.\n\nWSL not installed? Run wsl --install from an administrator shell, restart, then open wsl.\nSee Microsoft's WSL install guide for details.\n\nYour automation client (Puppeteer, Playwright, etc.) can run either inside WSL or on the Windows host. WSL forwards localhost:9222 automatically.\n\n**Install from Docker**\n\nLightpanda provides official Docker\nimages for both Linux amd64 and\narm64 architectures.\nThe following command fetches the Docker image and starts a new container exposing Lightpanda's CDP server on port 9222.\n \n\nDump a URL\n\n \n\nYou can use --dump markdown to convert directly into markdown, or\n--dump png > page.png or --dump pdf > page.pdf for a text-only rendering\nof the page.\n--wait-until, --wait-ms, --wait-selector and --wait-script are\navailable to adjust waiting time before dump.\n\nStart a CDP server\n\n \nOnce the CDP server started, you can run a Puppeteer script by configuring the\nbrowserWSEndpoint.\n\n \n Example Puppeteer script \n\n \n \n\nStart a webdriver Bidi server\n\nUse --protocol webdriver to enable Bidi support.\nYou can start both, CDP and Bidi, with --protocol webdriver --protocol cdp\n\n \n\nAgent mode\n\nlightpanda agent lets you drive the browser with a native agent. Describe what\nyou want in plain English or with slash commands, and it controls the browser:\nnavigating pages, clicking through flows, filling forms, extracting structured\ndata. Think of it as a robot you're directing to use the web, more than a\nchatbot you're having a conversation with.\n\nBecause the agent runs inside the same process as the browser, every tool call\nis a direct operation and you retain Lightpanda's speed and memory advantage.\n\nThe output of an agent session is a\nPandaScript: vanilla JavaScript\nwith a small set of native browser primitives built directly into Lightpanda.\nRun /save to export one from your current session, then replay it with\nlightpanda run .js. Scripts are deterministic and token-free, so\nyou can prototype with the LLM and ship the output to production without a\nmodel at runtime.\n\nIt supports Anthropic, OpenAI, Gemini, Google Vertex AI, Mistral, Hugging\nFace, the multi-lab gateways Vercel AI Gateway\nand OpenRouter (one key for hundreds of models), any OpenAI-compatible endpoint via\nOPENAI_BASE_URL, and local models via Ollama or llama.cpp. You can also run\nwithout an LLM using --no-llm, which drops you into the REPL. See the\nagent documentation for the full\nreference.\n\n \n\nNative MCP and skill\n\nThe MCP server communicates via MCP JSON-RPC 2.0 over stdio.\n\nAdd to your MCP configuration:\n \n\nHTTP transport and independent sessions\n\nFor serving several agents from one process, start the MCP server over HTTP\ninstead of stdio by giving it a port (add --host x.x.x.x to specify the\ninterface to listen on):\n\n \n\nClients POST JSON-RPC to http://host:9223/mcp. Each connection is routed to\nits own **browsing session** — its own page, cookies and memory — so agents no\nlonger clobber each other's page:\n\n- A client that initializes without an Mcp-Session-Id header is assigned a\n fresh session; the id comes back in the response's Mcp-Session-Id header.\n Send it on subsequent requests to stay on that session (**isolation**).\n- Two agents that send the **same** Mcp-Session-Id share one browsing context\n (**sharing** — e.g. a workflow where several agents work the same page).\n- The session_new, session_list and session_close tools manage sessions\n explicitly. Sending DELETE /mcp with an Mcp-Session-Id closes that session.\n\nRead full documentation\n\nA skill is available in lightpanda-io/agent-skill.\n\nShell completion\n\nlightpanda completion prints a completion script for commands, flags and their values:\n\n \n\nTelemetry\n\nBy default, Lightpanda collects and sends usage telemetry. This can be disabled by setting an environment variable LIGHTPANDA_DISABLE_TELEMETRY=true. You can read Lightpanda's privacy policy at: https://lightpanda.io/privacy-policy.\n\nCore dumps\n\nSet LIGHTPANDA_DISABLE_CORE_DUMP (to any value) to suppress crash core dumps by zeroing the soft RLIMIT_CORE at startup.\n\nStatus\n\nHere are the key features we have implemented.\nFor full details, see our Web Platform Tests results.\n\n- [x] CORS\n- [x] HTTP loader (Libcurl)\n- [x] HTML parser (html5ever)\n- [x] DOM tree\n- [x] Javascript support (v8)\n- [x] DOM APIs\n- [x] Ajax\n - [x] XHR API\n - [x] Fetch API\n- [x] DOM and Markdown dump\n- [x] CDP/websockets server\n- [x] Click\n- [x] Input form\n- [x] Cookies\n- [x] Custom HTTP headers\n- [x] Proxy support\n- [x] Network interception\n- [x] Respect robots.txt with option --obey-robots\n- [x] CDP and Webdriver Bidi\n- [x] Adblocker\n\nBuild from sources\n\nPrerequisites\n\nLightpanda is written with Zig 0.15.2. You have to\ninstall it with the right version in order to build the project.\n\nLightpanda also depends on\nv8,\nLibcurl and html5ever.\n\nTo be able to build the v8 engine, you have to install some libs:\n\nFor **Debian/Ubuntu based Linux**:\n\n \nYou also need to install Rust.\n\nFor systems with **Nix**, you can use the devShell:\n \n\nFor **MacOS**, you need cmake and Rust.\n \n\nBuild and run\n\nYou can build the entire browser with make build or make build-dev for debug\nenv.\n\nBut you can directly use the zig command: zig build run.\n\nEmbed v8 snapshot\n\nLighpanda uses v8 snapshot. By default, it is created on startup but you can\nembed it by using the following commands:\n\nGenerate the snapshot.\n \n\nBuild using the snapshot binary.\n \n\nSee #1279 for more details.\n\nTest\n\nUnit Tests\n\nYou can test Lightpanda by running make test.\n\n \n\nEnd to end tests\n\nTo run end to end tests, you need to clone the demo\nrepository into ../demo dir.\n\nYou have to install the demo's node\nrequirements\n\nYou also need to install Go > v1.24.\n\n \n\nWeb Platform Tests\n\nLightpanda is tested against the standardized Web Platform\nTests.\n\nWe use a fork including a custom\ntestharnessreport.js. Results are published daily.\n\nFor reference, you can execute a WPT test case with your browser via\nwpt.live.\n\nConfigure WPT HTTP server\n\nTo run the test, you must clone the repository, configure the custom hosts and generate the\nMANIFEST.json file.\n\nClone the repository with the fork branch.\n \n\nEnter into the wpt/ dir.\n\nInstall custom domains in your /etc/hosts\n \n\nGenerate MANIFEST.json\n \nUse the WPT's setup\nguide for\ndetails.\n\nRun WPT test suite\n\nAn external Go runner is provided by\ngithub.com/lightpand", 'readme_raw_len': 9748, 'one_line_zh': '🏠 🍎 🐧 - 面向 AI 与网页自动化的无头浏览器，内置 MCP 服务器', 'one_line_plain': 'Run a headless browser for AI and web automation with a built-in MCP server'}, {'id': 4348, 'key': 'jlowin-fastmcp', 'kind': 'mcp', 'name': 'FastMCP', 'one_line': '🐍 - High-level framework for building MCP servers in Python', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/PrefectHQ/fastmcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/jlowin/fastmcp', 'downloads': None, 'stars': 28137, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/jlowin.png?size=64', 'official_desc': 'ef="trendshift.io target="_blank"> The Model Context Protocol (MCP) connects LLMs to tools and data.', 'stars_own': 28137, 'stars_creator': None, 'stars_own_repo': 'PrefectHQ/fastmcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "FastMCP 🚀\n\n Move fast and make things. \n\n*Made with 💙 by Prefect*\n\n \n \n \n \n \n \n\n \n \n\n---\n\nThe Model Context Protocol (MCP) connects LLMs to tools and data. FastMCP is a full MCP application framework for servers, clients, and interactive apps. A server starts with ordinary Python:\n\n \n\nWhy FastMCP\n\nBuilding an effective MCP application is harder than it looks. FastMCP handles all of it. Declare a tool with a Python function, and the schema, validation, and documentation are generated automatically. Connect to a server with a URL, and transport negotiation, authentication, and protocol lifecycle are managed for you. You focus on your logic, and the MCP part just works: **with FastMCP, best practices are built in.**\n\n**That's why FastMCP is the standard framework for working with MCP.** FastMCP 1.0 was incorporated into the official MCP Python SDK in 2024. Today, the actively maintained standalone project is downloaded a million times a day, and some version of FastMCP powers 70% of MCP servers across all languages.\n\nFastMCP has three pillars:\n\n \n \n \n \n \n Servers \n \n Expose tools, resources, and prompts to LLMs.\n \n \n \n \n Apps \n \n Give your tools interactive UIs rendered directly in the conversation.\n \n \n \n \n Clients \n \n Connect to any MCP server — local or remote, programmatic or CLI.\n \n \n \n\n**Servers** wrap your Python functions into MCP-compliant tools, resources, and prompts. **Clients** connect to any server with full protocol support. And **Apps** give your tools interactive UIs rendered directly in the conversation.\n\n**Building in TypeScript?** FastMCP for TypeScript is the official counterpart, built and maintained by the same team. Same pillars, same ideas, npm install @prefecthq/fastmcp-ts.\n\nReady to build? Start with the installation guide or jump straight to the quickstart.\n\nScale MCP with Horizon\n\nFastMCP handles the MCP application layer. **Prefect Horizon** is the enterprise MCP gateway for scaling servers and tools across teams, with centralized governance over how they are deployed, discovered, secured, and used.\n\nFastMCP and Horizon are built by the same team at Prefect.\n\nDeploy FastMCP servers from GitHub with branch previews and instant rollback. Create a private registry of every MCP your company uses. Secure access with SSO and tool-level RBAC. Get audit logs, observability, and governance across your MCP stack. Remix approved tools into purpose-built endpoints for teams and agents.\n\nStart with FastMCP. Scale with Horizon →\n\nInstallation\n\nWe recommend adding FastMCP to your project with uv:\n\n \n\nFor full installation instructions, including verification and upgrading, see the **Installation Guide**.\n\n**Upgrading?** We have guides for:\n- Upgrading from FastMCP 3\n- Upgrading from FastMCP 2\n- Upgrading from MCP SDK v1 or v2\n- Upgrading from the low-level SDK v1 or v2\n\n📚 Documentation\n\nFastMCP's complete documentation is available at **gofastmcp.com**, including detailed guides, API references, and advanced patterns.\n\nDocumentation is also available in llms.txt format, which is a simple markdown standard that LLMs can consume easily:\n\n- llms.txt is essentially a sitemap, listing all the pages in the documentation.\n- llms-full.txt contains the entire documentation. Note this may exceed the context window of your LLM.\n\n**Community:** Join our Discord server to connect with other FastMCP developers and share what you're building.\n\nContributing\n\nStart with CONTRIBUTING.md for accepted contributions and the issue-assignment workflow, then follow the Contributing Guide for setup and validation. Agents should read AGENTS.md.", 'readme_raw_len': 3596, 'one_line_zh': '用于在 Python 中构建 MCP 服务器的高层框架', 'one_line_plain': 'Build MCP servers using a Python framework'}, {'id': 8081, 'key': 'skillsmp-com-creators-pydantic-pydantic-ai-pydantic-ai-slim-pydantic-ai-agents-skills-buil', 'kind': 'skill', 'name': 'building-pydantic-ai-agents', 'one_line': 'Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), workspaces, structured…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/pydantic/pydantic-ai/pydantic-ai-slim-pydantic-ai-agents-skills-building-pydantic-ai-agents', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/pydantic/pydantic-ai/pydantic-ai-slim-pydantic-ai-agents-skills-building-pydantic-ai-agents', 'downloads': None, 'stars': 20650, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/pydantic.png?size=64', 'official_desc': 'Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), workspaces, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, attach a workspace, de', 'stars_own': None, 'stars_creator': 20734, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'pydantic/pydantic-ai', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '用 Pydantic AI 构建 AI Agent：工具、能力（含按需加载）、工作区与结构化…', 'one_line_plain': 'Build AI agents with Pydantic AI — tools, capabilities, workspaces, and structured output'}, {'id': 1431, 'key': 'glips-figma-context-mcp', 'kind': 'mcp', 'name': 'GLips/Figma-Context-MCP', 'one_line': '📇 🏠 - Gives coding agents direct access to Figma data to implement designs in one shot', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server to provide Figma layout information to AI coding agents like Cursor', 'url': 'https://github.com/GLips/Figma-Context-MCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/GLips/Figma-Context-MCP', 'downloads': None, 'stars': 16012, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/GLips.png?size=64', 'official_desc': '📇 🏠 - Gives coding agents direct access to Figma data to implement designs in one shot.', 'stars_own': 16012, 'stars_creator': None, 'stars_own_repo': 'GLips/Figma-Context-MCP', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Framelink MCP for Figma \n Give your coding agent access to your Figma data. Implement designs in any framework in one-shot. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n\nGive Cursor and other AI-powered coding tools access to your Figma files with this Model Context Protocol server.\n\nWhen Cursor has access to Figma design data, it's **way** better at one-shotting designs accurately than alternative approaches like pasting screenshots.\n\n See quickstart instructions → \n\nDemo\n\nWatch a demo of building a UI in Cursor with Figma design data\n\n \n\nHow it works\n\n1. Open your IDE's chat (e.g. agent mode in Cursor).\n2. Paste a link to a Figma file, frame, or group.\n3. Ask Cursor to do something with the Figma file—e.g. implement the design.\n4. Cursor will fetch the relevant metadata from Figma and use it to write your code.\n\nThis MCP server is specifically designed for use with Cursor. Before responding with context from the Figma API, it simplifies and translates the response so only the most relevant layout and styling information is provided to the model.\n\nReducing the amount of context provided to the model helps make the AI more accurate and the responses more relevant.\n\nGetting Started\n\nMany code editors and other AI clients use a configuration file to manage MCP servers.\n\nThe figma-developer-mcp server can be configured by adding the following to your configuration file.\n\n> NOTE: You will need to create a Figma access token to use this server. Instructions on how to create a Figma API access token can be found here.\n\nMacOS / Linux\n\n \n\nWindows\n\n \n\nOr you can set FIGMA_API_KEY and PORT in the env field.\n\nIf you need more information on how to configure the Framelink MCP for Figma, see the Framelink docs.\n\nStar History\n\n \n\nLearn More\n\nThe Framelink MCP for Figma is simple but powerful. Get the most out of it by learning more at the Framelink site.", 'readme_raw_len': 1859, 'one_line_zh': '让编码 Agent 直接访问 Figma 数据，一步到位实现设计还原', 'one_line_plain': 'Give coding agents direct access to Figma data to implement designs instantly'}, {'id': 4178, 'key': 'apify-actors-mcp-server', 'kind': 'mcp', 'name': 'apify/actors-mcp-server', 'one_line': '📇 ☁️ - Use Apify Actors, pre-built cloud tools, to extract data from websites, e-commerce, social media,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/apify/apify-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/apify/actors-mcp-server', 'downloads': None, 'stars': 12050, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/apify.png?size=64', 'official_desc': 'smithery badge"> The Apify Model Context Protocol (MCP) server at **mcp.apify.com** enables your AI agents to extract data from social media, search engines, maps, e-commerce sites, and any other website using thousands of ready-made scrapers, crawlers, and automation tools from Apify Store.', 'stars_own': 12050, 'stars_creator': None, 'stars_own_repo': 'apify/apify-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "mcp.apify.com \n \n\n \n \n \n \n \n \n\nThe Apify Model Context Protocol (MCP) server at **mcp.apify.com** enables your AI agents to extract data from social media, search engines, maps, e-commerce sites, and any other website using thousands of ready-made scrapers, crawlers, and automation tools from Apify Store. It supports OAuth, allowing you to connect from clients like Claude.ai or Visual Studio Code using just the URL.\n\n> **🚀 Use the hosted Apify MCP Server!**\n>\n> For the best experience, connect your AI assistant to our hosted server at **https://mcp.apify.com**. The hosted server supports the latest features - including output schema inference for structured Actor results - that are not available when running locally via stdio.\n\n> ⚠️ **Legacy SSE transport removed.** The https://mcp.apify.com/sse endpoint has been removed in favor of Streamable HTTP. Migrate your client to **https://mcp.apify.com** — drop the /sse suffix from your configuration.\n\n💰 The server also supports agentic payments: buy a token from AGI to run any Actor, or pay per-request via direct x402 (Pay Per Event Actors only) or Skyfire.\n\nApify MCP Server is compatible with Claude Code, Claude.ai, Cursor, VS Code and any client that adheres to the Model Context Protocol.\nCheck out the MCP clients section for more details or visit the MCP configuration page.\n\n \n\nTable of Contents\n- 🌐 Introducing Apify MCP Server\n- 🚀 Quickstart\n- 🤖 MCP clients\n- 🪄 Try Apify MCP instantly\n- 💰 Agentic payments\n - How agentic payments work\n - 🪙 AGI (recommended)\n - 💸 Direct x402\n - 🔥 Skyfire\n- 🛠️ Tools, resources, and prompts\n- 📊 Telemetry\n- 💬 Usage examples\n- 🐛 Troubleshooting\n- ⚙️ Development\n- 🔒 Privacy policy\n- 🤝 Contributing\n- 📚 Learn more\n\n🌐 Introducing Apify MCP Server\n\nThe Apify MCP Server allows an AI assistant to use any Apify Actor as a tool to perform a specific task.\nFor example, it can:\n- Use Facebook Posts Scraper to extract data from Facebook posts from multiple pages/profiles.\n- Use Google Maps Email Extractor to extract contact details from Google Maps.\n- Use Google Search Results Scraper to scrape Google Search Engine Results Pages (SERPs).\n- Use Instagram Scraper to scrape Instagram posts, profiles, places, photos, and comments.\n- Use RAG Web Browser to search the web, scrape the top N URLs, and return their content.\n- Use Web Fetch to fetch any URL and return its content as Markdown, plain text, HTML, or links — with JavaScript rendering and anti-bot protection.\n\n**Video tutorial: Integrate 8,000+ Apify Actors and Agents with Claude**\n\n \n\n🚀 Quickstart\n\nYou can use the Apify MCP Server in two ways:\n\n**HTTPS Endpoint (mcp.apify.com)**: Connect from your MCP client via OAuth or by including the Authorization: Bearer header in your requests. This is the recommended method for most use cases. Because it supports OAuth, you can connect from clients like Claude.ai or Visual Studio Code using just the URL: https://mcp.apify.com.\n- https://mcp.apify.com streamable transport\n\n**Standard Input/Output (stdio)**: Ideal for local integrations and command-line tools like the Claude for Desktop client.\n- Set the MCP client server command to npx @apify/actors-mcp-server and the APIFY_TOKEN environment variable to your Apify API token.\n- See npx @apify/actors-mcp-server --help for more options.\n\nYou can find detailed instructions for setting up the MCP server in the Apify documentation.\n\n🤖 MCP clients\n\nApify MCP Server is compatible with any MCP client that adheres to the Model Context Protocol, but the level of support for dynamic tool discovery and other features may vary between clients.\n \n\nTo interact with the Apify MCP Server, you can use clients such as Claude Desktop, Visual Studio Code, or Apify Tester MCP Client.\n\nVisit mcp.apify.com to configure the server for your preferred client.\n\n \n\nTested clients\n\n- Claude Desktop\n- Claude.ai (web)\n- ChatGPT\n- VS Code (Genie)\n- Cursor\n- OpenCode\n- Kiro\n- Apify Tester MCP Client — designed for testing Apify MCP servers\n\n🪄 Try Apify MCP instantly\n\nWant to try Apify MCP without any setup?\n\nCheck out Apify Tester MCP Client\n\nThis interactive, chat-like interface provides an easy way to explore the capabilities of Apify MCP without any local setup.\nSign in with your Apify account and start experimenting with web scraping, data extraction, and automation tools!\n\nOr use the MCP bundle file (formerly known as Anthropic Desktop extension file, or DXT) for one-click installation: Apify MCP Server MCPB file\n\n💰 Agentic payments\n\nYou can pay for Actor runs without an Apify API token using **AGI**, **direct x402**, or **Skyfire**.\n\n- **AGI** (agi.apify.com) mints a prepaid Apify API token in exchange for an x402 or MPP payment. Use the token like a normal API token against mcp.apify.com and api.apify.com — works for any Actor, not just Pay Per Event ones. **Recommended** for new integrations; see AGI (recommended) below.\n- **Direct x402** pays with USDC on Base per request and does not require a separate platform account. It is fully supported by mcpc (brew install apify/tap/mcpc or npm install -g @apify/mcpc). We use mcpc because it is one of the few MCP clients that supports the latest features and the x402 protocol natively.\n- **Skyfire** pays with PAY tokens and requires a Skyfire account with a funded wallet. It does not require a special MCP client; the entire payment flow is handled directly through the MCP tool call parameters.\n\n> ℹ️ **Scope:** Both direct x402 and Skyfire are limited to Pay Per Event Actors, don't support Standby Actors, and settle per run instead of minting a token.\n\nHow agentic payments work\n\nActor run costs vary, so both payment methods use a prepaid balance model. The payment flow happens in four steps:\n\n1. **Discovery**: The agent discovers Actors with search-actors or fetch-actor-details. Those calls are free.\n2. **Prepayment**: Before running a paid Actor tool, the agent funds a prepaid balance.\n - **Direct x402**: mcpc automatically signs a $1.00 USDC transaction.\n - **Skyfire**: The agent creates a PAY token (minimum $5.00) using Skyfire's create-pay-token tool.\n3. **Execution**: The agent calls the Actor tool.\n - **Direct x402**: Handled automatically by mcpc using the prepaid balance.\n - **Skyfire**: The agent explicitly passes the PAY token in the skyfire-pay-id input property.\n4. **Resolution**: The tool returns the Actor results. Unused funds stay available for later runs.\n - **Direct x402**: After 60 minutes of inactivity, the server refunds any unused balance to the wallet on Base.\n - **Skyfire**: Skyfire returns unused funds when the token expires.\n\n🪙 AGI (recommended)\n\nAGI (Apify Agent General Interface) is the recommended way for autonomous agents to pay for Apify usage without an account. Pay once via x402 or MPP, receive a prepaid, spend-capped Apify API token, and use it directly against mcp.apify.com and api.apify.com (Authorization: Bearer ) — for any Actor.\n\nFull protocol, supported payment methods, and current terms (minimum amount, token lifetime, refund policy) are documented at **agi.apify.com/AGENTS.md** — treat it as the single source of truth.\n\n💸 Direct x402\n\nThe x402 protocol enables direct, machine-to-machine payments. Your MCP client can use it to pay for Actor runs with USDC on the Base blockchain, completely bypassing the need for an Apify API token.\n\nPrerequisites\n\n- A wallet with USDC on Base mainnet.\n\nSetup\n\nCreate or import a wallet:\n\n \n\nConnect to the server with x402 enabled:\n\n \n\nYou can now call a paid tool:\n\n \n\n🔥 Skyfire\n\nSkyfire provides managed payment infrastructure for AI agents. Instead of authenticating with an Apify API token, your agent passes a Skyfire payment token to cover the cost of each tool call using PAY tokens.\n\nPrerequisites\n\n- A Skyfire account with a funded wallet.\n- An MCP client that supports multiple servers, such as Claude Desktop, OpenCode, or VS Code.\n\nSetup\n\nConfigure the Skyfire MCP server and the Apify MCP Server in your client. Add payment=skyfire to the Apify server UR", 'readme_raw_len': 28910, 'one_line_zh': '使用平台上预置的云端工具，从网站、电商与社交媒体中提取数据。', 'one_line_plain': 'Use Apify Actors: extract data from websites, e-commerce, and social media using pre-built cloud tools'}, {'id': 20, 'key': 'skillsmp-com-creators-dart-lang-sdk-agents-skills-dart-aot-development-path-7fe32fc61bf5d6', 'kind': 'skill', 'name': 'dart-aot-development', 'one_line': 'Instructions on building and testing changes using Dart AOT mode', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/dart-lang/sdk/agents-skills-dart-aot-development--path-7fe32fc61bf5d601a137e70cd0c5d745994c371a4f79746b064b849aa2807e99', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/dart-lang/sdk/agents-skills-dart-aot-development--path-7fe32fc61bf5d601a137e70cd0c5d745994c371a4f79746b064b849aa2807e99', 'downloads': None, 'stars': 11303, 'installs': None, 'mentions': 2, 'score': 203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 24, 'icon': 'https://github.com/dart-lang.png?size=64', 'official_desc': 'Instructions on building and testing changes using Dart AOT mode. Use when working on Dart runtime, Dart AOT compiler or other related components.', 'stars_own': None, 'stars_creator': 11312, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'dart-lang/sdk', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '使用 Dart AOT 模式构建与测试变更的说明', 'one_line_plain': 'Guide Dart AOT mode builds and tests for code changes'}, {'id': 602, 'key': 'awslabs-mcp', 'kind': 'mcp', 'name': 'awslabs/mcp', 'one_line': '🎖️ ☁️ - Suite of servers for integrating with AWS services and resources', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Open source MCP Servers for AWS', 'url': 'https://github.com/awslabs/mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/awslabs/mcp', 'downloads': None, 'stars': 9794, 'installs': None, 'mentions': 22, 'score': 2203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/awslabs.png?size=64', 'official_desc': '🎖️ 🐍 ☁️ - Official AWS MCP server for interacting with AWS services using the Model Context Protocol. Enables AI assistants to manage AWS resources through natural language and programmatic interfaces', 'stars_own': 9794, 'stars_creator': None, 'stars_own_repo': 'awslabs/mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Open source MCP servers for AWS\n\nA suite of specialized MCP servers that help you get the most out of AWS, wherever you use MCP.\n\n \n \n \n \n\n> [!TIP]\n> The Agent Toolkit for AWS is now live! The Agent Toolkit for AWS is the successor to the MCP servers, plugins, and skills available on AWS Labs, and was informed by feedback from customers like you. If you're building production software using coding agents or building agents for your own customers, we recommend Agent Toolkit for AWS. It includes IAM condition keys to distinguish agent actions from human ones, CloudWatch and CloudTrail visibility, and skills that have been evaluated for accuracy and effectiveness. This repo continues to work and accept contributions. Over time, the most useful projects here will move into Agent Toolkit for AWS.\n\nTable of Contents\n\n- Open source MCP servers for AWS\n - Table of Contents\n - What is the Model Context Protocol (MCP) and how does it work with MCP Servers for AWS?\n - Open source MCP servers for AWS Transport Mechanisms\n - Supported transport mechanisms\n - Server Sent Events Support Removal\n - Why MCP Servers for AWS?\n - Available MCP Servers: Quick Installation\n - 🚀 Getting Started with AWS\n - Browse by What You're Building\n - 📚 Real-time access to official AWS documentation\n - 🏗️ Infrastructure \\& Deployment\n - Container Platforms\n - Serverless \\& Functions\n - Support\n - 🤖 AI \\& Machine Learning\n - 📊 Data \\& Analytics\n - SQL \\& NoSQL Databases\n - Search \\& Analytics\n - Backend API Providers\n - Caching \\& Performance\n - 🛠️ Developer Tools \\& Support\n - 📡 Integration \\& Messaging\n - 💰 Cost \\& Operations\n - 🧬 Healthcare \\& Lifesciences\n - Browse by How You're Working\n - 👨\u200d💻 Vibe Coding \\& Development\n - Core Development Workflow\n - Infrastructure as Code\n - Application Development\n - Container \\& Serverless Development\n - Testing \\& Data\n - Lifesciences Workflow Development\n - Healthcare Data Management\n - 💬 Conversational Assistants\n - Knowledge \\& Search\n - Content Processing \\& Generation\n - Business Services\n - 🤖 Autonomous Background Agents\n - Data Operations \\& ETL\n - Caching \\& Performance\n - Workflow \\& Integration\n - Operations \\& Monitoring\n - MCP AWS Lambda Handler Module\n - When to use Local vs Remote MCP Servers?\n - Local MCP Servers\n - Remote MCP Servers\n - Use Cases for the Servers\n - Installation and Setup\n - For macOS/Linux\n - For Windows\n - Running MCP servers in containers\n - Getting Started with Kiro\n - ~/.kiro/settings/mcp.json\n - Getting Started with Cline and Amazon Bedrock\n - cline_mcp_settings.json\n - Getting Started with Cursor\n - .cursor/mcp.json\n - Getting Started with Windsurf\n - ~/.codeium/windsurf/mcp_config.json\n - Getting Started with VS Code\n - .vscode/mcp.json\n - Getting Started with Claude Code\n - .mcp.json\n - Getting Started with fx\n - ~/.fx/mcp.json\n - Samples\n - Vibe coding\n - Additional Resources\n - Security\n - Contributing\n - Developer guide\n - License\n - Disclaimer\n\nWhat is the Model Context Protocol (MCP) and how does it work with MCP Servers for AWS?\n\n> The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.\n>\n> — Model Context Protocol README\n\nAn MCP Server is a lightweight program that exposes specific capabilities through the standardized Model Context Protocol. Host applications (such as chatbots, IDEs, and other AI tools) have MCP clients that maintain 1:1 connections with MCP servers. Common MCP clients include agentic AI coding assistants (like Kiro, Cline, Cursor, Windsurf) as well as chatbot applications like Claude Desktop, with more clients coming soon. MCP servers can access local data sources and remote services to provide additional context that improves the generated outputs from the models.\n\nMCP Servers for AWS use this protocol to provide AI applications access to AWS documentation, contextual guidance, and best practices. Through the standardized MCP client-server architecture, AWS capabilities become an intelligent extension of your development environment or AI application.\n\nMCP Servers for AWS enable enhanced cloud-native development, infrastructure management, and development workflows—making AI-assisted cloud computing more accessible and efficient.\n\nThe Model Context Protocol is an open source project run by Anthropic, PBC. and open to contributions from the entire community. For more information on MCP, you can find further documentation here\n\nOpen source MCP servers for AWS Transport Mechanisms\n\nSupported transport mechanisms\n\nThe MCP protocol currently defines two standard transport mechanisms for client-server communication:\n- stdio, communication over standard in and standard out\n- streamable HTTP\n\nThe MCP servers in this repository are designed to support stdio only.\n\nYou are responsible for ensuring that your use of these servers comply with the terms governing them, and any laws, rules, regulations, policies, or standards that apply to you.\n\nServer Sent Events Support Removal\n\n**Important Notice:** On May 26th, 2025, Server Sent Events (SSE) support was removed from all MCP servers in their latest major versions. This change aligns with the Model Context Protocol specification's backwards compatibility guidelines.\n\nWe are actively working towards supporting Streamable HTTP, which will provide improved transport capabilities for future versions.\n\nFor applications still requiring SSE support, please use the previous major version of the respective MCP server until you can migrate to alternative transport methods.\n\nWhy MCP Servers for AWS?\n\nMCP servers enhance the capabilities of foundation models (FMs) in several key ways:\n\n- **Improved Output Quality**: By providing relevant information directly in the model's context, MCP servers significantly improve model responses for specialized domains like AWS services. This approach reduces hallucinations, provides more accurate technical details, enables more precise code generation, and ensures recommendations align with current AWS best practices and service capabilities.\n\n- **Access to Latest Documentation**: FMs may not have knowledge of recent releases, APIs, or SDKs. MCP servers bridge this gap by pulling in up-to-date documentation, ensuring your AI assistant always works with the latest AWS capabilities.\n\n- **Workflow Automation**: MCP servers convert common workflows into tools that foundation models can use directly. Whether it's CDK, Terraform, or other AWS-specific workflows, these tools enable AI assistants to perform complex tasks with greater accuracy and efficiency.\n\n- **Specialized Domain Knowledge**: MCP servers provide deep, contextual knowledge about AWS services that might not be fully represented in foundation models' training data, enabling more accurate and helpful responses for cloud development tasks.\n\nAvailable MCP Servers: Quick Installation\n\nGet started quickly with one-click installation buttons for popular MCP clients. Click the buttons below to install servers directly in Cursor or VS Code:\n\n🚀 Getting Started with AWS\n\nFor AWS interactions, we recommend starting with:\n\n| Server Name | Description | Install |\n|-------------------------------------------------------------------------------------------------------------|-------------|---------|\n| AWS MCP Server (in preview) | Start here for secure, auditable AWS interactions! This remote, managed MCP server is hosted by AWS and combines comprehensive AWS API support with access to the latest AWS documentation, API references, What's New posts, and Getting Started information. Features pre-built Agent SOPs that follow AWS best practices, helping agents complete complex multi-step AWS tasks reliably. Built with safety and control in mind: syntactically validated API calls, IAM-based permissions", 'readme_raw_len': 38934, 'one_line_zh': '用于集成 AWS 服务与资源的服务器套件', 'one_line_plain': 'Integrate with AWS services and resources using a suite of servers'}, {'id': 5735, 'key': 'skillsmp-com-creators-kyegomez-swarms-skill', 'kind': 'skill', 'name': 'swarms', 'one_line': 'Build agents and multi-agent systems with the Swarms framework — the Agent class, tools, autonomous loops,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/kyegomez/swarms/skill', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/kyegomez/swarms/skill', 'downloads': None, 'stars': 7245, 'installs': None, 'mentions': 8, 'score': 803.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/kyegomez.png?size=64', 'official_desc': 'Build agents and multi-agent systems with the Swarms framework — the Agent class, tools, autonomous loops, memory, and the 15+ multi-agent architectures (SequentialWorkflow, ConcurrentWorkflow, GraphWorkflow, HierarchicalSwarm, SwarmRouter, and more). Use whenever writing, reviewing, or debugging co', 'stars_own': None, 'stars_creator': 7280, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'kyegomez/swarms', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '用该框架构建 Agent 与多 Agent 系统，含工具与自主循环等能力。', 'one_line_plain': 'Build agents and multi-agent systems using the Swarms framework, including the Agent class, tools and autonomous loops'}, {'id': 1314, 'key': '21st-dev-magic-mcp', 'kind': 'mcp', 'name': '21st-dev/Magic-MCP', 'one_line': 'Create UI components inspired by 21st.dev design engineers', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': "It's like v0, but in your Cursor / Claude Code / Windsurf: search 10,000+ React/Tailwind components, generate new UI with AI, and publish your own — right from your editor. Magic MCP is now the 21st MCP; this package keeps old configs working. Setup: 21st.dev/mcp", 'url': 'https://github.com/21st-dev/magic-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/21st-dev/magic-mcp', 'downloads': None, 'stars': 6119, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/21st-dev.png?size=64', 'official_desc': 'Create UI components inspired by 21st.dev design engineers.', 'stars_own': 6119, 'stars_creator': None, 'stars_own_repo': '21st-dev/magic-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Magic MCP → 21st MCP\n\n> **Magic MCP is now the 21st MCP.** The Magic MCP server (@21st-dev/magic) has been replaced by the unified **21st MCP**, installed via the **21st CLI** — setup at 21st.dev/mcp. This package remains published as a thin compatibility proxy so old configs keep working.\n\nInstall the 21st MCP (recommended)\n\n \n\nOr add it manually — it is a plain HTTP MCP server:\n\n \n\nGet an API key at 21st.dev/mcp.\n\n> **Old Magic API keys were reset.** Keys issued by the old Magic console no longer work anywhere. Generate a fresh key at 21st.dev/mcp.\n\nWhat this package does now (v0.2.0+)\n\nnpx -y @21st-dev/magic@latest API_KEY="..." still works: since v0.2.0 it is a small stdio proxy that forwards every MCP message to the 21st MCP server. Existing mcp.json entries that reference @21st-dev/magic keep functioning — but they now speak to the same server as the 21st CLI, with the tools available to the authenticated account.\n\nThe API key is accepted in all the historical forms: positional API_KEY="...", --API_KEY=..., /API_KEY:..., -API_KEY ..., or the TWENTY_FIRST_API_KEY / API_KEY_21ST environment variables.\n\nAI access\n\nBuilder component access does not enable hosted 21st AI. The server lists\ngenerate and iterate_generation only when AI access is enabled. Check\nget_usage.aiGenerationEnabled; this reports access, not the remaining AI\ncredit balance. With AI off, use search and get_component, then adapt the\ncode with your own coding agent. Existing drafts remain readable.\n\nA cached or legacy generation call can return ai_subscription_required. Do\nnot retry until AI is enabled. Refresh the client tool list or reconnect after\nenabling AI. The proxy forwards discovery to the server, so existing proxy\nversions receive this behavior without an npm update.\n\nserver.json mirrors the official dev.21st/mcp registry listing. The registry\nversion is independent of this compatibility package version.\n\nOld tool names → new tool names\n\nThe 21st MCP still accepts the legacy Magic tool names and translates them, so agents that remember the old names keep working. Prefer the new names:\n\n| Legacy Magic tool | 21st MCP tool |\n| --- | --- |\n| 21st_magic_component_builder | generate |\n| 21st_magic_component_inspiration | get_inspiration |\n| 21st_magic_component_refiner | generate (new generation from the refinement prompt) |\n| logo_search | search_logo (one query per call) |\n\nThe current server exposes much more than the old four tools: catalog search across components/themes/templates, paid code retrieval, bookmarks, team libraries, UI generation with variants, profile management, and more. Connect and call tools/list to see the tools available to your account.\n\nInstall as a plugin\n\nThis repository is also packaged as an agent plugin (MCP server + a UI skill). You don\'t need to wait for any store — this repo IS a marketplace, add it directly:\n\n \n\nSet your API key in the API_KEY_21ST variable (free key at 21st.dev/mcp).\n\nStore listings:\n\n- **Cursor / Grok Bot** — install "21st" from the Cursor Marketplace, then set your API_KEY_21ST under **Plugins → Configure**. Get a key at 21st.dev/mcp.\n- **Grok Build** — grok plugin install 21st --trust (from the official xAI marketplace), then export API_KEY_21ST.\n- **Claude Code** — the repo carries a .claude-plugin/ manifest: /plugin install it from any marketplace that lists this repo, or load locally with claude --plugin-dir . and export API_KEY_21ST.\n\nThe plugin config expects the API key in the API_KEY_21ST variable in every client.\n\nLinks\n\n- 21st MCP setup & API keys: https://21st.dev/mcp\n- 21st CLI on npm: https://www.npmjs.com/package/@21st-dev/cli\n- 21st.dev — discover, publish, and generate UI components: https://21st.dev\n\nFAQ\n\n**Why did my old Magic MCP stop working?**\nThe Magic backend (magic.21st.dev) was superseded by the unified 21st MCP, and all old API keys were reset for security. Update to a fresh key from 21st.dev/mcp — your existing @21st-dev/magic config will then work again through this compatibility proxy, though we recommend switching to npx @21st-dev/cli@latest init.\n\n**Is /ui still a thing?**\nUse natural language: ask your agent to search 21st for components (search), or use hosted UI generation (generate) when AI is enabled. The old /ui, /21 trigger phrases were a convention of the legacy tools\' descriptions, not the protocol.', 'readme_raw_len': 4347, 'one_line_zh': '参考 21st.dev 设计工程师的作品创建 UI 组件', 'one_line_plain': 'Create UI components inspired by 21st.dev design engineers'}, {'id': 5251, 'key': 'louis-cfm-coucou', 'kind': 'repo', 'name': 'Louis-CFM/coucou', 'one_line': "A tiny friend in your Mac's notch and on your iPhone that keeps an eye on your AI coding agents: Claude Code, Codex, Cursor, Gemini CLI, Antigravity and more. Approve from the notch or your Lock Screen.", 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': "A tiny friend in your Mac's notch and on your iPhone that keeps an eye on your AI coding agents: Claude Code, Codex, Cursor, Gemini CLI, Antigravity and more. Approve from the notch or your Lock Screen.", 'url': 'https://github.com/Louis-CFM/coucou', 'source': 'github', 'source_url': 'https://github.com/Louis-CFM/coucou', 'downloads': None, 'stars': 5579, 'installs': None, 'mentions': 1, 'score': 112.37, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-10', 'times_seen': 1, 'icon': 'https://github.com/Louis-CFM.png?size=64', 'official_desc': "A tiny friend in your Mac's notch and on your iPhone that keeps an eye on your AI coding agents: Claude Code, Codex, Cursor, Gemini CLI, Antigravity and more. Approve from the notch or your Lock Screen.", 'stars_own': 5579, 'stars_creator': None, 'stars_own_repo': 'Louis-CFM/coucou', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Coucou\n\n**A tiny friend that lives in your Mac\'s notch — or at the top of your screen on Windows and Linux — and keeps an eye on your AI coding agent sessions. And now on your phone too: iPhone and Android.**\n\nApprove permissions, watch your agents work, drop a file, chat with Claude — all without leaving what you\'re doing. Walk away from your Mac and Mochi follows you to your iPhone: Lock Screen, Dynamic Island, widgets, Siri.\n\n**New in 0.4: Coucou is a real Mac app.** Hold a key anywhere, speak, let go: your words are pasted where you type, like Wispr Flow — free, open source, and offline with Parakeet. Record your calls without a bot in the meeting and get the transcript, the summary and the to-dos.\n\n \n \n \n \n \n \n \n \n \n \n \n\n \n\n \n\n---\n\nWhy\n\nSome studios showed off gorgeous notch companions… and never let anyone use them.\n**Coucou is the open version.** Every line of code is open source under the MIT License: read it, fork it, learn from it. The Coucou name, Mochi and the sounds stay © Louis Raillé (see License).\n\nMeet **Mochi**: a soft little squircle with big eyes that pops out of your notch, waves hello, follows your cursor with its eyes, gets annoyed when you poke it (and dizzy if you insist), and tells you the moment Claude Code needs you.\n\nCoucou on the Mac: dictation, notes, one window\n\n \n \n \n\nSince 0.4, Coucou has its own window and sits in the Dock. On the left, **Flow** — everything about your voice — and **Notch**, all the settings of the island. *(macOS, GitHub build)*\n\n- 🎙️ **Flow dictation** — hold a key anywhere on the Mac (right ⌥ by default, or fn, right ⌘ or right ⌃), speak, let go: your words are pasted where you type, in any app. Two quick taps for hands-free, Esc to cancel, ⌃⌘V to paste the last one again. Your clipboard gets back what it held.\n- 💊 **The pill** — a small see-through pill at the bottom of the screen (or on its left or right edge). Hover it for a toolbar: dictate, record a call, open Coucou. While you speak, Mochi listens and nine dots follow your voice.\n- 🦜 **Parakeet, offline** — run NVIDIA\'s Parakeet on your Mac\'s Neural Engine through FluidAudio: more accurate, 25 languages, nothing leaves your Mac. One click to download (about 650 MB), Apple silicon only. Until then the Mac\'s own recognition is used.\n- ✍️ **Your words, your way** — a dictionary of names Coucou always spells right, replacements, snippets (say a short phrase, paste a whole text), and an optional clean-up by Claude with your own key: as said, natural or polished, per app.\n- 📝 **Notetaker** — click Record a call: Coucou records your microphone and what your Mac plays, no bot joins the call. Then you get the transcript person by person, a summary, the key points and the to-dos, written by Claude with your key or by Apple Intelligence on your Mac. The audio is deleted once the transcript is written. Tell the people in the call that you record.\n- 📊 **Statistics** — words dictated, time saved, words per minute, days in a row and the apps you dictate in. Counted on your Mac, never sent anywhere.\n\n \n \n \n \n \n \n \n\nFlow dictation and the Notetaker are on the Mac for now; Windows and Linux are next.\n\nFeatures\n\n- 🤖 **Claude Code, Cursor, Codex, Gemini CLI, Antigravity, Copilot CLI, Muse Code, OpenCode, Amp, Hermes, Claude Desktop and other agents, live** — see every session in your notch: what it reads, edits and runs, step by step. Tag a hook payload with coucou_agent to give any agent its own pill (see docs/AGENTS.md). Finished? Mochi does a happy little jump.\n- See what Claude is editing, live in the notch: each file modification shows the file name and +N −M counts in the ticker, tap to read the full diff\n- ✅ **Approve and answer from the notch** — Claude Code permission requests show up with **Allow / Deny / Always**; AskUserQuestion prompts show the choices right in the notch (single or multi-select, up to 4 questions). One click, or "Reply in terminal" to fall back to the CLI. Codex also gets Allow / Deny.\n- 🧑\u200d💻 **Jump to the right terminal** — open the exact terminal window of a session *(macOS)*.\n- 💬 **Chat with Claude, Gemini, OpenAI, DeepSeek, Qwen, Moonshot, or a local model (Ollama / LM Studio)** — click the model name above the chat box to switch provider and pick a model. Cloud providers use your own API key; local providers connect to a server running on your Mac. *(Gemini, OpenAI, DeepSeek, Qwen, Moonshot and local models: macOS)*\n- 📈 **Portfolio pill** *(macOS)* — track your crypto across 16 networks (Bitcoin, Ethereum, Solana, Polygon, BNB, Avalanche, Arbitrum, Optimism, Base, Dogecoin, Litecoin, TRON, XRP, Cardano, Cosmos, NEAR) and 5 exchanges (Binance, OKX, Bybit, Kraken, Coinbase) in the notch. Live balances, 7-day sparklines and 24-hour price change per holding. Read-only API keys for exchanges, public addresses for wallets — nothing leaves your Mac.\n- 📊 **Claude plan usage** *(macOS, GitHub build)* — a small pill in the notch header shows your 5-hour and weekly Claude plan limits. Enable it from Settings → Agents → Plan usage. Pro and Max plans only.\n- 📊 **Codex plan usage** *(macOS, GitHub build)* — a Codex pill next to it shows your Codex limits and how many free resets you have left, read from the Codex CLI. Turn it on in Settings → Agents → Plan usage.\n- 📋 **Declare the tools you use** — open Settings → Active pills and pick your main workspace tool (VS Code, Cursor, Codex or Antigravity), then toggle up to 4 more: Gemini CLI, Anthropic, Google AI, OpenAI, Ollama, LM Studio and service integrations *(macOS)*.\n- 📎 **Drop a file on the notch** — Mochi turns into a box and swallows it, then ask a question about it or send it by email *(email: macOS, Mail.app)*.\n- 🖥️ **Mochi on the desktop** — drag Mochi out of the island to set him loose on your desktop: he floats as a 120 pt companion, follows your cursor, wears his outfit, reacts to alerts by flying home and flying back, and comes back where you left him on next launch.\n- 🪟 **Drag Mochi onto any window** — attach that window as context for Claude *(macOS)*.\n- 🔌 **Integrations** — Stripe payments, n8n workflows, GitHub (open PRs, reviews requested, CI status), Vercel deployments, Resend emails, Notion, Cal.com. Each one gets its own little colored Mochi.\n- 🎵 **Apple Music pill** *(macOS, GitHub build)* — add the Apple Music pill in Settings → Active pills to see what\'s playing and control playback from the notch; Mochi dances while it plays.\n- 🎧 **Spotify pill** *(macOS, GitHub build)* — add the Spotify pill in Settings → Active pills: album cover, title and artist, a progress bar you drag to seek, play/pause, previous/next, shuffle, repeat and volume, right from the notch; Mochi dances while it plays.\n- 👗 **Dress Mochi up** — right-click him for the wardrobe. He also dresses up for the seasons on his own.\n- ⌨️ **Keyboard shortcuts** — open the chat, jump to an alert or a terminal, switch pills, mute, send Mochi to the desktop or open the wardrobe from anywhere; all customizable in Settings → Shortcuts.\n- 🎭 **A real character** — idle breathing, blinks, eyes on a sphere that follow your mouse, emotes, 28 handcrafted sounds, a greeting on launch.\n- 🔊 **Your own sounds** *(macOS)* — drop a file named like one of Mochi\'s sounds (finish.wav, approval.mp3…) in Settings → General → Sound → **Open sounds folder** to replace it.\n- 🗣️ **«\u202fOK Coucou\u202f»** *(macOS, GitHub build)* — talk to Mochi like Jarvis. Say «\u202fOK Coucou\u202f», then ask: «\u202fhow much did I make on Stripe today?\u202f», «\u202fswap GitHub for Stripe\u202f», «\u202fplay some Daft Punk\u202f», «\u202fwho won the game last night?\u202f», «\u202fsend an email to Enzo to tell him the image is ready\u202f». Claude is the brain (your own Anthropic API key, set in Settings → Chat); speech recognition runs on your Mac, and only your words and the data Coucou reads to answer go to Anthropic, never audio. Mochi listens, thinks and answers out loud; emails open in the notch and leave only when you click Send. Settings → Voice.\n- 🎙️ **Dictate in the chat** *(macOS, GitHub build)* — clic', 'readme_raw_len': 30717, 'one_line_zh': '住在 Mac 刘海与 iPhone 上、帮你盯住 AI 编程 Agent 的小助手，可在刘海或锁屏上批准操作', 'one_line_plain': "Watch your AI coding agents from your Mac's menu bar or iPhone lock screen and approve their actions"}, {'id': 5317, 'key': 'evalstate-fast-agent', 'kind': 'repo', 'name': 'evalstate/fast-agent', 'one_line': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'url': 'https://github.com/evalstate/fast-agent', 'source': 'github', 'source_url': 'https://github.com/evalstate/fast-agent', 'downloads': None, 'stars': 3943, 'installs': None, 'mentions': 1, 'score': 110.86, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-19', 'times_seen': 18, 'icon': 'https://github.com/evalstate.png?size=64', 'official_desc': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'stars_own': 3943, 'stars_creator': None, 'stars_own_repo': 'evalstate/fast-agent', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'uvx fast-agent-mcp@latest', 'install_src': 'HEAD/README.md', 'install_conf': 'medium', 'desc_src': 'github-api', 'readme_body': 'Start Here\n\n> [!TIP]\n> Please see https://fast-agent.ai for latest documentation.\n\n**fast-agent** is a flexible way to interact with LLMs, excellent for use as a Coding Agent, Development Toolkit, Evaluation or Workflow platform.\n\nTo start an interactive session with shell support, install uv and run\n\n \n\nTo start coding with Hugging Face inference providers or use your OpenAI Codex plan:\n\n \n\nEnter a shell with !, or run shell commands e.g. ! cd web && npm run build.\n\nManage skills with the /skills command, and connect to MCP Servers with /connect. The default **fast-agent** registry contains skills to let you set up LSP, Agent and Tool Hooks, Compaction strategies, Automation and more.\n\n \n\nIt\'s recommended to install **fast-agent** to set up the shell aliases and other tooling.\n\n \n\nUse local models with the generic provider, or automatically create the correct configuration for llama.cpp:\n\n \n\nAny **fast-agent** setup or program can be used with any ACP client - the simplest way is to use fast-agent-acp:\n\n \n\n**fast-agent** enables you to create and interact with sophisticated multimodal Agents and Workflows in minutes. It is the first framework with complete, end-to-end tested MCP Feature support including Sampling and Elicitations.\n\nfast-agent is CLI-first, with an optional prompt_toolkit-powered interactive terminal prompt (TUI-style input, completions, and in-terminal menus); responses can stream live to the terminal via rich without relying on full-screen curses UIs or external GUI overlays.\n\n \n\nThe simple declarative syntax lets you concentrate on composing your Prompts and MCP Servers to build effective agents.\n\nModel support is comprehensive with native support for Anthropic, OpenAI and Google providers as well as Azure, Ollama, Deepseek and dozens of others via TensorZero. Structured Outputs, PDF and Vision support is simple to use and well tested. Passthrough and Playback LLMs enable rapid development and test of Python glue-code for your applications.\n\nRecent features include:\n\n- Agent Skills (SKILL.md)\n- MCP Apps\n- OpenAI Apps SDK\n- Shell Mode\n- Advanced MCP Transport Diagnsotics\n- MCP Elicitations\n\n \n\nfast-agent is the only tool that allows you to inspect Streamable HTTP Transport usage - a critical feature for ensuring reliable, compliant deployments. OAuth is supported with KeyRing storage for secrets. Use the fast-agent auth command to manage.\n\n> [!IMPORTANT]\n>\n> Documentation is included in this repository under docs/. Use the docs helper script from the\n> repository root to install, generate, build, serve, screenshot, and assess the site.\n\nAgent Application Development\n\nPrompts and configurations that define your Agent Applications are stored in simple files, with minimal boilerplate, enabling simple management and version control.\n\nChat with individual Agents and Components before, during and after workflow execution to tune and diagnose your application. Agents can request human input to get additional context for task completion.\n\nSimple model selection makes testing Model MCP Server interaction painless. You can read more about the motivation behind this project here\n\n \n\nGet started:\n\nStart by installing the uv package manager for Python. Then:\n\n \n\nWith no configured model, an interactive run opens the model picker.\nAutomation and server runs must pass --model, set FAST_AGENT_MODEL, configure\ndefault_model, or declare a model on the agent.\n\nFor packaged starter agents, use fast-agent go --pack --model .\nThis installs the pack into the selected fast-agent home if needed, then\nstarts go normally. --model is a fallback for cards without an explicit\nmodel setting; a model declared directly in an AgentCard still wins.\n\nOther quickstart examples include a Researcher Agent (with Evaluator-Optimizer workflow) and Data Analysis Agent (similar to the ChatGPT experience), demonstrating MCP Roots support.\n\n> [!TIP]\n> Windows Users - there are a couple of configuration changes needed for the Filesystem and Docker MCP Servers - necessary changes are detailed within the configuration files.\n\nBasic Agents\n\nDefining an agent is as simple as:\n\n \n\nWe can then send messages to the Agent:\n\n \n\nOr start an interactive chat with the Agent:\n\n \n\nHere is the complete sizer.py Agent application, with boilerplate code:\n\n \n\nThe Agent can then be run with uv run sizer.py.\n\nSpecify a model with the --model switch - for example uv run sizer.py --model sonnet.\n\nModel strings also accept query overrides. For example:\n\n- uv run sizer.py --model "gpt-5?reasoning=low"\n- uv run sizer.py --model "claude-sonnet-4-6?web_search=on"\n- uv run sizer.py --model "claude-sonnet-4-5?context=1m"\n- uv run sizer.py --model "gpt-5?poll_period=240"\n\nFor Anthropic models, ?context=1m is only needed for earlier Sonnet 4 / Sonnet 4.5\nmodels that still require the explicit 1M context opt-in. Claude Sonnet 4.6 and\nClaude Opus 4.6 already use their long context window by default, so ?context=1m\nis accepted for backward compatibility but is unnecessary there.\n\nCombining Agents and using MCP Servers\n\n_To generate examples use fast-agent quickstart workflow. This example can be run with uv run workflow/chaining.py. Place fast-agent.yaml in the active fast-agent home, or pass an explicit config path when needed._\n\nAgents can be chained to build a workflow, using MCP Servers defined in the fast-agent.yaml file:\n\n \n\nAll Agents and Workflows respond to .send("message") or .prompt() to begin a chat session.\n\nSaved as social.py we can now run this workflow from the command line with:\n\n \n\nAdd the --quiet switch to disable progress and message display and return only the final response - useful for simple automations.\n\nMAKER\n\nMAKER (“Massively decomposed Agentic processes with K-voting Error Reduction”) wraps a worker agent and samples it repeatedly until a response achieves a k-vote margin over all alternatives (“first-to-ahead-by-k” voting). This is useful for long chains of simple steps where rare errors would otherwise compound.\n\n- Reference: Solving a Million-Step LLM Task with Zero Errors\n- Credit: Lucid Programmer (PR author)\n\n \n\nAgents As Tools\n\nThe Agents As Tools workflow takes a complex task, breaks it into subtasks, and calls other agents as tools based on the main agent instruction.\n\nThis pattern is inspired by the OpenAI Agents SDK Agents as tools feature.\n\nWith child agents exposed as tools, you can implement routing, parallelization, and orchestrator-workers decomposition directly in the instruction (and combine them). Multiple tool calls per turn are supported and executed in parallel.\n\nCommon usage patterns may combine:\n\n- Routing: choose the right specialist tool(s) based on the user prompt.\n- Parallelization: fan out over independent items/projects, then aggregate.\n- Orchestrator-workers: break a task into scoped subtasks (often via a simple JSON plan), then coordinate execution.\n\n \n\nExtended example and all params sample is available in the repository as\nexamples/workflows/agents_as_tools_extended.py.\n\nMCP OAuth (v2.1)\n\nFor SSE and HTTP MCP servers, OAuth is enabled by default with minimal configuration. A local callback server is used to capture the authorization code, with a paste-URL fallback if the port is unavailable.\n\n- Minimal per-server settings in fast-agent.yaml:\n\n \n\n- The OAuth client uses PKCE and in-memory token storage (no tokens written to disk).\n- Token persistence: by default, tokens are stored securely in your OS keychain via keyring. If a keychain is unavailable (e.g., headless container), in-memory storage is used for the session.\n- To force in-memory only per server, set:\n\n \n\n- To disable OAuth for a specific server , set auth.oauth: false for that server.\n\nMCP Ping (optional)\n\nThe MCP ping utility can be enabled by either peer (client or server). See the Ping overview.\n\nClient-side pinging is configured per server (default: 30s interval, 3 missed pings):\n\n \n\n**Global tools (@fast.tool)** — available to all agents that don\'t declare their own tools:\n\n \n\nAgen', 'readme_raw_len': 10194, 'one_line_zh': '编写、构建与评估 Agent，出色的 Model 与 Skills/MCP/ACP/A2A 支持', 'one_line_plain': 'Create, run and test code agents with support for Model, Skills, MCP, ACP and A2A protocols'}, {'id': 5498, 'key': 'davepoon-buildwithclaude', 'kind': 'repo', 'name': 'davepoon/buildwithclaude', 'one_line': 'A single hub to find Claude Skills, Agents, Commands, Hooks, Plugins, and Marketplace collections to extend Claude Code, Claude Desktop, Agent SDK and OpenClaw', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'A single hub to find Claude Skills, Agents, Commands, Hooks, Plugins, and Marketplace collections to extend Claude Code, Claude Desktop, Agent SDK and OpenClaw', 'url': 'https://github.com/davepoon/buildwithclaude', 'source': 'github', 'source_url': 'https://github.com/davepoon/buildwithclaude', 'downloads': None, 'stars': 3654, 'installs': None, 'mentions': 1, 'score': 110.24, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-12', 'times_seen': 1, 'icon': 'https://github.com/davepoon.png?size=64', 'official_desc': 'A single hub to find Claude Skills, Agents, Commands, Hooks, Plugins, and Marketplace collections to extend Claude Code, Claude Desktop, Agent SDK and OpenClaw', 'stars_own': 3654, 'stars_creator': None, 'stars_own_repo': 'davepoon/buildwithclaude', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Build with Claude\n\n**Claude Skills, Agents, Commands, Hooks, Plugins, Marketplaces collections for and extend Claude Code**\n\n \n \n \n \n\n**A plugin marketplace and discovery platform for Claude Code. Browse curated plugins, discover community contributions, and extend your Claude Code workflows.**\n\nQuick Start\n\n \n\nWhat's Included\n\nBuild with Claude Plugins\n\nCurated collections maintained in this repository:\n\n| Type | Count | Description |\n|------|-------|-------------|\n| **Agents** | 117 | Specialized AI experts (Python, Go, DevOps, Security, etc.) |\n| **Commands** | 175 | Slash commands for automation (/commit, /docs, /tdd) |\n| **Hooks** | 28 | Event-driven automation (notifications, git, formatting) |\n| **Skills** | 26 | Reusable capabilities from plugins |\n| **Plugins** | 51 | Bundled plugin packages by category |\n\nCommunity Discovery\n\nThe platform indexes plugins from the broader Claude Code ecosystem:\n\n- **20k+ Community Plugins** from external marketplaces\n- **4,500+ MCP Servers** for database, API, and tool connections\n- **1,100+ Plugin Marketplaces** from the community\n\nWeb UI\n\nBrowse, search, and explore everything at **buildwithclaude.com**\n\n \n\n \n\n \n\n \n\n \n\nFeatures\n\n- Browse all plugin types with filtering\n- Search across plugins, agents, commands, hooks, skills\n- Copy install commands with one click\n- View full documentation and usage examples\n- Discover MCP servers and community plugins\n\nInstallation Options\n\nOption 1: Plugin Marketplace (Recommended)\n\n \n\nOption 2: Manual Installation\n\n \n\nAvailable Plugin Categories\n\nAgents (11 categories)\n\n- **Development & Architecture** - Backend, frontend, mobile, GraphQL experts\n- **Language Specialists** - Python, Go, Rust, TypeScript, C/C++ experts\n- **Quality & Security** - Code review, security audit, debugging\n- **Infrastructure & Operations** - DevOps, cloud, database optimization\n- **Data & AI** - ML engineering, data pipelines, AI development\n- **Crypto & Blockchain** - Trading systems, DeFi, Web3 development\n\nBrowse all agents →\n\n \n\nCommands (22 categories)\n\n- **Version Control** - Commit, PR creation, branch management\n- **Code Analysis** - Testing, review, optimization\n- **Documentation** - Docs generation, changelogs, API specs\n- **Project Management** - Todos, PRDs, task tracking\n\nBrowse all commands →\n\n \n\nHooks (8 categories)\n\n- **Notifications** - Slack, Discord, Telegram alerts\n- **Git** - Auto-staging, smart commits\n- **Development** - Lint on save, auto-format\n- **Security** - File protection, vulnerability scanning\n\nBrowse all hooks →\n\n \n\nUsage Examples\n\nUsing Agents\n\nAgents are automatically invoked based on context, or explicitly called:\n\n \n\nUsing Commands\n\nCommands use the / prefix:\n\n \n\nUsing Hooks\n\nHooks run automatically on events like tool calls or session start.\n\nContributing\n\nWe welcome contributions!\n\nAdding Plugins\n\n1. Create a new directory in plugins/ following the naming convention\n2. Add your plugin files (agents, commands, hooks)\n3. Run npm test to validate\n4. Submit a pull request\n\nPlugin Format\n\n**Agent** (plugins/agents-*/agents/*.md):\n \n\n**Command** (plugins/commands-*/commands/*.md):\n \n\n**Hook** (plugins/hooks-*/hooks/*.md):\n \n\nSee CONTRIBUTING.md for detailed guidelines.\n\nLinks\n\n- **Web UI**: buildwithclaude.com\n- **Documentation**: Claude Code Docs\n- **Plugin Marketplaces**: Plugin Docs\n- **Issues**: GitHub Issues\n- **Visual Index**: Vexilo · A field guide to Claude Code — Interactive index of 31 agents · 99 commands · 123 skills · 13 rules, organized around the 5-step workflow. (companion repo)\n\nLicense\n\nMIT License - see LICENSE for details.\n\n---\n\nMade with ❤️ by Dave Poon", 'readme_raw_len': 3628, 'one_line_zh': '一站式查找 Claude 技能、Agent、命令、钩子、插件与市场合集，用于扩展 Claude Code、Claude Desktop、Agent SDK 与 OpenClaw', 'one_line_plain': 'Browse and install Claude Skills, Agents, Commands, Hooks, Plugins and Marketplace collections'}, {'id': 8089, 'key': 'skillsmp-com-creators-aieradev-clypra-agents-skills-bugfix-regression', 'kind': 'skill', 'name': 'bugfix-regression', 'one_line': 'Guides AI agents through diagnosing, reproducing, fixing, and permanently testing defects in Clypra', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/aieradev/clypra/agents-skills-bugfix-regression', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/aieradev/clypra/agents-skills-bugfix-regression', 'downloads': None, 'stars': 3322, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/AIEraDev.png?size=64', 'official_desc': 'Guides AI agents through diagnosing, reproducing, fixing, and permanently testing defects in Clypra. Activate when investigating crashes, rendering freezes, audio/video drift, playhead glitches, timeline corruption, or unexpected behavior. Enforces evidence-based diagnosis and mandatory regression t', 'stars_own': None, 'stars_creator': 3329, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'aieradev/clypra', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '引导 AI Agent 诊断、复现、修复并永久测试 Clypra 中的缺陷', 'one_line_plain': 'Guide AI agents to diagnose, reproduce, fix, and permanently test defects in Clypra'}, {'id': 4350, 'key': 'punkpeye-fastmcp', 'kind': 'mcp', 'name': 'FastMCP', 'one_line': '📇 - High-level framework for building MCP servers in TypeScript', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A TypeScript framework for building MCP servers.', 'url': 'https://github.com/punkpeye/fastmcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/punkpeye/fastmcp', 'downloads': None, 'stars': 3275, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/punkpeye.png?size=64', 'official_desc': '📇 - A high-level framework for building MCP servers in TypeScript', 'stars_own': 3275, 'stars_creator': None, 'stars_own_repo': 'punkpeye/fastmcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'FastMCP\n\nA TypeScript framework for building MCP servers capable of handling client sessions.\n\n> [!IMPORTANT]\n>\n> FastMCP implements the legacy, handshake-based MCP revisions (2025-11-25 and earlier). It does not support the current specification, 2026-07-28, which made the protocol stateless — no initialize handshake and no Mcp-Session-Id. For a framework targeting the current spec, use ViteMCP.\n\nFeatures\n\n- Simple Tool, Resource, Prompt definition\n- OpenAPI to MCP conversion\n- Authentication\n- Passing headers through context\n- Session ID and Request ID tracking\n- Sessions\n- Image content\n- Audio content\n- Embedded\n- Logging\n- Error handling\n- HTTP Streaming (with SSE compatibility)\n- HTTPS Support for secure connections\n- Custom HTTP routes for REST APIs, webhooks, and admin interfaces\n- Edge Runtime Support for Cloudflare Workers, Deno Deploy, and more\n- Stateless mode for serverless deployments\n- CORS (enabled by default)\n- Progress notifications\n- Streaming output\n- Typed server events\n- Prompt argument auto-completion\n- Sampling\n- Elicitation\n- Configurable ping behavior\n- Health-check endpoint\n- Roots\n- In-memory transport for unit testing without binding a port\n- CLI for testing and debugging\n\nWhen to use FastMCP over the official SDK?\n\nFastMCP is built on top of the official SDK.\n\nThe official SDK provides foundational blocks for building MCPs, but leaves many implementation details to you:\n\n- Initiating and configuring all the server components\n- Handling of connections\n- Handling of tools\n- Handling of responses\n- Handling of resources\n- Adding prompts, resources, resource templates\n- Embedding resources, image and audio content blocks\n\nFastMCP eliminates this complexity by providing an opinionated framework that:\n\n- Handles all the boilerplate automatically\n- Provides simple, intuitive APIs for common tasks\n- Includes built-in best practices and error handling\n- Lets you focus on your MCP\'s core functionality\n\n**When to choose FastMCP:** You want to build MCP servers quickly without dealing with low-level implementation details.\n\n**When to use the official SDK:** You need maximum control or have specific architectural requirements. In this case, we encourage referencing FastMCP\'s implementation to avoid common pitfalls.\n\nInstallation\n\n \n\nQuickstart\n\n> [!NOTE]\n>\n> There are many real-world examples of using FastMCP in the wild. See the Showcase for examples.\n\n \n\n_That\'s it!_ You have a working MCP server.\n\nYou can test the server in terminal with:\n\n \n\nIf you are looking for a boilerplate repository to build your own MCP server, check out fastmcp-boilerplate.\n\nRemote Server Options\n\nFastMCP supports multiple transport options for remote communication, allowing an MCP hosted on a remote machine to be accessed over the network.\n\nHTTP Streaming\n\nHTTP streaming provides a more efficient alternative to SSE in environments that support it, with potentially better performance for larger payloads.\n\nYou can run the server with HTTP streaming support:\n\n \n\nThis will start the server and listen for HTTP streaming connections on http://localhost:8080/mcp.\n\n> **Note:** You can also customize the endpoint path using the httpStream.endpoint option (default is /mcp).\n\n> **Note:** To serve HTTP streaming and built-in OAuth routes under an issuer path, set httpStream.basePath (for example, /issuer1). This exposes authorization server metadata at /.well-known/oauth-authorization-server/issuer1 per RFC 8414.\n\n> **Note:** This also starts an SSE server on http://localhost:8080/sse.\n\nYou can connect to these servers using the appropriate client transport.\n\nFor HTTP streaming connections:\n\n \n\nFor SSE connections:\n\n \n\nHTTPS Support\n\nFastMCP supports HTTPS for secure connections by providing SSL certificate options:\n\n \n\nThis will start the server with HTTPS on https://localhost:8443/mcp.\n\n**SSL Options:**\n\n- sslCert - Path to SSL certificate file\n- sslKey - Path to SSL private key file\n- sslCa - (Optional) Path to CA certificate for mutual TLS authentication\n\n**For testing**, you can generate self-signed certificates:\n\n \n\n**For production**, obtain certificates from a trusted CA like Let\'s Encrypt.\n\nSee the https-server example for a complete demonstration.\n\nCORS Configuration\n\nBy default, FastMCP enables CORS with a standard set of allowed headers. You can customize the CORS behavior by passing a cors option:\n\n \n\nThe cors option accepts:\n\n- true (default) - enable CORS with default settings\n- false - disable CORS entirely\n- An object with these fields:\n - origin - a string, array of strings, or a function (origin: string) => boolean\n - allowedHeaders - a string or array of strings\n - methods - array of allowed HTTP methods\n - exposedHeaders - array of headers to expose\n - credentials - boolean to allow credentials\n - maxAge - preflight cache duration in seconds\n\nThe CorsOptions type is exported from fastmcp for convenience.\n\nCustom HTTP Routes\n\nFastMCP allows you to add custom HTTP routes alongside MCP endpoints, enabling you to build comprehensive HTTP services that include REST APIs, webhooks, admin interfaces, and more - all within the same server process.\n\n \n\nCustom routes use the underlying Hono app returned by server.getApp() and support:\n\n- Hono\'s HTTP methods: get, post, put, delete, patch, options, and more\n- Path parameters (:param) and wildcards (*)\n- Query string parsing\n- JSON, text, form, and other body helpers from c.req\n- Custom status codes and headers\n- Middleware and route groups through Hono\n\nRoutes are matched in the order they are registered, allowing you to define specific routes before catch-all patterns.\n\nPublic and Protected Routes\n\nCustom Hono routes are public unless you add your own route middleware or authentication checks. For protected custom routes, put your auth logic in a reusable helper and call it from both FastMCP\'s authenticate option and your Hono route handlers:\n\n \n\nPublic routes are perfect for:\n\n- OAuth discovery endpoints (.well-known/*)\n- Health checks and status pages\n- Static assets and documentation\n- Webhook endpoints from external services\n- Public APIs that don\'t require user authentication\n\nSee the custom-routes example for a complete demonstration.\n\nEdge Runtime Support\n\nFastMCP supports edge runtimes like Cloudflare Workers, enabling deployment of MCP servers to the edge with minimal latency worldwide.\n\nChoosing Between FastMCP and EdgeFastMCP\n\n| Use Case | Class | Import |\n| ------------------------------- | ------------- | -------------------------------------------- |\n| Node.js, Express, Bun | FastMCP | import { FastMCP } from "fastmcp" |\n| Cloudflare Workers, Deno Deploy | EdgeFastMCP | import { EdgeFastMCP } from "fastmcp/edge" |\n\n| Feature | FastMCP | EdgeFastMCP |\n| -------------------- | ------------------------------ | -------------------------------------- |\n| Runtime | Node.js | Edge (V8 isolates) |\n| Start method | server.start({ port }) | export default server |\n| Transport | stdio, httpStream, SSE | HTTP Streamable only |\n| Sessions | Stateful or stateless | Stateless only |\n| File system | Yes | No |\n| OAuth/Authentication | Built-in authenticate option | Use Hono middleware (built-in planned) |\n| Custom routes | server.getApp() | server.getApp() |\n\n> **Note:** Built-in authentication for EdgeFastMCP is planned for a future release. Both FastMCP and EdgeFastMCP use Hono internally, so there\'s no technical barrier—EdgeFastMCP was simply written before OAuth was added to FastMCP. PRs are welcome to add an authenticate option that accepts web Request instead of Node.js http.IncomingMessage.\n>\n> In the meantime, use Hono middleware:\n>\n> \n\nCloudflare Workers\n\nTo deploy FastMCP to Cloudflare Workers, use the EdgeFastMCP class from the /edge subpath:\n\n \n\nEdge Runtime Differences\n\nWhen running on edge runtimes:\n\n- **Stateless by default**: Each request is handled independently\n- **No filesystem access**: Use fetch APIs for external data\n- **V8 Isolates**: Fast cold starts and efficient resource usage\n- ', 'readme_raw_len': 40996, 'one_line_zh': '用于在 TypeScript 中构建 MCP 服务器的高层框架', 'one_line_plain': 'Build MCP servers using a TypeScript framework'}, {'id': 1488, 'key': 'jpisnice-shadcn-ui-mcp-server', 'kind': 'mcp', 'name': 'Jpisnice/shadcn-ui-mcp-server', 'one_line': '📇 🏠 - Access shadcn/ui v4 components, blocks, demos and metadata', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation,compaitable with react,svelte 5,vue & React Native', 'url': 'https://github.com/Jpisnice/shadcn-ui-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Jpisnice/shadcn-ui-mcp-server', 'downloads': None, 'stars': 3062, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Jpisnice.png?size=64', 'official_desc': '📇 🏠 - Access shadcn/ui v4 components, blocks, demos and metadata.', 'stars_own': 3062, 'stars_creator': None, 'stars_own_repo': 'Jpisnice/shadcn-ui-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Shadcn UI v4 MCP Server\n\n \n \n \n\n \n\n> **🚀 The fastest way to integrate shadcn/ui components into your AI workflow**\n\nA Model Context Protocol (MCP) server that provides AI assistants with comprehensive access to shadcn/ui v4 components, blocks, demos, and metadata. Seamlessly retrieve React, Svelte, Vue, and React Native implementations for your AI-powered development workflow.\n\n✨ Key Features\n\n- **🎯 Multi-Framework Support** - React, Svelte, Vue, and React Native implementations\n- **📦 Component Source Code** - Latest shadcn/ui v4 TypeScript source\n- **🎨 Component Demos** - Example implementations and usage patterns \n- **🏗️ Blocks Support** - Complete block implementations (dashboards, calendars, forms)\n- **📋 Metadata Access** - Dependencies, descriptions, and configuration details\n- **🔍 Directory Browsing** - Explore repository structures\n- **⚡ Smart Caching** - Efficient GitHub API integration with rate limit handling, plus protocol cache hints (ttlMs/cacheScope) on list and read results\n- **⌨️ Argument Completion** - Autocomplete for prompt and resource-template arguments (component names, package managers, build tools, and more)\n- **🌐 Streamable HTTP Transport** - Stateless /mcp endpoint for multi-client deployments (MCP spec 2026-07-28)\n- **🐳 Docker Ready** - Production-ready containerization with Docker Compose\n\n🚀 Quick Start\n\n \n\n**🎯 Get your GitHub token in 2 minutes**: docs/getting-started/github-token.md\n\n📦 One-Click Installation (Claude Desktop)\n\nDownload and double-click the .mcpb file for instant installation:\n\n1. Download shadcn-ui-mcp-server.mcpb from Releases\n2. Double-click the file - Claude Desktop opens automatically\n3. Enter your GitHub token (optional, for higher rate limits)\n4. Click Install - tools are available immediately\n\n**Manual install:** Claude Desktop → Settings → MCP → Add Server → Browse → Select .mcpb file\n\n> **References:** Anthropic Desktop Extensions | Building MCPB\n\n🌐 HTTP Transport & Docker Deployment\n\nThe server implements the **2026-07-28 MCP specification** (via MCP TypeScript SDK v2) and stays compatible with 2025-era clients. To make your client use the modern protocol, or to require it, see the Modern Protocol Guide. Besides stdio, it can serve **Streamable HTTP** at a single stateless /mcp endpoint for multi-client and production deployments:\n\nQuick Start with HTTP\n \n\nTransport Modes\n- **stdio** (default) - Standard input/output for CLI usage\n- **http** - Streamable HTTP at /mcp\n- **dual** - Both stdio and HTTP simultaneously\n\n> The legacy SSE transport (/sse + /message) was removed. --mode sse is still accepted as a deprecated alias for http.\n\nDocker Examples\n \n\nEnvironment Variables\n- MCP_TRANSPORT_MODE - Transport mode (stdio|http|dual)\n- MCP_PORT - HTTP server port (default: 7423 - SHADCN on keypad!)\n- MCP_HOST - Host binding (default: 0.0.0.0)\n- MCP_CORS_ORIGINS - CORS origins (comma-separated)\n- MCP_PROTOCOL - Protocol eras to accept: any (default; 2026-07-28 plus 2025-era fallback) or modern (2026-07-28 only). CLI: --protocol\n- GITHUB_PERSONAL_ACCESS_TOKEN - GitHub API token\n- UI_LIBRARY - UI primitive library: radix (default) or base (React only)\n\n📚 Documentation\n\n| Section | Description |\n|---------|-------------|\n| 🚀 Getting Started | Installation, setup, and first steps |\n| ⚙️ Configuration | Framework selection, tokens, and options |\n| 🔌 Integration | Editor and tool integrations |\n| 📖 Usage | Examples, tutorials, and use cases |\n| 🎨 Frameworks | Framework-specific documentation |\n| 🐛 Troubleshooting | Common issues and solutions |\n| 🔧 API Reference | Tool reference and technical details |\n\n🎨 Framework Support\n\nThis MCP server supports four popular shadcn implementations:\n\n| Framework | Repository | Maintainer | Description |\n|-----------|------------|------------|-------------|\n| **React** (default) | shadcn/ui | shadcn | React components from shadcn/ui v4 |\n| **Svelte** | shadcn-svelte | huntabyte | Svelte components from shadcn-svelte |\n| **Vue** | shadcn-vue | unovue | Vue components from shadcn-vue |\n| **React Native** | react-native-reusables | Founded Labs | React Native components from react-native-reusables |\n\nUI Library (React only)\n\nshadcn/ui v4 supports two primitive libraries for React: **Radix UI** (default) and **Base UI**.\n\n \n\nClaude Desktop config example:\n \n\n🛠️ Essential Setup\n\n1. Get GitHub Token (Recommended)\n \n\n2. Run Server\n \n\n3. Integrate with Your Editor\n- **Autohand Code**: See Autohand Code Integration below\n- **Claude Code**: See Claude Code Integration below\n- **VS Code**: docs/integration/vscode.md\n- **Cursor**: docs/integration/cursor.md\n- **Claude Desktop**: docs/integration/claude-desktop.md\n- **Continue.dev**: docs/integration/continue.md\n\n💻 Autohand Code Integration\n\nUse the Autohand Code CLI to add the shadcn/ui MCP server:\n\n \n\nAdd --scope project before shadcn to save the server in the current project's .autohand configuration instead of your user configuration.\n\n💻 Claude Code Integration\n\nQuick Add (CLI)\n\n \n\nHTTP Transport\n\nFor production deployments with the Streamable HTTP transport:\n\n \n\nFramework-Specific Commands\n\nSee Claude Code Integration Guide for framework-specific commands (React, Svelte, Vue, React Native).\n\n> **Reference:** Claude Code MCP Documentation\n\n🎯 Use Cases\n\n- **AI-Powered Development** - Let AI assistants build UIs with shadcn/ui\n- **Multi-Client Deployments** - Stateless HTTP transport scales horizontally without sticky sessions\n- **Production Environments** - Docker Compose ready with health checks and monitoring\n- **Component Discovery** - Explore available components and their usage\n- **Multi-Framework Learning** - Compare React, Svelte, Vue, and React Native implementations\n- **Rapid Prototyping** - Get complete block implementations for dashboards, forms, etc.\n- **Code Generation** - Generate component code with proper dependencies\n\n📦 Installation\n\n \n\n🔨 Building from Source\n\nPrerequisites\n\n- Node.js >= 20.0.0\n- npm or pnpm\n\nBuild Steps\n\n \n\nRun Locally\n\n \n\nBuilding MCPB Package\n\nThe project includes a manifest.json following the MCPB specification. The .mcpb file is a ZIP archive containing the server, dependencies, and configuration.\n\nSee CONTRIBUTING.md for detailed packaging instructions.\n\n> **Reference:** Building Desktop Extensions with MCPB\n\n🔗 Quick Links\n\n- 📖 Full Documentation\n- 🚀 Getting Started Guide\n- 🌐 HTTP Transport & Docker Guide\n- 🧭 Modern Protocol Guide (2026-07-28)\n- 📝 Changelog\n- 🎨 Framework Comparison\n- 🔧 API Reference\n- 🐛 Troubleshooting\n- 💬 Issues & Discussions\n\n📄 License\n\nMIT License - see LICENSE for details.\n\n🙏 Acknowledgments\n\n- **shadcn** - For the amazing React UI component library\n- **huntabyte** - For the excellent Svelte implementation\n- **unovue** - For the comprehensive Vue implementation\n- **Founded Labs** - For the React Native implementation\n- **Anthropic** - For the Model Context Protocol specification\n\n---\n\n**Made with ❤️ by Janardhan Polle**\n\n**Star ⭐ this repo if you find it helpful!**", 'readme_raw_len': 6973, 'one_line_zh': '📇 🏠 - 访问 shadcn/ui v4 的组件、区块、演示与元数据', 'one_line_plain': 'shadcn/ui lets users access v4 components, blocks, demos, and metadata for UI building'}, {'id': 4499, 'key': 'vibheksoni-stealth-browser-mcp', 'kind': 'repo', 'name': 'vibheksoni/stealth-browser-mcp', 'one_line': 'Stealth browser automation for AI agents over MCP and the Chrome DevTools Protocol: navigation, network hooks, DOM extraction, and pixel-accurate UI cloning.', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Stealth browser automation for AI agents over MCP and the Chrome DevTools Protocol: navigation, network hooks, DOM extraction, and pixel-accurate UI cloning.', 'url': 'https://github.com/vibheksoni/stealth-browser-mcp', 'source': 'github', 'source_url': 'https://github.com/vibheksoni/stealth-browser-mcp', 'downloads': None, 'stars': 3054, 'installs': None, 'mentions': 1, 'score': 107.44, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-10', 'times_seen': 1, 'icon': 'https://github.com/vibheksoni.png?size=64', 'official_desc': 'Stealth browser automation for AI agents over MCP and the Chrome DevTools Protocol: navigation, network hooks, DOM extraction, and pixel-accurate UI cloning.', 'stars_own': 3054, 'stars_creator': None, 'stars_own_repo': 'vibheksoni/stealth-browser-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'pip install fastmcp', 'install_src': 'HEAD/README.md', 'install_conf': 'low', 'desc_src': 'github-api', 'readme_body': "Stealth Browser MCP\n\n**Stealth browser automation for MCP-compatible AI agents.**\n\nNavigate Cloudflare challenges, anti-bot checks, and login walls with real Chrome-family browser instances powered by nodriver, Chrome DevTools Protocol, and FastMCP.\n\n \n \n \n \n \n \n\n \n\n---\n\nSponsors\n\n \n\nNodeMaven — The most efficient proxy provider for Web Scraping and Automation with the Highest Quality IP on the market.\n\n**Why NodeMaven?**\n\n- ZIP targeting\n- 99.9% uptime\n- IP filtering: all proxies have fraud score Sponsored placement. NodeMaven is a paid sponsor of this project.\n\n---\n\nTable of Contents\n\n- Sponsors\n- Demo\n- Features\n- Quickstart\n- Agent Skill\n- Trust Model\n- Modular Architecture\n- Server Configuration\n- Toolbox\n- Stealth vs Playwright MCP\n- Troubleshooting\n- Examples\n- Showcase\n- Roadmap\n- Contributing\n- Support\n- License\n\n---\n\nDemo\n\n \n \n \n \n \n \n \n\n*Stealth Browser MCP passing a Cloudflare challenge, cloning UI elements, and inspecting network traffic through AI chat commands.*\n\n---\n\nFeatures\n\n- **Anti-bot resistance** - Has passed Cloudflare and Queue-It style challenges in testing; results vary by site, region, browser version, and detector version.\n- **101 tools across 11 sections** - From basic navigation to advanced CDP function execution.\n- **Modular loading** - Run the full 101-tool surface or a minimal 21-tool core; disable sections you do not need.\n- **Pixel-accurate element cloning** - Extract complete elements with CSS, DOM structure, events, animations, and assets via CDP.\n- **Network inspection** - Inspect requests, responses, headers, payloads, and captured bodies through your AI agent.\n- **Dynamic hook system** - Restricted Python hooks can intercept, block, redirect, fulfill, or modify request/response flows.\n- **CDP execution** - Run JavaScript, direct CDP commands, and pre-document scripts through trusted local MCP clients.\n- **Instant text input** - Paste large content via CDP, type with human-like keystrokes, or press real keys with trusted CDP key events.\n- **Cross-platform** - Windows, macOS, Linux, Docker, and CI environments with automatic browser/platform detection.\n- **Browser support** - Chrome, Chromium, and Microsoft Edge with automatic executable discovery.\n- **Clean MCP integration** - Works through standard MCP clients without custom brokers.\n\n---\n\nQuickstart\n\n1. Clone and install\n\nRequires Python 3.10+ and Chrome, Chromium, or Microsoft Edge.\n\n \n\n2. Add to your MCP client\n\n**Claude Code CLI (recommended):**\n\nWindows:\n\n \n\nmacOS / Linux:\n\n \n\nReplace the example path with your real clone path.\n\n \n Manual JSON configuration (Claude Desktop, Cursor, etc.) \n\nWindows (%APPDATA%\\Claude\\claude_desktop_config.json):\n\n \n\nmacOS / Linux (~/Library/Application Support/Claude/claude_desktop_config.json):\n\n \n\n \n\n \n Experimental FastMCP CLI install \n\nThese commands are provided for users who prefer FastMCP's installer. The stdio JSON setup above is the recommended path.\n\n \n\n \n\n3. Test it\n\nRestart your MCP client and ask your agent:\n\n> Use stealth-browser to navigate to https://example.com and take a screenshot.\n\n---\n\nAgent Skill\n\nThis repo includes an agent skill at skills/stealth-browser-mcp for clients that support Codex-style skills. It teaches agents the recommended tool order, state checks, pre-document CDP script usage, network debugging flow, and browser cleanup rules.\n\nUse the skill when an LLM struggles to choose the right MCP tool or forgets to verify browser state after actions. If your client does not load repo-local skills automatically, add or symlink skills/stealth-browser-mcp into your client skills directory.\n\n---\n\nTrust Model\n\nThis server exposes powerful local browser-control primitives: CDP execution, page JavaScript execution, cookie/storage access, network interception, dynamic hooks, header mutation, navigation, and file uploads from allowlisted local paths.\n\nTreat the MCP client or agent as the security principal. The recommended deployment is local stdio transport with a trusted desktop MCP client. If you use HTTP transport, set STEALTH_BROWSER_MCP_AUTH_TOKEN and do not expose unauthenticated HTTP outside trusted local or private networks.\n\n---\n\nModular Architecture\n\nChoose exactly what functionality you need. Run the full 101-tool suite or strip it down to the 21-tool core.\n\n| Mode | Tools | Use Case |\n|------|-------|----------|\n| **Full** (default) | 101 | Complete browser automation and debugging |\n| **Minimal** (--minimal) | 21 | Core browser automation and interaction |\n| **Custom** (--disable-*) | Your choice | Disable specific sections |\n| **Xpool safe** (--xpool-safe) | 84 | Disable CDP function tools that trigger Runtime.enable |\n\n \n\nUse --debug or set STEALTH_BROWSER_DEBUG=1 to enable verbose server diagnostics on stderr. Normal MCP stdio runs stay quiet by default to avoid corrupting JSON-RPC transport output.\n\n**Available sections:**\n\n| Section | Tools | Description |\n|---------|-------|-------------|\n| browser-management | 8 | Core browser operations |\n| element-interaction | 13 | Page interaction and manipulation |\n| element-extraction | 9 | Element cloning and extraction |\n| file-extraction | 9 | File-based extraction tools |\n| network-debugging | 10 | Network monitoring and capture |\n| cdp-functions | 17 | Chrome DevTools Protocol execution |\n| progressive-cloning | 10 | Advanced element cloning |\n| cookies-storage | 3 | Cookie management |\n| tabs | 5 | Tab management |\n| debugging | 7 | Debug and system tools |\n| dynamic-hooks | 10 | AI-assisted network hooks |\n\n---\n\nServer Configuration\n\nThese are environment variables for the MCP server process. Set them in your shell, service manager, Docker environment, or MCP client env block.\n\n| Variable | Default | Meaning |\n|----------|---------|---------|\n| STEALTH_BROWSER_MCP_AUTH_TOKEN | unset | Optional bearer token for HTTP transport. When set, HTTP clients must send Authorization: Bearer . |\n| MCP_AUTH_TOKEN | unset | Backward-compatible alias for STEALTH_BROWSER_MCP_AUTH_TOKEN. |\n| BROWSER_IDLE_TIMEOUT | 600 | Global idle timeout in seconds before an unused browser instance is auto-closed. Set 0 to disable idle reaping globally. |\n| BROWSER_IDLE_REAPER_INTERVAL | 60 | Background reaper check interval in seconds. |\n| BROWSER_TAB_RECYCLE_NAVIGATIONS | 0 | Replace the main tab with a fresh one after this many navigations. 0 keeps the same tab, which preserves history and session storage. |\n| BROWSER_ORPHAN_PROFILE_MAX_AGE | 21600 | Startup cleanup threshold in seconds for stale uc_* temp profiles. Set 0 to disable this startup sweep. |\n| STEALTH_BROWSER_EXECUTABLE | auto-detect | Path to the Chrome, Chromium, or Edge executable to launch instead of the auto-detected one. |\n| BROWSER_FILE_UPLOAD_ALLOWED_DIRS | repo root | Directories that file_upload() may read from. Separate multiple roots with ; on Windows or : on macOS/Linux. |\n| STEALTH_BROWSER_DEBUG | 0 | Enable verbose debug logging to stderr when set to 1. |\n| DEBUG | 0 | Legacy alias for STEALTH_BROWSER_DEBUG. |\n| XPOOL_SAFE_MODE | 0 | Disable the cdp-functions section at startup. |\n| PORT | 8000 | Default HTTP port when --port is not provided. |\n\n**PowerShell example:**\n\n \n\n**MCP client env block:**\n\n \n\n**HTTP client example:**\n\n \n\n**Per-instance idle override:**\n\n- spawn_browser(idle_timeout_seconds=1800) keeps that instance for 30 minutes of inactivity.\n- spawn_browser(idle_timeout_seconds=0) disables idle reaping for that one instance.\n\n---\n\nToolbox\n\n \n Browser Management (8) \n\n| Tool | Description |\n|------|-------------|\n| spawn_browser() | Create a stealth browser instance |\n| list_instances() | List active browser sessions |\n| close_instance() | Clean shutdown of a browser |\n| get_instance_state() | Full browser state information |\n| navigate() | Navigate to URLs |\n| go_back() | Navigate back in history |\n| go_forward() | Navigate forward in history |\n| reload_page() | Reload current page |\n\n \n\n \n Element Interaction (13) \n\n| Tool | Description |\n|------|-------------|\n| query_elem", 'readme_raw_len': 18080, 'one_line_zh': '通过 MCP 与 Chrome DevTools 协议为 AI Agent 提供隐身浏览器自动化：导航、网络钩子、DOM 提取与像素级 UI 克隆', 'one_line_plain': 'Stealth browser automation over MCP and Chrome DevTools Protocol that lets agents navigate, hook networks, extract DOM, and clone UI pixels'}, {'id': 8056, 'key': 'skillsmp-com-creators-aldegad-sprite-gen-skill', 'kind': 'skill', 'name': 'sprite-gen', 'one_line': 'Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults,…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/aldegad/sprite-gen/skill', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/aldegad/sprite-gen/skill', 'downloads': None, 'stars': 2836, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/aldegad.png?size=64', 'official_desc': 'Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults, automatic cleanup and optional curation. Handles sprite requests, ordinary image generation/editing, typography logos (로고, 타이포그래피, 타이틀 글자, 타이틀 로고, wordmark, logo, title lettering: a title scr', 'stars_own': None, 'stars_creator': 2918, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'aldegad/sprite-gen', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '通过 GPT 或 Grok 生成图像与游戏精灵，带引导式供应商选择与分开保存的默认值…', 'one_line_plain': 'Generate images and game sprites using GPT or Grok, with guided provider choices and saved defaults'}, {'id': 235, 'key': 'metatool-ai-metatool-app', 'kind': 'mcp', 'name': 'metatool-ai/metatool-app', 'one_line': '📇 ☁️ 🏠 🍎 🪟 🐧 - MetaMCP: unified middleware MCP server that manages your MCP connections through a GUI', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/metatool-ai/metamcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/metatool-ai/metatool-app', 'downloads': None, 'stars': 2713, 'installs': None, 'mentions': 22, 'score': 2203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/metatool-ai.png?size=64', 'official_desc': 'WNFjHvupy3q8TFn3Hos2IAk4Ju5dCo8B3wP7VPr/FGaKiG+T+v+TQqIrOqMTL1VdWV1DdmcbO8KXBz6esmYWYKPwDL5b5FA1a0hwapHiom0r/cKaoqr+27/XcrS5UwSMbQAAAABJRU5ErkJggg==" alt="DeepWiki: MetaMCP"> Latest Update:** This ai-dev branch will be the forward onging dev branch which contains ai agent changes.', 'stars_own': 2713, 'stars_creator': None, 'stars_own_repo': 'metatool-ai/metamcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': '🚀 MetaMCP (MCP Aggregator, Orchestrator, Middleware, Gateway in one docker) \n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n\n> \n\n**📢 Latest Update:** This ai-dev branch will be the forward onging dev branch which contains ai agent changes. Please test before you build the image based on this branch. There has been many PRs thanks to the community but merging and reviewing them has been a growing effort too. I decided to include ai changes. At least so far the core functionality works. There is also a community maintained fork (ty a lot!): https://github.com/Umbrella-IT-Group/metamcp\n\n**📢 Update:** *From the author: apologize for some recent maintainence delay, but will at least keep merging PRs, more background [here]*\n\n**MetaMCP** is a MCP proxy that lets you dynamically aggregate MCP servers into a unified MCP server, and apply middlewares. MetaMCP itself is a MCP server so it can be easily plugged into **ANY** MCP clients.\n\n \n\n---\n\nFor more details, consider visiting our documentation site: https://docs.metamcp.com\n\nEnglish | 简体中文\n📋 Table of Contents \n\n- 🎯 Use Cases\n- 📖 Concepts\n - 🖥️ **MCP Server**\n - 🔐 **Environment Variables \\& Secrets (STDIO MCP Servers)**\n - 🏷️ **MetaMCP Namespace**\n - 🌐 **MetaMCP Endpoint**\n - ⚙️ **Middleware**\n - 🔍 **Inspector**\n - ✏️ **Tool Overrides \\& Annotations**\n- 🚀 Quick Start\n - 🐳 Run with Docker Compose (Recommended)\n - 📦 Build development environment with Dev Containers (VSCode/Cursor)\n - 💻 Local Development\n- 🔌 MCP Protocol Compatibility\n- 🔗 Connect to MetaMCP\n - 📝 E.g., Cursor via mcp.json\n - 🖥️ Connecting Claude Desktop and Other STDIO-only Clients\n - 🔧 API Key Auth Troubleshooting\n- ❄️ Cold Start Problem and Custom Dockerfile\n- 🧾 Log Levels\n- 🔐 Authentication\n- 🚦 Traffic Management\n - 🚧 **MCP Rate Limit**\n- 🔗 OpenID Connect (OIDC) Provider Support\n - 🛠️ **Configuration**\n - 🏢 **Supported Providers**\n - 🔒 **Security Features**\n - 📱 **Usage**\n- ⚙️ Registration Controls\n - 🎛️ **Available Controls**\n - 🏢 **Enterprise Use Cases**\n - 🛠️ **Configuration**\n- 🌐 Custom Deployment and SSE conf for Nginx\n- 🏗️ Architecture\n - 📊 Sequence Diagram\n- 🗺️ Roadmap\n- 🌐 i18n\n- 🤝 Contributing\n- 📄 License\n- 🙏 Credits\n\n🎯 Use Cases\n- 🏷️ **Group MCP servers into namespaces, host them as meta-MCPs, and assign public endpoints** (SSE or Streamable HTTP), with auth. One-click to switch a namespace for an endpoint.\n- 🎯 **Pick tools you only need when remixing MCP servers.** Apply other **pluggable middleware** around observability, security, etc. (coming soon)\n- 🔍 **Use as enhanced MCP inspector** with saved server configs, and inspect your MetaMCP endpoints in house to see if it works or not.\n- 🔍 **Use as Elasticsearch for MCP tool selection** (coming soon)\n\nGenerally developers can use MetaMCP as **infrastructure** to host dynamically composed MCP servers through a unified endpoint, and build agents on top of it.\n\nQuick demo video: https://youtu.be/Cf6jVd2saAs\n\n \n\n📖 Concepts\n\n🖥️ **MCP Server**\nA MCP server configuration that tells MetaMCP how to start a MCP server.\n\n \n\n🔐 **Environment Variables & Secrets (STDIO MCP Servers)**\n\nFor **STDIO MCP servers**, MetaMCP supports three ways to handle environment variables and secrets:\n\n**1. Raw Values** - Direct string values (not recommended for secrets):\n \n\n**2. Environment Variable References** - Use ${ENV_VAR_NAME} syntax:\n \n\n**3. Auto-matching** - If the expected environment variable name in your tool matches the container\'s environment variable, you can omit it entirely. MetaMCP will automatically pass through matching environment variables.\n\n> **🔒 Security Note**: Environment variable references (${VAR_NAME}) are resolved from the MetaMCP container\'s environment at runtime. This keeps actual secret values out of your configuration and git repository.\n\n> **⚙️ Development Note**: For local development with pnpm run dev:docker, ensure your environment variables are listed in turbo.json under globalEnv to be passed to the development processes. This is not required for production Docker deployments.\n\n🏷️ **MetaMCP Namespace**\n- Group one or more MCP servers into a namespace\n- Enable/disable MCP servers or at tool level\n- Apply middlewares to MCP requests and responses\n- Override tool names/titles/descriptions per namespace and attach custom MCP annotations (e.g. { "annotations": { "readOnlyHint": false } })\n\n🌐 **MetaMCP Endpoint**\n- Create endpoints and assign namespace to endpoints\n- Multiple MCP servers in the namespace will be aggregated and emitted as a MetaMCP endpoint\n- Choose between API-Key Auth (in header or query param) or standard OAuth in MCP Spec 2025-06-18\n- Host through **SSE** or **Streamable HTTP** transports in MCP and **OpenAPI** endpoints for clients like Open WebUI\n\n⚙️ **Middleware**\n- Intercepts and transforms MCP requests and responses at namespace level\n- **Built-in example**: "Filter inactive tools" - optimizes tool context for LLMs\n- **Future ideas**: tool logging, error traces, validation, scanning\n\n🔍 **Inspector**\nSimilar to the official MCP inspector, but with **saved server configs** - MetaMCP automatically creates configurations so you can debug MetaMCP endpoints immediately.\n\n✏️ **Tool Overrides & Annotations**\n- Open a namespace → **Tools** tab to see every tool coming from connected MCP servers.\n- Each saved tool can be expanded and edited inline: update the display **name/title/description** or provide a JSON blob with namespace-specific annotations (for example { "annotations": { "readOnlyHint": false } }).\n- Badges in the table ("Overridden", "Annotations") show which tools currently have custom metadata. Hover them to read a tooltip describing what was overridden.\n- Annotation overrides are merged with whatever the upstream MCP server returns, so you can safely add custom UI hints without losing provider metadata.\n\n🚀 Quick Start\n\n**🐳 Run with Docker Compose (Recommended)**\n\nClone repo, prepare .env, and start with docker compose:\n\n \n\nIf you modify APP_URL env vars, make sure you only access from the APP_URL, because MetaMCP enforces CORS policy on the URL, so no other URL is accessible.\n\nNote that the pg volume name may collide with your other pg dockers, which is global, consider rename it in docker-compose.yml:\n\n \n\n**📦 Build development environment with Dev Containers (VSCode/Cursor)**\n\nYou can use the VSCode/Cursor extension to build the development environment in a container.\n\nIt only requires that you have an environment running Docker or a similar alternative (the docker/docker compose command is required), and no other dependent components need to be installed on your host machine.\n\n1. First, clone the MetaMCP source code, open project in Visual Studio Code.\n \n2. Switch to Dev Containers. Open the VSCode Command Palette, and execute Dev Containers: Reopen in Container.\n\nVSCode will open the Dev Containers project in a new window, where it will build the runtime and install the toolchain according to the Dockerfile before starting the connection and finally installing the MetaMCP dependencies.\n \n\n> **note**\n> This process requires a reliable network connection, and it will access Docker Hub, GitHub, and some other sites. You will need to ensure the network connection yourself, otherwise the container build may fail.\n\nWait some minutes, depending on the internet connection or computer performance, it may take from a few minutes to tens of minutes, you can click on the Progress Bar in the bottom right corner to view a live log where you will be able to check unusual stuck.\n \n\nAfter finished, you can run pnpm dev to start the development server.\n\n**💻 Local Development**\n\nStill recommend running postgres through docker for easy setup:\n\n \n\n🔌 MCP Protocol Compatibility\n\n- ✅ **Tools, Resources, and Prompts** supported\n- ✅ **OAuth-enabled MCP servers** tested for 03-26 version\n\nIf you have questions, feel free to leave **GitHub issues** or **PRs**.\n\n🔗 Connect to MetaMCP\n\n📝 E.g., Cursor via mcp.json\n\nExample mcp.json\n\n \n\n🖥️ Connecting Claude Desktop and Other STDIO-only', 'readme_raw_len': 17689, 'one_line_zh': 'MetaMCP：统一的中间件 MCP 服务器，通过图形界面管理 MCP 连接', 'one_line_plain': 'Manage MCP connections through a unified middleware GUI'}, {'id': 8338, 'key': 'feder-cr-git-ai', 'kind': 'repo', 'name': 'feder-cr/git-ai', 'one_line': 'Git designed for AI agents', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '["开源项目"]', 'desc_raw': '', 'url': 'https://github.com/feder-cr/git-ai', 'source': 'github', 'source_url': 'https://github.com/feder-cr/git-ai', 'downloads': None, 'stars': 2438, 'installs': None, 'mentions': 1, 'score': 107.87, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-19', 'last_seen': '2026-10-19', 'times_seen': 1, 'icon': 'https://github.com/feder-cr.png?size=64', 'official_desc': 'Git designed for AI agents.', 'stars_own': 2438, 'stars_creator': None, 'stars_own_repo': 'feder-cr/git-ai', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': None, 'one_line_plain': 'Git designed for AI agents to manage code changes and version history'}, {'id': 3676, 'key': 'mariocandela-beelzebub', 'kind': 'mcp', 'name': 'mariocandela/beelzebub', 'one_line': '☁️ - Honeypot framework for building decoy MCP tools that an agent would never use in normal work, to detect…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/beelzebub-labs/beelzebub', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/mariocandela/beelzebub', 'downloads': None, 'stars': 2201, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/mariocandela.png?size=64', 'official_desc': '☁️ - Honeypot framework for building decoy MCP tools that an agent would never use in normal work, to detect prompt injection and malicious agent behavior.', 'stars_own': 2201, 'stars_creator': None, 'stars_own_repo': 'beelzebub-labs/beelzebub', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "Beelzebub\n\n**Open-source deception framework.**\n\nDeploy realistic decoys. Observe attacker behavior. Turn interactions into security evidence.\n\n**Explore Beelzebub Platform** · Documentation · Quick start\n\n \n \n \n \n \n\nBeelzebub gives security teams a configurable way to study activity directed at decoy services. Define the environment an attacker encounters, choose how it responds, and capture the interaction for investigation. Run the framework independently or connect it to Beelzebub Platform.\n\nWhy Beelzebub\n\n- **Deploy decoys that fit your environment.** Define services, routes, and response rules in YAML. Use static handlers for predictable behavior or LLM-powered responses for adaptive interactions. Extend the runtime with trusted Go plugins when you need custom behavior.\n- **Observe what happens after contact.** Capture evidence from decoy interactions, including commands, HTTP request details, and session context, depending on the protocol. Give analysts a record of activity directed at the service to support investigation.\n- **Bring evidence into your workflow.** Inspect logs locally, publish structured events to RabbitMQ, or enable Beelzebub Platform reporting. Monitor runtime activity through Prometheus metrics and choose where captured data is sent.\n\nSee it in action\n\nWatch an LLM-powered decoy respond to attacker input. The demo illustrates how generated responses can sustain an interaction beyond a fixed set of command handlers.\n\n \n\nHow it works\n\n \n\nDefine services and rules in YAML, validate the configuration, and start the runtime. Trusted Go plugins are compiled into the runtime to extend services and responses. Each service handles incoming interactions and emits evidence through the configured event output. LLM responses require a configured provider; static handlers can run without one.\n\nThe framework supports **SSH, HTTP, TCP, TELNET, and MCP**. MCP decoys expose bait tools that make suspicious invocations observable during controlled agent testing. They can provide evidence of prompt-injection attempts; they do not guarantee detection of every attempt.\n\nDeployment options include a local Go binary, Docker Compose, and Kubernetes with Helm. See the architecture guide for runtime behavior and extension boundaries.\n\nExplore the decoys\n\nStart with one of the **18 example configurations** in configurations/services. Open a YAML file to inspect its rules and adapt it to your environment.\n\n| Category | Example decoys |\n| --- | --- |\n| Remote access | SSH · SSH (LLM) · TELNET · RDP · VNC |\n| Web | WordPress (LLM fallback) · HTTP 401 · HTTP methods · Apache directory maze |\n| Databases & caches | MySQL · PostgreSQL (LLM) · SQL Server · Redis · Memcached |\n| Network services | SMB · LDAP |\n| IoT | MQTT |\n| AI agents | MCP bait tools |\n\nExamples range from banners and selected responses to interactive exchanges. **LLM** examples require a configured provider; WordPress uses it for its catch-all route. Check your deployment's port mappings when enabling additional examples.\n\nGet started\n\nYou need **Git, a shell, and Docker Engine with Compose v2**. Use an **isolated lab host** with authorization for all configured listeners: the installer starts a bundle of example services, including privileged ports. On Linux it can enable **host networking**; Docker does not provide network isolation in that mode.\n\nUse synthetic credentials and review the production safety guide before exposing services to the internet.\n\n \n\nPlatform reporting is optional. Leave the Platform token blank when prompted to run independently. The runtime validates its configuration before starting listeners.\n\nTest the default HTTP decoy and inspect its logs:\n\n \n\nExpect **401 Unauthorized** from the bundled HTTP example on port 8080. This is the decoy's configured response, confirming that the listener is reachable. If the installer skipped an occupied port, resolve the conflict before testing. Press Ctrl+C to stop following logs.\n\nStop the lab when finished:\n\n \n\nFor a single-service setup or another deployment method, follow the installation guide.\n\nBeelzebub Platform\n\nThe open-source framework is the foundation: deploy, operate, and extend it independently. **Beelzebub Platform** is the managed product for teams that need to coordinate deception across environments and connect runtime evidence to a broader security workflow.\n\nExplore the platform's deployment, investigation, and reporting capabilities, and see how they fit your team's requirements.\n\n**Explore Beelzebub Platform →**\n\nResources and community\n\n- **Documentation:** configuration, protocols, operations, integrations, and recipes.\n- **Plugin authoring:** build extensions with the public Go SDK. Plugins execute in-process; review their source and pin trusted versions.\n- **Contributing:** contribute code, examples, or documentation under the Code of Conduct. Report vulnerabilities privately through SECURITY.md.\n\nFor development, use the Go version declared in go.mod, Git, and Make. Build with make build; run make test.unit, go vet ./..., and make validate-all before submitting runtime changes. The development workflow covers integration tests and their Docker dependencies.\n\nThank you to JetBrains for supporting development with tools through its open-source support program.\n\nLicensed under the GNU General Public License v3.0.", 'readme_raw_len': 5358, 'one_line_zh': '用于构建诱饵 MCP 工具的蜜罐框架，正常工作中 Agent 不会用到这些工具，一旦调用即可发现。', 'one_line_plain': 'Build decoy MCP tools to detect unauthorized agent activity'}, {'id': 1465, 'key': 'joshuayoes-ios-simulator-mcp', 'kind': 'mcp', 'name': 'ios-simulator-mcp', 'one_line': '📇 🏠 🍎 - Interact with iOS simulators: get simulator information, control UI interactions and inspect UI…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for interacting with the iOS simulator', 'url': 'https://github.com/joshuayoes/ios-simulator-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/joshuayoes/ios-simulator-mcp', 'downloads': None, 'stars': 2194, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/joshuayoes.png?size=64', 'official_desc': '📇 🏠 🍎 - Interact with iOS simulators: get simulator information, control UI interactions and inspect UI elements.', 'stars_own': 2194, 'stars_creator': None, 'stars_own_repo': 'joshuayoes/ios-simulator-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'iOS Simulator MCP Server\n\n \n\nA Model Context Protocol (MCP) server for interacting with iOS simulators. This server allows you to interact with iOS simulators by getting information about them, controlling UI interactions, and inspecting UI elements.\n\n> **Security Notice**: Command injection vulnerabilities present in versions ;\n}\n json\n{\n "bundle_id": "com.example.app",\n "terminate_running": true,\n "env": {\n "FOO": "bar",\n "BAZ": "qux"\n }\n}\n typescript\n{\n /**\n * Udid of target, can also be set with the IDB_UDID env var\n * Format: UUID (8-4-4-4-12 hexadecimal characters)\n */\n udid?: string;\n /** Bundle identifier of the app to terminate (e.g., com.apple.mobilesafari) */\n bundle_id: string;\n}\n typescript\n{\n /**\n * Udid of target, can also be set with the IDB_UDID env var\n * Format: UUID (8-4-4-4-12 hexadecimal characters)\n */\n udid?: string;\n /** The URL or deep link to open (e.g., https://example.com or myapp://screen/detail) */\n url: string;\n}\n typescript\n{\n /**\n * Udid of target, can also be set with the IDB_UDID env var\n * Format: UUID (8-4-4-4-12 hexadecimal characters)\n */\n udid?: string;\n}\n \n Verify all accessibility elements on the current screen\n \n Enter "QA Test" into the text input field and confirm the input is correct\n \n Tap on coordinates x=250, y=400 and verify the expected element is triggered\n \n Swipe from x=150, y=600 to x=150, y=100 and confirm correct behavior\n \n Describe the UI element at position x=300, y=350 to ensure proper labeling and functionality\n \n View the current simulator screen\n \n Take a screenshot of the current simulator screen and save it to my_screenshot.png\n \n Start recording a video of the simulator screen (saves to the default output directory, which is ~/Downloads unless overridden by IOS_SIMULATOR_MCP_DEFAULT_OUTPUT_DIR)\n \n Stop the current simulator screen recording\n \n Install the app at path/to/MyApp.app on the simulator\n \n Launch the Safari app (com.apple.mobilesafari) on the simulator\n bash\nnpx uri-scheme open "myapp://products/42" --ios\n bash\nxcrun simctl openurl booted "myapp://products/42"\n \nOpen the deep link myapp://products/42 in the simulator, then verify the product\ndetails screen is shown\n bash\n # Open with your default editor (or use \'code\', \'vim\', etc.)\n open ~/.cursor/mcp.json\n # Or use Cursor\'s command if available\n # cursor ~/.cursor/mcp.json\n json\n {\n "mcpServers": {\n // ... other servers might be listed here ...\n "ios-simulator": {\n "command": "npx",\n "args": ["-y", "ios-simulator-mcp"]\n }\n }\n }\n json\n {\n "mcpServers": {\n "ios-simulator": {\n "command": "pnpm",\n "args": ["dlx", "ios-simulator-mcp"]\n }\n }\n }\n bash\n git clone https://github.com/joshuayoes/ios-simulator-mcp\n cd ios-simulator-mcp\n bash\n npm install\n # or, using pnpm (installs from the committed pnpm-lock.yaml):\n pnpm install\n bash\n npm run build\n # or:\n pnpm run build\n json\n {\n "mcpServers": {\n // ... other servers might be listed here ...\n "ios-simulator": {\n "command": "node",\n "args": ["/full/path/to/your/ios-simulator-mcp/build/index.js"]\n }\n }\n }\n bash\n claude mcp add ios-simulator npx ios-simulator-mcp\n # or, with pnpm:\n claude mcp add ios-simulator -- pnpm dlx ios-simulator-mcp\n bash\n claude mcp add ios-simulator -- node "/full/path/to/your/ios-simulator-mcp/build/index.js"\n json\n{\n "mcpServers": {\n "ios-simulator": {\n "command": "npx",\n "args": ["-y", "ios-simulator-mcp"],\n "env": {\n "IOS_SIMULATOR_MCP_FILTERED_TOOLS": "screenshot,record_video,stop_recording",\n "IOS_SIMULATOR_MCP_DEFAULT_OUTPUT_DIR": "~/Code/awesome-project/tmp",\n "IOS_SIMULATOR_MCP_IDB_PATH": "~/bin/idb"\n }\n }\n }\n}\n`\n\nMCP Registry Server Listings\n\n \n \n \n\n \n\nLicense\n\nMIT', 'readme_raw_len': 3633, 'one_line_zh': '📇 🏠 🍎 - 与 iOS 模拟器交互：获取模拟器信息、控制 UI 操作并检查 UI 界面', 'one_line_plain': 'Interact with iOS simulators to get information, control UI and inspect UI elements'}, {'id': 348, 'key': 'jau123-meigen-ai-design-mcp', 'kind': 'mcp', 'name': 'jau123/MeiGen-AI-Design-MCP', 'one_line': '', 'one_line_ok': 0, 'why_bad': 'ok', 'fields': '["MCP 服务"]', 'desc_raw': 'Discover inspiration and create images and videos with the latest AI models. Works with ChatGPT, Claude, Cursor, and other MCP clients.', 'url': 'https://github.com/jau123/MeiGen-AI-Design-MCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/jau123/MeiGen-AI-Design-MCP', 'downloads': None, 'stars': 1787, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/jau123.png?size=64', 'official_desc': '[](https://glama.ai/mcp/servers/@jau123/mei-gen-ai-design-mcp) 📇 ☁️ 🏠 - Image generation', 'stars_own': 1787, 'stars_creator': None, 'stars_own_repo': 'jau123/MeiGen-AI-Design-MCP', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "MeiGen AI Design MCP \n\n \n Discover inspiration. Create images and videos with the latest AI models. \n Open-source MCP tools for ChatGPT, Claude, Cursor, OpenClaw and other AI assistants.\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n \n English · 中文 \n Quick start · Capabilities · Manual setup · Documentation \n \n\nQuick start\n\n \n \n\nChoose the AI tool you use:\n\n| Client | Get started |\n|---|---|\n| **ChatGPT** | Install from the official ChatGPT store → sign in to MeiGen → start a new chat and use MeiGen. No API key needed. |\n| **Claude** | Connect remote MCP and sign in to MeiGen. Optional creative helpers are available separately. |\n| **Cursor** | Add the remote MCP URL, then sign in. |\n| **OpenClaw** | Install from ClawHub, then sign in with OAuth. |\n| **Other MCP clients** | Connect remote MCP with OAuth. Setup guide → |\n\nTry a search first:\n\n> Use MeiGen to find minimalist product photography inspiration. Show me the reference images and prompts.\n\nWant more options? With remote MCP, ask for another batch of the same search, or describe a new direction.\n\nSearch and model/Skill lookups do not spend generation credits. MeiGen Cloud generation uses **purchased credits** from your account; daily free credits do not apply. Check current prices and confirm the scope before generating.\n\n \n \n\nWhat you can do\n\n| Capability | Try asking |\n|---|---|\n| Inspiration search | “Find cinematic portrait references and show their prompts.” |\n| Image & video generation | “Create one product photo, then animate it into a short video.” |\n| Reference media | “Use this clip or audio as a reference with a compatible video model.” |\n| Saved characters — remote OAuth | “List my MeiGen characters and use this one in the video.” |\n| Personal library — remote OAuth | “Find my saved references, or the prompt from a previous creation.” |\n| Background removal | “Remove this background and give me a transparent PNG.” |\n| Product Detail Images | “Make a hero shot, a close-up and a lifestyle image for this product.” |\n| Marketing Poster | “Design one poster for our weekend coffee tasting.” |\n| AI Backgrounds | “Place this product on a sunlit stone counter.” |\n| Upscale | “Enhance this original photo while preserving its appearance.” |\n\nProvide a source image when the task needs one. Your assistant checks available models, options and prices with list_models / list_skills, prepares inputs, and returns results. See the Skill API guide for input limits and tool-call examples.\n\nVideo references follow each model’s supported frame, content-image, video and audio inputs. Media preparation does not spend generation credits, requires a positive purchased balance, and creates temporary public URLs cleared after seven days. Saved characters can be read and reused through OAuth; creating characters is not supported. See 3.1 release notes.\n\nBring your MeiGen library into the conversation\n\nBrowse and save ideas in MeiGen, then continue with your assistant. With remote OAuth, it can search your favorites, browse boards, find past creations and read a selected original prompt. For questions about your preferred style, it can use relevant favorites as references.\n\nAsk to save a selected inspiration or your own result, optionally to a private board. Saving requires your request or prior permission, does not publish the work, and does not spend generation credits. Reading and saving use separate account permissions; existing connections may need additional consent.\n\n**Available through remote OAuth and the official ChatGPT plugin.** Local npm and API keys cannot access the private library. Refresh your client’s tool list and complete any requested additional consent. See the account-library reference for tool names and prompt pagination.\n\n \n\nManual MCP setup\n\nUse remote OAuth for a new MeiGen connection. Keep one connection per client to avoid duplicate tools. Local npm and the optional client plugins below support existing API-key integrations and OpenAI-compatible image providers.\n\n \n\nRemote MCP\n\nAdd this **Streamable HTTP** URL in your client's MCP settings, then choose **Connect / Authenticate** and sign in to MeiGen:\n\n \n\nOAuth requires support in your client. If unavailable, use a compatible host or the official ChatGPT plugin; a new API key is not a substitute for account sign-in. Reconnect and ask “List MeiGen's available Skills and current prices” to verify the connection without generating.\n\nFor optional Codex CLI / IDE MCP configuration, see the advanced setup guide.\n\nLocal npm — legacy compatibility and BYOK\n\nExisting local integrations and BYOK workflows can keep this stdio configuration. New MeiGen users should use remote OAuth above. Node.js 22+ is recommended:\n\n \n\nExisting local API keys remain compatible with generation, public search and Skills as MEIGEN_API_TOKEN in the server's private environment, but cannot access the private library. New connections use remote OAuth. OpenAI-compatible providers support general image generation; video and the five visual Skills use MeiGen Cloud. See client-specific setup, providers and CLI.\n\n**Upgrading from 2.x?** 3.0.0 is available on npm. It removes ComfyUI and the bundled offline prompt library. Read the migration notes and preserve your existing credentials and settings when updating.\n\n \n\n \n Claude plugin \n\nFor new connections, use remote OAuth. The optional plugin below retains its bundled legacy stdio connection; keep one active MeiGen connection per host when switching. To install only within that supported setup, run inside Claude, then restart:\n\n \n\nUse /meigen:setup for connection guidance or existing legacy/BYOK configuration. The plugin adds /meigen:gen, /meigen:find, /meigen:models and optional creative agents. Plugin guide →\n\n \n\n \n\n \n OpenClaw \n\nInstall MeiGen from ClawHub in current OpenClaw, then connect your account with OAuth:\n\n \n\nThe 4.1 plugin includes creative guides and remote tools, including OAuth-only favorites, boards and history. The standalone creative-toolkit Skill is instructions only and is not needed alongside the plugin. Setup and migration from local 3.x →\n\n \n\n \n\n \n Let your coding assistant set up MCP \n\nPaste this into an assistant that can configure your MCP client:\n\n \n\n \n\n \n\nDocumentation\n\n- Client setup, configuration, privacy and troubleshooting · Upgrading\n- Five Skills HTTP API and MCP examples\n- Compose image/video steps and recover interrupted jobs\n- Claude plugin commands and agents\n- 3.0 migration notes · Maintainer release guide\n\nIf a generation is interrupted, check the original task with check_generation or check_skill before starting another paid request. Keep your original assets and download results you need to retain.\n\nCommunity & license\n\nDiscord · Issues · MeiGen · Privacy policy\n\nFeatured in Awesome MCP Servers and wshobson/agents.\n\nMIT. The open-source server is free to use; cloud generation and third-party providers have their own usage costs.", 'readme_raw_len': 6913, 'one_line_zh': '发现灵感，并用最新 AI 模型创作图像与视频。支持 ChatGPT、Claude、Cursor 及其他 MCP 客户端。', 'one_line_plain': None}]/2026-10-20 https://anclum.com/en/[{'id': 1932, 'key': 'microsoft-markitdown-tree-main-packages-markitdown-mcp', 'kind': 'mcp', 'name': 'microsoft/markitdown', 'one_line': '🎖️ 🐍 🏠 - Convert many file formats, local or remote, to Markdown for LLMs using MarkItDown', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Python tool for converting files and office documents to Markdown.', 'url': 'https://github.com/microsoft/markitdown/tree/main/packages/markitdown-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/microsoft/markitdown/tree/main/packages/markitdown-mcp', 'downloads': None, 'stars': 191102, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/microsoft.png?size=64', 'official_desc': '🎖️ 🐍 🏠 - Convert many file formats, local or remote, to Markdown for LLMs using MarkItDown.', 'stars_own': 191102, 'stars_creator': None, 'stars_own_repo': 'microsoft/markitdown', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'MarkItDown\n\n \n \n\n> [!IMPORTANT]\n> MarkItDown performs I/O with the privileges of the current process. Like open() or requests.get(), it will access resources that the process itself can access. Sanitize your inputs in untrusted environments, and call the narrowest convert_* function needed for your use case (e.g., convert_stream(), or convert_local()). See the Security Considerations section of the documentation for more information.\n\nMarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to textract, but with a focus on preserving important document structure and content as Markdown (including: headings, lists, tables, links, etc.) While the output is often reasonably presentable and human-friendly, it is meant to be consumed by text analysis tools -- and may not be the best option for high-fidelity document conversions for human consumption.\n\nMarkItDown currently supports the conversion from:\n\n- PDF\n- PowerPoint\n- Word\n- Excel\n- Images (EXIF metadata and OCR)\n- Audio (EXIF metadata and speech transcription)\n- HTML\n- Text-based formats (CSV, JSON, XML)\n- ZIP files (iterates over contents)\n- YouTube URLs\n- EPubs\n- ... and more!\n\nWhy Markdown?\n\nMarkdown is extremely close to plain text, with minimal markup or formatting, but still\nprovides a way to represent important document structure. Mainstream LLMs, such as\nOpenAI\'s GPT-4o, natively "_speak_" Markdown, and often incorporate Markdown into their\nresponses unprompted. This suggests that they have been trained on vast amounts of\nMarkdown-formatted text, and understand it well. As a side benefit, Markdown conventions\nare also highly token-efficient.\n\nPrerequisites\nMarkItDown requires Python 3.10 through 3.14. It is recommended to use a virtual environment to avoid dependency conflicts.\n\nWith the standard Python installation, you can create and activate a virtual environment using the following commands:\n\n \n\nIf using uv, you can create a virtual environment with:\n\n \n\nIf you are using Anaconda, you can create a virtual environment with:\n\n \n\nInstallation\n\nTo install MarkItDown, use pip: pip install \'markitdown[all]\'. Alternatively, you can install it from the source:\n\n \n\nUsage\n\nCommand-Line\n\n \n\nOr use -o to specify the output file:\n\n \n\nYou can also pipe content:\n\n \n\nOptional Dependencies\nMarkItDown has optional dependencies for activating various file formats. Earlier in this document, we installed all optional dependencies with the [all] option. However, you can also install them individually for more control. For example:\n\n \n\nwill install only the dependencies for PDF, DOCX, and PPTX files.\n\nAt the moment, the following optional dependencies are available:\n\n* [all] Installs all optional dependencies\n* [pptx] Installs dependencies for PowerPoint files\n* [docx] Installs dependencies for Word files\n* [xlsx] Installs dependencies for Excel files\n* [xls] Installs dependencies for older Excel files\n* [pdf] Installs dependencies for PDF files\n* [outlook] Installs dependencies for Outlook messages\n* [az-doc-intel] Installs dependencies for Azure Document Intelligence\n* [az-content-understanding] Installs dependencies for Azure Content Understanding\n* [audio-transcription] Installs dependencies for audio transcription of wav and mp3 files\n* [youtube-transcription] Installs dependencies for fetching YouTube video transcription\n\nPlugins\n\nMarkItDown also supports 3rd-party plugins. Plugins are disabled by default. To list installed plugins:\n\n \n\nTo enable plugins use:\n\n \n\nTo find available plugins, search GitHub for the hashtag #markitdown-plugin. To develop a plugin, see packages/markitdown-sample-plugin.\n\nmarkitdown-ocr Plugin\n\nThe markitdown-ocr plugin adds OCR support to PDF, DOCX, PPTX, and XLSX converters, extracting text from embedded images using LLM Vision — the same llm_client / llm_model pattern that MarkItDown already uses for image descriptions. No new ML libraries or binary dependencies required.\n\n**Installation:**\n\n \n\n**Usage:**\n\nPass the same llm_client and llm_model you would use for image descriptions:\n\n \n\nIf no llm_client is provided the plugin still loads, but OCR is silently skipped and the standard built-in converter is used instead.\n\nSee packages/markitdown-ocr/README.md for detailed documentation.\n\nAzure Content Understanding\n\nAzure Content Understanding provides higher-quality conversion with structured field extraction (YAML front matter), multi-modal support (documents, images, audio, video), and configurable analyzers.\n\nInstall: pip install \'markitdown[az-content-understanding]\'\n\nWhen to use Content Understanding\n\nContent Understanding is ideal when you need capabilities beyond what built-in or Document Intelligence converters provide:\n\n- **Audio and video files** — CU is the only option for video, and the higher-quality cloud option for audio. Built-in converters have no video support and only basic audio transcription.\n- **Structured field extraction** — Prebuilt or custom-built analyzers extract domain-specific fields (invoice amounts, receipt dates, contract clauses) serialized as YAML front matter. Neither built-in nor Doc Intel integration exposes fields.\n- **Higher-quality document extraction** — Cloud-based layout analysis and OCR for scanned PDFs, complex tables, and multi-page documents.\n- **Single API for all modalities** — One cu_endpoint handles documents, images, audio, and video with automatic analyzer routing.\n\n| Capability | Built-in converters | Azure Document Intelligence | Azure Content Understanding |\n|------------|---------------------|-----------------------------|-----------------------------|\n| Document conversion | Offline, format-specific extraction | Cloud layout extraction | Cloud multimodal extraction |\n| Structured fields | Not available | Not exposed by this integration | YAML front matter from analyzer fields |\n| Custom analyzers | Not available | Not configurable in this integration | Supported with cu_analyzer_id |\n| Audio and video | Basic audio, no video | Not supported | Audio and video analyzers |\n| Cost | Local compute only | Billable Azure API calls | Billable Azure API calls |\n\n**CLI:**\n\n \n\nThe endpoint can also be set once in the environment, so callers only need --use-cu:\n\n \n\n**Python API:**\n\n \n\n**With a custom analyzer** (for domain-specific field extraction):\n\n \n\nWhen cu_analyzer_id is set, the converter automatically scopes it to compatible file types based on the analyzer\'s modality. Incompatible types (e.g., audio files with a document analyzer) auto-route to default prebuilt analyzers.\n\n**Cost note:** Each convert() call for a CU-routed format is a billable Azure API call. Use cu_file_types to restrict which formats route to CU:\n\n \n\nMore information about Azure Content Understanding can be found here.\n\nAzure Document Intelligence\n\nTo use Microsoft Document Intelligence for conversion:\n\n \n\nThe endpoint can also be set once in the environment, so callers only need -d:\n\n \n\nMore information about how to set up an Azure Document Intelligence Resource can be found here\n\nPython API\n\nBasic usage in Python:\n\n \n\nDocument Intelligence conversion in Python:\n\n \n\nTo use Large Language Models for image descriptions (currently only for pptx and image files), provide llm_client and llm_model:\n\n \n\nmax_retries controls the OpenAI client\'s automatic retries for retryable errors (the default is 2); 5 allows up to six attempts with backoff. See the OpenAI SDK retry documentation.\n\nIf any attempt succeeds, image conversion continues normally. If the client raises an error after exhausting its retries, or encounters a non-retryable error, MarkItDown tries other applicable converters and raises FileConversionException only if none succeeds.\n\nDocker\n\n \n\nContributing\n\nBefore starting significant work, please read What to Contribute, which describes what is in and out of scope for this repository.\n\nThis project welcomes contributions and suggestio', 'readme_raw_len': 12893, 'one_line_zh': '🎖️ 🐍 🏠 - 使用 MarkItDown 把本地或远程的多种文件格式转换为 Markdown，供 LLM 使用。', 'one_line_plain': 'MarkItDown converts many file formats, local or remote, to Markdown for LLMs'}, {'id': 7696, 'key': 'thedotmack-claude-mem', 'kind': 'repo', 'name': 'thedotmack/claude-mem', 'one_line': 'Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions,…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '["开源项目"]', 'desc_raw': '', 'url': 'https://github.com/thedotmack/claude-mem', 'source': 'github', 'source_url': 'https://github.com/thedotmack/claude-mem', 'downloads': None, 'stars': 101789, 'installs': None, 'mentions': 1, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-15', 'last_seen': '2026-10-19', 'times_seen': 5, 'icon': 'https://github.com/thedotmack.png?size=64', 'official_desc': 'Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More', 'stars_own': 101789, 'stars_creator': None, 'stars_own_repo': 'thedotmack/claude-mem', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx claude-mem', 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "   \n \n    \n \n \n\n \n 🇨🇳 中文 •\n 🇹🇼 繁體中文 •\n 🇯🇵 日本語 •\n 🇵🇹 Português •\n 🇧🇷 Português •\n 🇰🇷 한국어 •\n 🇪🇸 Español •\n 🇩🇪 Deutsch •\n 🇫🇷 Français •\n 🇮🇱 עברית •\n 🇸🇦 العربية •\n 🇷🇺 Русский •\n 🇵🇱 Polski •\n 🇨🇿 Čeština •\n 🇳🇱 Nederlands •\n 🇹🇷 Türkçe •\n 🇺🇦 Українська •\n 🇻🇳 Tiếng Việt •\n 🇵🇭 Tagalog •\n 🇮🇩 Indonesia •\n 🇹🇭 ไทย •\n 🇮🇳 हिन्दी •\n 🇧🇩 বাংলা •\n 🇵🇰 اردو •\n 🇷🇴 Română •\n 🇸🇪 Svenska •\n 🇮🇹 Italiano •\n 🇬🇷 Ελληνικά •\n 🇭🇺 Magyar •\n 🇫🇮 Suomi •\n 🇩🇰 Dansk •\n 🇳🇴 Norsk \n \n\n Persistent memory compression system built for Claude Code . \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n Quick Start •\n How It Works •\n Search Tools •\n Documentation •\n Configuration •\n Troubleshooting •\n License \n \n\n \n Claude-Mem seamlessly preserves context across sessions by automatically capturing tool usage observations, generating semantic summaries, and making them available to future sessions. This enables Claude to maintain continuity of knowledge about projects even after sessions end or reconnect.\n \n\n---\n\nQuick Start\n\nInstall claude-mem for Grok Bot:\n\n \n\nGrok Bot has no host hooks, so we watch the chat log files. Default is CMEM Pro, the hosted memory. Local observer is opt-in: --provider host. Installing this plugin does not install Cursor.\n\n**Awareness push pilot (LFG + Orifice):** needle observations (decision, bugfix, security_alert, sensitive) are appended as dated - YYYY-MM-DD [awareness] … lines into that bot's memory/log/YYYY-MM.md. Grok Bot already re-reads the log from disk. This does not write profile.md, user-memory, or project memory. Disable with CLAUDE_MEM_GROK_BOT_AWARENESS_ENABLED=false.\n\nInstall with a single command:\n\n \n\nThe installer sets everything up first, then asks you to sign in to claude-mem in your browser (email magic link — no card required). Signing in provisions a memory key for your account and unlocks the **claude-mem observer** with a **30 Day Free Trial**: memory runs off-plan, so you get up to 100% more usage from your plan. When the free trial ends, memory automatically falls back to your Anthropic plan unless you subscribe. After sign-in you pick your memory provider — the claude-mem observer, your own OpenRouter or Gemini key, or your Anthropic plan.\n\nPrefer to skip the sign-in? Pass an explicit --provider flag, set CLAUDE_MEM_ONLINE_OPTIN=false, or run in CI/non-interactive shells — the installer completes without any account interaction.\n\nOr install for OpenCode:\n\n \n\nThis works with OpenCode 1.3.4 or later, including OpenCode 2. Restart OpenCode after installing.\n\nOr install for **T3 Code** (Codex and Claude Code providers):\n\n \n\nThe installer discovers T3 Code's enabled providers, registers native Claude-Mem plugins in their configured homes, and supports T3-managed Codex. Restart T3 Code, trust the provider's hooks when prompted, and start a new thread. See the T3 Code integration guide for custom server settings, status, and removal.\n\nOr install for Antigravity CLI (setup guide):\n\n \n\nOr install for OMP (Oh My Pi):\n\n \n\nOr install the native Pi extension or DeepSeek Harness plugin:\n\n \n\nPi and DeepSeek Harness use the worker runtime. See native harness setup for capture, recall, and troubleshooting.\n\nOr install from the plugin marketplace inside Claude Code:\n\n \n\nRestart Claude Code. Context from previous sessions will automatically appear in new sessions.\n\n> **Note:** Claude-Mem is also published on npm, but npm install -g claude-mem installs the **SDK/library only** — it does not register the plugin hooks or set up the worker service. Always install via npx claude-mem install or the /plugin commands above.\n\n🦞 OpenClaw Gateway\n\nInstall claude-mem as a persistent memory plugin on OpenClaw gateways with a single command:\n\n \n\nThe installer handles dependencies, plugin setup, AI provider configuration, worker startup, and optional real-time observation feeds to Telegram, Discord, Slack, and more. See the OpenClaw Integration Guide for details.\n\n**Key Features:**\n\n- 🧠 **Persistent Memory** - Context survives across sessions\n- 📊 **Progressive Disclosure** - Layered memory retrieval with token cost visibility\n- 🔍 **Skill-Based Search** - Query your project history with mem-search skill\n- 🖥️ **Web Viewer UI** - Real-time memory stream at the worker URL printed on startup\n- 💻 **Claude Desktop Skill** - Search memory from Claude Desktop conversations\n- 🔒 **Privacy Control** - Use tags to exclude sensitive content from storage\n- ⚙️ **Context Configuration** - Fine-grained control over what context gets injected\n- 🤖 **Automatic Operation** - No manual intervention required\n- 🔗 **Citations** - Reference past observations with IDs through the worker API or view all in the web viewer\n\n---\n\nDocumentation\n\n📚 **View Full Documentation** - Browse on official website\n\nGetting Started\n\n- **Installation Guide** - Quick start & advanced installation\n- **Usage Guide** - How Claude-Mem works automatically\n- **Search Tools** - Query your project history with natural language\n- **Cloud Sync** - Back up your memories to cmem.ai — no daemon, the worker syncs on write\n\nBest Practices\n\n- **Context Engineering** - AI agent context optimization principles\n- **Progressive Disclosure** - Philosophy behind Claude-Mem's context priming strategy\n\nArchitecture\n\n- **Overview** - System components & data flow\n- **Architecture Evolution** - The journey from v3 to v5\n- **Hooks Architecture** - How Claude-Mem uses lifecycle hooks\n- **Hooks Reference** - Every hook entry explained\n- **Worker Service** - HTTP API & Bun management\n- **Database** - SQLite schema & FTS5 search\n- **Search Architecture** - Hybrid search with Chroma vector database\n\nConfiguration & Development\n\n- **Configuration** - Environment variables & settings\n- **Development** - Building, testing, contributing\n- **Troubleshooting** - Common issues & solutions\n\n---\n\nHow It Works\n\n**Core Components:**\n\n1. **Lifecycle Hooks** - SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Stop, SessionEnd. Each is one shell-free command, claude-mem hook claude-code , run by a small launcher that the installer puts on PATH\n2. **Smart Install** - Cached dependency checker (Setup hook on claude --init / --maintenance, plus npx claude-mem install / repair)\n3. **Worker Service** - Local HTTP API with web viewer UI and search endpoints, managed by Bun\n4. **SQLite Database** - Stores sessions, observations, summaries\n5. **mem-search Skill** - Natural language queries with progressive disclosure\n6. **Chroma Vector Database** - Hybrid semantic + keyword search for intelligent context retrieval\n\nSee Architecture Overview for details.\n\n---\n\nMCP Search Tools\n\nClaude-Mem provides intelligent memory search through **4 MCP tools** following a token-efficient **3-layer workflow pattern**:\n\n**The 3-Layer Workflow:**\n\n1. **search** - Get compact index with IDs (~50-100 tokens/result)\n2. **timeline** - Get chronological context around interesting results\n3. **get_observations** - Fetch full details ONLY for filtered IDs (~500-1,000 tokens/result)\n\n**How It Works:**\n- Claude uses MCP tools to search your memory\n- Start with search to get an index of results\n- Use timeline to see what was happening around specific observations\n- Use get_observations to fetch full details for relevant IDs\n- **~10x token savings** by filtering before fetching details\n\n**Available MCP Tools:**\n\n1. **search** - Search memory index with full-text queries, filters by type/date/project\n2. **timeline** - Get chronological context around a specific observation or query\n3. **get_observations** - Fetch full observation details by IDs (always batch multiple IDs)\n\n**Example Usage:**\n\n \n\nSee Search Tools Guide for detailed examples.\n\n---\n\nSystem Requirements\n\n- **Node.js**: 20.0.0 or higher\n- **Claude Code**: Latest version with plugin support\n- **Bun**: JavaScript runtime and process manager (auto-installed if missing)\n- **uv**: Python package manager for vector search (auto-installed if missing)\n- **SQLite 3**: ", 'readme_raw_len': 12188, 'one_line_zh': '为每个 Agent 提供跨会话持久上下文，捕获 Agent 在会话中的所有操作…', 'one_line_plain': 'Captures everything an agent does during sessions to provide persistent context across sessions'}, {'id': 4262, 'key': 'modelcontextprotocol-servers-tree-main-src-everything', 'kind': 'mcp', 'name': 'modelcontextprotocol/server-everything', 'one_line': '📇 🏠 - Exercises all features of the MCP protocol', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol Servers', 'url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/everything', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/everything', 'downloads': None, 'stars': 91381, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/modelcontextprotocol.png?size=64', 'official_desc': '📇 🏠 - Exercises all features of the MCP protocol.', 'stars_own': 91381, 'stars_creator': None, 'stars_own_repo': 'modelcontextprotocol/servers', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol servers\n\nThis repository is a collection of *reference implementations* for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources.\n\n> [!IMPORTANT]\n> If you are looking for a list of MCP servers, you can browse published servers on the MCP Registry. The repository served by this README is dedicated to housing just the small number of reference servers maintained by the MCP steering group.\n\n> [!WARNING]\n> The servers in this repository are intended as **reference implementations** to demonstrate MCP features and SDK usage. They are meant to serve as educational examples for developers building their own MCP servers, not as production-ready solutions. Developers should evaluate their own security requirements and implement appropriate safeguards based on their specific threat model and use case.\n\nThe servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.\nTypically, each MCP server is implemented with an MCP SDK:\n\n- C# MCP SDK\n- Go MCP SDK\n- Java MCP SDK\n- Kotlin MCP SDK\n- PHP MCP SDK\n- Python MCP SDK\n- Ruby MCP SDK\n- Rust MCP SDK\n- Swift MCP SDK\n- TypeScript MCP SDK\n\n🌟 Reference Servers\n\nThese servers aim to demonstrate MCP features and the official SDKs.\n\n- **Everything** - Reference / test server with prompts, resources, and tools.\n- **Fetch** - Web content fetching and conversion for efficient LLM usage.\n- **Filesystem** - Secure file operations with configurable access controls.\n- **Git** - Tools to read, search, and manipulate Git repositories.\n- **Memory** - Knowledge graph-based persistent memory system.\n- **Sequential Thinking** - Dynamic and reflective problem-solving through thought sequences.\n- **Time** - Time and timezone conversion capabilities.\n\nArchived\n\nThe following reference servers are now archived and can be found at servers-archived.\n\n- **AWS KB Retrieval** - Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.\n- **Brave Search** - Web and local search using Brave\'s Search API. Has been replaced by the official server (@brave/brave-search-mcp-server).\n- **EverArt** - AI image generation using various models.\n- **GitHub** - Repository management, file operations, and GitHub API integration.\n- **GitLab** - GitLab API, enabling project management.\n- **Google Drive** - File access and search capabilities for Google Drive.\n- **Google Maps** - Location services, directions, and place details.\n- **PostgreSQL** - Read-only database access with schema inspection.\n- **Puppeteer** - Browser automation and web scraping.\n- **Redis** - Interact with Redis key-value stores.\n- **Sentry** - Retrieving and analyzing issues from Sentry.io.\n- **Slack** - Channel management and messaging capabilities. Now maintained by Zencoder\n- **SQLite** - Database interaction and business intelligence capabilities.\n\n🚀 Getting Started\n\nUsing MCP Servers in this Repository\nTypeScript-based servers in this repository can be used directly with npx.\n\nFor example, this will start the Memory server:\n \n\nPython-based servers in this repository can be used directly with uvx or pip. uvx is recommended for ease of use and setup.\n\nFor example, this will start the Git server:\n \n\nFollow these instructions to install uv / uvx and these to install pip.\n\nUsing an MCP Client\nHowever, running a server on its own isn\'t very useful, and should instead be configured into an MCP client. For example, here\'s the Claude Desktop configuration to use the above server:\n\n \n\nOn Windows, wrap npx with cmd /c:\n\n \n\nAdditional examples of using the Claude Desktop as an MCP client might look like:\n\n \n\nOn Windows, apply the same wrapper to each npx-based entry above by changing "command" to "cmd" and prepending "/c", "npx" to the existing args. Leave uvx entries unchanged.\n\n🛠️ Creating Your Own Server\n\nInterested in creating your own MCP server? Visit the official documentation at modelcontextprotocol.io for comprehensive guides, best practices, and technical details on implementing MCP servers.\n\n📚 Learn More\n\nSee ADDITIONAL.md for a curated list of frameworks and resources that simplify building MCP servers and clients.\n\n🤝 Contributing\n\nSee CONTRIBUTING.md for information about contributing to this repository.\n\n📦 Releasing\n\nSee RELEASING.md for how packages are published (OIDC trusted publishing from CI — no registry tokens) and how to retry a failed publish.\n\n🔒 Security\n\nSee SECURITY.md for reporting security vulnerabilities.\n\n📜 License\n\nThis project is licensed under the Apache License, Version 2.0 for new contributions, with existing code under MIT - see the LICENSE file for details.\n\n💬 Community\n\n- GitHub Discussions\n\n⭐ Support\n\nIf you find MCP servers useful, please consider starring the repository and contributing new servers or improvements!\n\n---\n\nManaged by Anthropic, but built together with the community. The Model Context Protocol is open source and we encourage everyone to contribute their own servers and improvements!', 'readme_raw_len': 5101, 'one_line_zh': '用于演练 MCP 协议全部特性的官方示例服务。', 'one_line_plain': 'Exercise all features of the MCP protocol'}, {'id': 3452, 'key': 'modelcontextprotocol-servers-tree-main-src-fetch', 'kind': 'mcp', 'name': 'modelcontextprotocol/server-fetch', 'one_line': '🐍 🏠 ☁️ - Web content fetching and processing for AI consumption', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol Servers', 'url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/fetch', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/fetch', 'downloads': None, 'stars': 91381, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/modelcontextprotocol.png?size=64', 'official_desc': '🐍 🏠 ☁️ - Web content fetching and processing for AI consumption.', 'stars_own': 91381, 'stars_creator': None, 'stars_own_repo': 'modelcontextprotocol/servers', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol servers\n\nThis repository is a collection of *reference implementations* for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources.\n\n> [!IMPORTANT]\n> If you are looking for a list of MCP servers, you can browse published servers on the MCP Registry. The repository served by this README is dedicated to housing just the small number of reference servers maintained by the MCP steering group.\n\n> [!WARNING]\n> The servers in this repository are intended as **reference implementations** to demonstrate MCP features and SDK usage. They are meant to serve as educational examples for developers building their own MCP servers, not as production-ready solutions. Developers should evaluate their own security requirements and implement appropriate safeguards based on their specific threat model and use case.\n\nThe servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.\nTypically, each MCP server is implemented with an MCP SDK:\n\n- C# MCP SDK\n- Go MCP SDK\n- Java MCP SDK\n- Kotlin MCP SDK\n- PHP MCP SDK\n- Python MCP SDK\n- Ruby MCP SDK\n- Rust MCP SDK\n- Swift MCP SDK\n- TypeScript MCP SDK\n\n🌟 Reference Servers\n\nThese servers aim to demonstrate MCP features and the official SDKs.\n\n- **Everything** - Reference / test server with prompts, resources, and tools.\n- **Fetch** - Web content fetching and conversion for efficient LLM usage.\n- **Filesystem** - Secure file operations with configurable access controls.\n- **Git** - Tools to read, search, and manipulate Git repositories.\n- **Memory** - Knowledge graph-based persistent memory system.\n- **Sequential Thinking** - Dynamic and reflective problem-solving through thought sequences.\n- **Time** - Time and timezone conversion capabilities.\n\nArchived\n\nThe following reference servers are now archived and can be found at servers-archived.\n\n- **AWS KB Retrieval** - Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.\n- **Brave Search** - Web and local search using Brave\'s Search API. Has been replaced by the official server (@brave/brave-search-mcp-server).\n- **EverArt** - AI image generation using various models.\n- **GitHub** - Repository management, file operations, and GitHub API integration.\n- **GitLab** - GitLab API, enabling project management.\n- **Google Drive** - File access and search capabilities for Google Drive.\n- **Google Maps** - Location services, directions, and place details.\n- **PostgreSQL** - Read-only database access with schema inspection.\n- **Puppeteer** - Browser automation and web scraping.\n- **Redis** - Interact with Redis key-value stores.\n- **Sentry** - Retrieving and analyzing issues from Sentry.io.\n- **Slack** - Channel management and messaging capabilities. Now maintained by Zencoder\n- **SQLite** - Database interaction and business intelligence capabilities.\n\n🚀 Getting Started\n\nUsing MCP Servers in this Repository\nTypeScript-based servers in this repository can be used directly with npx.\n\nFor example, this will start the Memory server:\n \n\nPython-based servers in this repository can be used directly with uvx or pip. uvx is recommended for ease of use and setup.\n\nFor example, this will start the Git server:\n \n\nFollow these instructions to install uv / uvx and these to install pip.\n\nUsing an MCP Client\nHowever, running a server on its own isn\'t very useful, and should instead be configured into an MCP client. For example, here\'s the Claude Desktop configuration to use the above server:\n\n \n\nOn Windows, wrap npx with cmd /c:\n\n \n\nAdditional examples of using the Claude Desktop as an MCP client might look like:\n\n \n\nOn Windows, apply the same wrapper to each npx-based entry above by changing "command" to "cmd" and prepending "/c", "npx" to the existing args. Leave uvx entries unchanged.\n\n🛠️ Creating Your Own Server\n\nInterested in creating your own MCP server? Visit the official documentation at modelcontextprotocol.io for comprehensive guides, best practices, and technical details on implementing MCP servers.\n\n📚 Learn More\n\nSee ADDITIONAL.md for a curated list of frameworks and resources that simplify building MCP servers and clients.\n\n🤝 Contributing\n\nSee CONTRIBUTING.md for information about contributing to this repository.\n\n📦 Releasing\n\nSee RELEASING.md for how packages are published (OIDC trusted publishing from CI — no registry tokens) and how to retry a failed publish.\n\n🔒 Security\n\nSee SECURITY.md for reporting security vulnerabilities.\n\n📜 License\n\nThis project is licensed under the Apache License, Version 2.0 for new contributions, with existing code under MIT - see the LICENSE file for details.\n\n💬 Community\n\n- GitHub Discussions\n\n⭐ Support\n\nIf you find MCP servers useful, please consider starring the repository and contributing new servers or improvements!\n\n---\n\nManaged by Anthropic, but built together with the community. The Model Context Protocol is open source and we encourage everyone to contribute their own servers and improvements!', 'readme_raw_len': 5101, 'one_line_zh': '为 AI 消费而抓取并处理网页内容的官方 MCP 服务。', 'one_line_plain': 'Fetch and process web content for AI consumption'}, {'id': 1933, 'key': 'modelcontextprotocol-servers-tree-main-src-filesystem', 'kind': 'mcp', 'name': 'modelcontextprotocol/server-filesystem', 'one_line': '📇 🏠 - Direct local file system access', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol Servers', 'url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem', 'downloads': None, 'stars': 91381, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/modelcontextprotocol.png?size=64', 'official_desc': '📇 🏠 - Direct local file system access.', 'stars_own': 91381, 'stars_creator': None, 'stars_own_repo': 'modelcontextprotocol/servers', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol servers\n\nThis repository is a collection of *reference implementations* for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources.\n\n> [!IMPORTANT]\n> If you are looking for a list of MCP servers, you can browse published servers on the MCP Registry. The repository served by this README is dedicated to housing just the small number of reference servers maintained by the MCP steering group.\n\n> [!WARNING]\n> The servers in this repository are intended as **reference implementations** to demonstrate MCP features and SDK usage. They are meant to serve as educational examples for developers building their own MCP servers, not as production-ready solutions. Developers should evaluate their own security requirements and implement appropriate safeguards based on their specific threat model and use case.\n\nThe servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.\nTypically, each MCP server is implemented with an MCP SDK:\n\n- C# MCP SDK\n- Go MCP SDK\n- Java MCP SDK\n- Kotlin MCP SDK\n- PHP MCP SDK\n- Python MCP SDK\n- Ruby MCP SDK\n- Rust MCP SDK\n- Swift MCP SDK\n- TypeScript MCP SDK\n\n🌟 Reference Servers\n\nThese servers aim to demonstrate MCP features and the official SDKs.\n\n- **Everything** - Reference / test server with prompts, resources, and tools.\n- **Fetch** - Web content fetching and conversion for efficient LLM usage.\n- **Filesystem** - Secure file operations with configurable access controls.\n- **Git** - Tools to read, search, and manipulate Git repositories.\n- **Memory** - Knowledge graph-based persistent memory system.\n- **Sequential Thinking** - Dynamic and reflective problem-solving through thought sequences.\n- **Time** - Time and timezone conversion capabilities.\n\nArchived\n\nThe following reference servers are now archived and can be found at servers-archived.\n\n- **AWS KB Retrieval** - Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.\n- **Brave Search** - Web and local search using Brave\'s Search API. Has been replaced by the official server (@brave/brave-search-mcp-server).\n- **EverArt** - AI image generation using various models.\n- **GitHub** - Repository management, file operations, and GitHub API integration.\n- **GitLab** - GitLab API, enabling project management.\n- **Google Drive** - File access and search capabilities for Google Drive.\n- **Google Maps** - Location services, directions, and place details.\n- **PostgreSQL** - Read-only database access with schema inspection.\n- **Puppeteer** - Browser automation and web scraping.\n- **Redis** - Interact with Redis key-value stores.\n- **Sentry** - Retrieving and analyzing issues from Sentry.io.\n- **Slack** - Channel management and messaging capabilities. Now maintained by Zencoder\n- **SQLite** - Database interaction and business intelligence capabilities.\n\n🚀 Getting Started\n\nUsing MCP Servers in this Repository\nTypeScript-based servers in this repository can be used directly with npx.\n\nFor example, this will start the Memory server:\n \n\nPython-based servers in this repository can be used directly with uvx or pip. uvx is recommended for ease of use and setup.\n\nFor example, this will start the Git server:\n \n\nFollow these instructions to install uv / uvx and these to install pip.\n\nUsing an MCP Client\nHowever, running a server on its own isn\'t very useful, and should instead be configured into an MCP client. For example, here\'s the Claude Desktop configuration to use the above server:\n\n \n\nOn Windows, wrap npx with cmd /c:\n\n \n\nAdditional examples of using the Claude Desktop as an MCP client might look like:\n\n \n\nOn Windows, apply the same wrapper to each npx-based entry above by changing "command" to "cmd" and prepending "/c", "npx" to the existing args. Leave uvx entries unchanged.\n\n🛠️ Creating Your Own Server\n\nInterested in creating your own MCP server? Visit the official documentation at modelcontextprotocol.io for comprehensive guides, best practices, and technical details on implementing MCP servers.\n\n📚 Learn More\n\nSee ADDITIONAL.md for a curated list of frameworks and resources that simplify building MCP servers and clients.\n\n🤝 Contributing\n\nSee CONTRIBUTING.md for information about contributing to this repository.\n\n📦 Releasing\n\nSee RELEASING.md for how packages are published (OIDC trusted publishing from CI — no registry tokens) and how to retry a failed publish.\n\n🔒 Security\n\nSee SECURITY.md for reporting security vulnerabilities.\n\n📜 License\n\nThis project is licensed under the Apache License, Version 2.0 for new contributions, with existing code under MIT - see the LICENSE file for details.\n\n💬 Community\n\n- GitHub Discussions\n\n⭐ Support\n\nIf you find MCP servers useful, please consider starring the repository and contributing new servers or improvements!\n\n---\n\nManaged by Anthropic, but built together with the community. The Model Context Protocol is open source and we encourage everyone to contribute their own servers and improvements!', 'readme_raw_len': 5101, 'one_line_zh': '📇 🏠 - 直接访问本地文件系统。', 'one_line_plain': 'Direct access to read and write local files'}, {'id': 4016, 'key': 'modelcontextprotocol-servers-tree-main-src-git', 'kind': 'mcp', 'name': 'modelcontextprotocol/server-git', 'one_line': '🐍 🏠 - Read, search and analyze local Git repositories', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol Servers', 'url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/git', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/git', 'downloads': None, 'stars': 91381, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/modelcontextprotocol.png?size=64', 'official_desc': '🐍 🏠 - Read, search and analyze local Git repositories.', 'stars_own': 91381, 'stars_creator': None, 'stars_own_repo': 'modelcontextprotocol/servers', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol servers\n\nThis repository is a collection of *reference implementations* for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources.\n\n> [!IMPORTANT]\n> If you are looking for a list of MCP servers, you can browse published servers on the MCP Registry. The repository served by this README is dedicated to housing just the small number of reference servers maintained by the MCP steering group.\n\n> [!WARNING]\n> The servers in this repository are intended as **reference implementations** to demonstrate MCP features and SDK usage. They are meant to serve as educational examples for developers building their own MCP servers, not as production-ready solutions. Developers should evaluate their own security requirements and implement appropriate safeguards based on their specific threat model and use case.\n\nThe servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.\nTypically, each MCP server is implemented with an MCP SDK:\n\n- C# MCP SDK\n- Go MCP SDK\n- Java MCP SDK\n- Kotlin MCP SDK\n- PHP MCP SDK\n- Python MCP SDK\n- Ruby MCP SDK\n- Rust MCP SDK\n- Swift MCP SDK\n- TypeScript MCP SDK\n\n🌟 Reference Servers\n\nThese servers aim to demonstrate MCP features and the official SDKs.\n\n- **Everything** - Reference / test server with prompts, resources, and tools.\n- **Fetch** - Web content fetching and conversion for efficient LLM usage.\n- **Filesystem** - Secure file operations with configurable access controls.\n- **Git** - Tools to read, search, and manipulate Git repositories.\n- **Memory** - Knowledge graph-based persistent memory system.\n- **Sequential Thinking** - Dynamic and reflective problem-solving through thought sequences.\n- **Time** - Time and timezone conversion capabilities.\n\nArchived\n\nThe following reference servers are now archived and can be found at servers-archived.\n\n- **AWS KB Retrieval** - Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.\n- **Brave Search** - Web and local search using Brave\'s Search API. Has been replaced by the official server (@brave/brave-search-mcp-server).\n- **EverArt** - AI image generation using various models.\n- **GitHub** - Repository management, file operations, and GitHub API integration.\n- **GitLab** - GitLab API, enabling project management.\n- **Google Drive** - File access and search capabilities for Google Drive.\n- **Google Maps** - Location services, directions, and place details.\n- **PostgreSQL** - Read-only database access with schema inspection.\n- **Puppeteer** - Browser automation and web scraping.\n- **Redis** - Interact with Redis key-value stores.\n- **Sentry** - Retrieving and analyzing issues from Sentry.io.\n- **Slack** - Channel management and messaging capabilities. Now maintained by Zencoder\n- **SQLite** - Database interaction and business intelligence capabilities.\n\n🚀 Getting Started\n\nUsing MCP Servers in this Repository\nTypeScript-based servers in this repository can be used directly with npx.\n\nFor example, this will start the Memory server:\n \n\nPython-based servers in this repository can be used directly with uvx or pip. uvx is recommended for ease of use and setup.\n\nFor example, this will start the Git server:\n \n\nFollow these instructions to install uv / uvx and these to install pip.\n\nUsing an MCP Client\nHowever, running a server on its own isn\'t very useful, and should instead be configured into an MCP client. For example, here\'s the Claude Desktop configuration to use the above server:\n\n \n\nOn Windows, wrap npx with cmd /c:\n\n \n\nAdditional examples of using the Claude Desktop as an MCP client might look like:\n\n \n\nOn Windows, apply the same wrapper to each npx-based entry above by changing "command" to "cmd" and prepending "/c", "npx" to the existing args. Leave uvx entries unchanged.\n\n🛠️ Creating Your Own Server\n\nInterested in creating your own MCP server? Visit the official documentation at modelcontextprotocol.io for comprehensive guides, best practices, and technical details on implementing MCP servers.\n\n📚 Learn More\n\nSee ADDITIONAL.md for a curated list of frameworks and resources that simplify building MCP servers and clients.\n\n🤝 Contributing\n\nSee CONTRIBUTING.md for information about contributing to this repository.\n\n📦 Releasing\n\nSee RELEASING.md for how packages are published (OIDC trusted publishing from CI — no registry tokens) and how to retry a failed publish.\n\n🔒 Security\n\nSee SECURITY.md for reporting security vulnerabilities.\n\n📜 License\n\nThis project is licensed under the Apache License, Version 2.0 for new contributions, with existing code under MIT - see the LICENSE file for details.\n\n💬 Community\n\n- GitHub Discussions\n\n⭐ Support\n\nIf you find MCP servers useful, please consider starring the repository and contributing new servers or improvements!\n\n---\n\nManaged by Anthropic, but built together with the community. The Model Context Protocol is open source and we encourage everyone to contribute their own servers and improvements!', 'readme_raw_len': 5101, 'one_line_zh': '读取、检索并分析本地 Git 仓库的官方 MCP 服务。', 'one_line_plain': 'Read, search and analyze local Git repositories'}, {'id': 2682, 'key': 'modelcontextprotocol-servers-tree-main-src-memory', 'kind': 'mcp', 'name': 'modelcontextprotocol/server-memory', 'one_line': '📇 🏠 - Knowledge graph-based persistent memory for maintaining context', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol Servers', 'url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/memory', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/memory', 'downloads': None, 'stars': 91381, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/modelcontextprotocol.png?size=64', 'official_desc': '📇 🏠 - Knowledge graph-based persistent memory for maintaining context.', 'stars_own': 91381, 'stars_creator': None, 'stars_own_repo': 'modelcontextprotocol/servers', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol servers\n\nThis repository is a collection of *reference implementations* for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources.\n\n> [!IMPORTANT]\n> If you are looking for a list of MCP servers, you can browse published servers on the MCP Registry. The repository served by this README is dedicated to housing just the small number of reference servers maintained by the MCP steering group.\n\n> [!WARNING]\n> The servers in this repository are intended as **reference implementations** to demonstrate MCP features and SDK usage. They are meant to serve as educational examples for developers building their own MCP servers, not as production-ready solutions. Developers should evaluate their own security requirements and implement appropriate safeguards based on their specific threat model and use case.\n\nThe servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.\nTypically, each MCP server is implemented with an MCP SDK:\n\n- C# MCP SDK\n- Go MCP SDK\n- Java MCP SDK\n- Kotlin MCP SDK\n- PHP MCP SDK\n- Python MCP SDK\n- Ruby MCP SDK\n- Rust MCP SDK\n- Swift MCP SDK\n- TypeScript MCP SDK\n\n🌟 Reference Servers\n\nThese servers aim to demonstrate MCP features and the official SDKs.\n\n- **Everything** - Reference / test server with prompts, resources, and tools.\n- **Fetch** - Web content fetching and conversion for efficient LLM usage.\n- **Filesystem** - Secure file operations with configurable access controls.\n- **Git** - Tools to read, search, and manipulate Git repositories.\n- **Memory** - Knowledge graph-based persistent memory system.\n- **Sequential Thinking** - Dynamic and reflective problem-solving through thought sequences.\n- **Time** - Time and timezone conversion capabilities.\n\nArchived\n\nThe following reference servers are now archived and can be found at servers-archived.\n\n- **AWS KB Retrieval** - Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.\n- **Brave Search** - Web and local search using Brave\'s Search API. Has been replaced by the official server (@brave/brave-search-mcp-server).\n- **EverArt** - AI image generation using various models.\n- **GitHub** - Repository management, file operations, and GitHub API integration.\n- **GitLab** - GitLab API, enabling project management.\n- **Google Drive** - File access and search capabilities for Google Drive.\n- **Google Maps** - Location services, directions, and place details.\n- **PostgreSQL** - Read-only database access with schema inspection.\n- **Puppeteer** - Browser automation and web scraping.\n- **Redis** - Interact with Redis key-value stores.\n- **Sentry** - Retrieving and analyzing issues from Sentry.io.\n- **Slack** - Channel management and messaging capabilities. Now maintained by Zencoder\n- **SQLite** - Database interaction and business intelligence capabilities.\n\n🚀 Getting Started\n\nUsing MCP Servers in this Repository\nTypeScript-based servers in this repository can be used directly with npx.\n\nFor example, this will start the Memory server:\n \n\nPython-based servers in this repository can be used directly with uvx or pip. uvx is recommended for ease of use and setup.\n\nFor example, this will start the Git server:\n \n\nFollow these instructions to install uv / uvx and these to install pip.\n\nUsing an MCP Client\nHowever, running a server on its own isn\'t very useful, and should instead be configured into an MCP client. For example, here\'s the Claude Desktop configuration to use the above server:\n\n \n\nOn Windows, wrap npx with cmd /c:\n\n \n\nAdditional examples of using the Claude Desktop as an MCP client might look like:\n\n \n\nOn Windows, apply the same wrapper to each npx-based entry above by changing "command" to "cmd" and prepending "/c", "npx" to the existing args. Leave uvx entries unchanged.\n\n🛠️ Creating Your Own Server\n\nInterested in creating your own MCP server? Visit the official documentation at modelcontextprotocol.io for comprehensive guides, best practices, and technical details on implementing MCP servers.\n\n📚 Learn More\n\nSee ADDITIONAL.md for a curated list of frameworks and resources that simplify building MCP servers and clients.\n\n🤝 Contributing\n\nSee CONTRIBUTING.md for information about contributing to this repository.\n\n📦 Releasing\n\nSee RELEASING.md for how packages are published (OIDC trusted publishing from CI — no registry tokens) and how to retry a failed publish.\n\n🔒 Security\n\nSee SECURITY.md for reporting security vulnerabilities.\n\n📜 License\n\nThis project is licensed under the Apache License, Version 2.0 for new contributions, with existing code under MIT - see the LICENSE file for details.\n\n💬 Community\n\n- GitHub Discussions\n\n⭐ Support\n\nIf you find MCP servers useful, please consider starring the repository and contributing new servers or improvements!\n\n---\n\nManaged by Anthropic, but built together with the community. The Model Context Protocol is open source and we encourage everyone to contribute their own servers and improvements!', 'readme_raw_len': 5101, 'one_line_zh': '基于知识图谱的持久记忆，用于维持上下文。', 'one_line_plain': 'Knowledge graph-based persistent memory for maintaining context'}, {'id': 1630, 'key': 'storybookjs-storybook-tree-next-code-addons-mcp', 'kind': 'mcp', 'name': 'storybookjs/storybook', 'one_line': '📇 🏠 - Helps agents write and test Storybook stories for UI components', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Storybook is the industry standard workshop for building, documenting, and testing UI components in isolation', 'url': 'https://github.com/storybookjs/storybook/tree/next/code/addons/mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/storybookjs/storybook/tree/next/code/addons/mcp', 'downloads': None, 'stars': 91338, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/storybookjs.png?size=64', 'official_desc': '📇 🏠 - Helps agents write and test Storybook stories for UI components.', 'stars_own': 91338, 'stars_creator': None, 'stars_own_repo': 'storybookjs/storybook', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Build bulletproof UI components faster \n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \nStorybook is a frontend workshop for building UI components and pages in isolation. Thousands of teams use it for UI development, testing, and documentation. Find out more at storybook.js.org !\n \n\n \n \n \n\n \n View README for: \n \n \n \n\nTable of contents\n\n- 🚀 Getting Started\n- 📒 Projects\n - 🛠 Supported Frameworks & Examples\n - 🔗Addons\n- 🏅 Badges & Presentation materials\n- 👥 Community\n- 👏 Contributing\n - 👨\u200d💻 Development scripts\n - 💸 Sponsors\n - 💵 Backers\n- :memo: License\n\nGetting Started\n\nVisit Storybook's website to learn more about Storybook and to get started.\n\nDocumentation\n\nDocumentation can be found on Storybook's docs site.\n\nExamples\n\nView Component Encyclopedia to see how leading teams use Storybook.\n\nUse storybook.new to quickly create an example project in Stackblitz.\n\nStorybook comes with a lot of addons for component design, documentation, testing, interactivity, and so on. Storybook's API makes it possible to configure and extend in various ways. It has even been extended to support React Native, Android, iOS, and Flutter development for mobile.\n\nCommunity\n\nFor additional help, share your issue in the repo's GitHub Discussions.\n\nProjects\n\nSupported Frameworks\n\n| Renderer | Demo | |\n| -------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |\n| React | | |\n| Angular | | |\n| Vue 3 | | |\n| Web components | | |\n| React Native | | |\n| HTML | | |\n| Ember | | |\n| Svelte | | |\n| Preact | | |\n| Qwik | | |\n| SolidJS | | |\n| Android, iOS, Flutter | | |\n\nAddons\n\n| Addons | |\n| ------------------------------------------------------------------------- | -------------------------------------------------------------------------- |\n| a11y | Test components for user accessibility in Storybook |\n| actions | Log actions as users interact with components in the Storybook UI |\n| backgrounds | Let users choose backgrounds in the Storybook UI |\n| cssresources | Dynamically add/remove CSS resources to the component iframe |\n| design assets | View images, videos, and weblinks alongside your story |\n| docs | Add high quality documentation to your components |\n| events | Interactively fire events to components that respond to EventEmitter |\n| google-analytics | Reports google analytics on stories |\n| graphql | Query a GraphQL server within Storybook stories |\n| jest | View the results of components' unit tests in Storybook |\n| links | Create links between stories |\n| measure | Visually inspect the layout and box model within the Storybook UI |\n| outline | Visually debug the CSS layout and alignment within the Storybook UI |\n| query params | Mock query params |\n| viewport | Change display sizes and layouts for responsive components using Storybook |\n\nSee Addon / Framework Support Table\n\nTo continue improving your experience, we have to eventually deprecate or remove certain addons in favor of new and better tools.\n\nIf you're using info/notes, we highly recommend you migrate to docs instead, and here is a guide to help you.\n\nIf you're using contexts, we highly recommend you migrate to toolbars and here is a guide to help you.\n\nIf you're using addon-storyshots, we highly recommend you migrate to the Storybook test-runner and here is a guide to help you.\n\nBadges & Presentation materials\n\nWe have a badge! Link it to your live Storybook example.\n\n \n\n \n\nIf you're looking for material to use in your Storybook presentation, such as logos, video material, and the colors we use, you can find it all on our brand repo.\n\nCommunity\n\n- Tweeting via @storybookjs\n- Blogging at storybook.js.org and Medium\n- Chatting on Discord\n- Videos and streams at YouTube\n\nContributing\n\nContributions to Storybook are always welcome!\n\n- 📥 Pull requests and 🌟 Stars are always welcome.\n- Read our contributing guide to get started,\n or find us on Discord, we will take the time to guide you.\n\nLooking for a first issue to tackle?\n\n- We tag issues with when we think they are well suited for people who are new to the codebase or OSS in general.\n- Talk to us, we'll find something that suits your skills and learning interests.\n\nDevelopment scripts\n\nStorybook is organized as a monorepo. Useful scripts include:\n\nyarn start\n\n> Runs a sandbox template storybook with test stories\n\nyarn task\n\n> As above, but gives you options to customize the sandbox (e.g. selecting other frameworks)\n\nyarn lint\n\n> boolean check if code conforms to linting rules - uses remark & eslint\n\n- yarn lint:js - will check js\n- yarn lint:md - will check markdown + code samples\n- yarn lint:js --fix - will automatically fix js\n\nyarn test\n\n> boolean check if unit tests all pass - uses jest\n\n- yarn run test --core --watch - will run core tests in watch-mode\n\nSponsors\n\nBecome a sponsor to have your logo and website URL on our README on Github. \\[Become a sponsor]\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\nBackers\n\nBy making a recurring donation, you can support us and our work. \\[Become a backer]\n\n \n\nLicense\n\nMIT\n\n-the end-", 'readme_raw_len': 5365, 'one_line_zh': '📇 🏠 - 帮助 Agent 为 UI 组件编写并测试 Storybook 故事', 'one_line_plain': 'MCP helps agents write and test Storybook stories for UI components'}, {'id': 2762, 'key': 'upstash-context7', 'kind': 'mcp', 'name': 'upstash/context7', 'one_line': '📇 ☁️ - Up-to-date code documentation for LLMs and AI code editors', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Context7 Platform Up-to-date docs for AI agents', 'url': 'https://github.com/upstash/context7', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/upstash/context7', 'downloads': None, 'stars': 63321, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/upstash.png?size=64', 'official_desc': '📇 ☁️ - Up-to-date code documentation for LLMs and AI code editors.', 'stars_own': 63321, 'stars_creator': None, 'stars_own_repo': 'upstash/context7', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Context7 - Up-to-date Docs for AI Agents\n\n \n\n \n\n❌ Without Context7\n\nLLMs rely on outdated or generic information about the libraries you use. You get:\n\n- ❌ Code examples are outdated and based on year-old training data\n- ❌ Hallucinated APIs that don\'t even exist\n- ❌ Generic answers for old package versions\n\n✅ With Context7\n\nContext7 pulls up-to-date, version-specific documentation and code examples straight from the source — and gives them to your coding agent.\n\n \n\n \n\n \n\nContext7 fetches up-to-date code examples and documentation right into your coding agent\'s context. No tab-switching, no hallucinated APIs that don\'t exist, no outdated code generation.\n\nWorks in two modes:\n\n- **CLI + Skills** — installs a skill that guides your agent to fetch docs using ctx7 CLI commands (no MCP required)\n- **MCP** — registers a Context7 MCP server so your agent can call documentation tools natively\n\nInstallation\n\n> [!NOTE]\n> **API Key Recommended**: Get a free API key at context7.com/dashboard for higher rate limits.\n\nSet up Context7 for your coding agents with a single command. The ctx7 CLI requires Node.js 22.13 or newer.\n\n \n\nAuthenticates via OAuth, generates an API key, and installs the appropriate skill. You can choose between CLI + Skills or MCP mode. Use --cursor, --claude, or --opencode to target a specific agent.\n\nTo remove the generated setup later, run npx ctx7 remove. If you globally installed the CLI with npm install -g ctx7, remove that package separately with npm uninstall -g ctx7.\n\nTo configure manually, use the Context7 server URL https://mcp.context7.com/mcp with your MCP client and pass your API key via the Authorization: Bearer YOUR_API_KEY header. See the link below for client-specific setup instructions.\n\n**Manual Installation / Other Clients →**\n\nImportant Tips\n\nUse Library Id\n\nIf you already know exactly which library you want to use, add its Context7 ID to your prompt. That way, Context7 can skip the library-matching step and directly retrieve docs.\n\n \n\nThe slash syntax tells Context7 exactly which library to load docs for.\n\nSpecify a Version\n\nTo get documentation for a specific library version, just mention the version in your prompt:\n\n \n\nContext7 will automatically match the appropriate version.\n\nAdd a Rule\n\nIf you installed via ctx7 setup, a skill is configured automatically that triggers Context7 for library-related questions. To set up a rule manually instead, add one to your coding agent:\n\n- **Cursor**: Cursor Settings > Rules\n- **Claude Code**: CLAUDE.md\n- Or the equivalent in your coding agent\n\n**Example rule:**\n\n \n\nAvailable Tools\n\nCLI Commands\n\n- ctx7 library : Searches the Context7 index by library name and returns matching libraries with their IDs.\n- ctx7 docs : Retrieves documentation for a library using a Context7-compatible library ID (e.g., /mongodb/docs, /vercel/next.js).\n\nMCP Tools\n\n- resolve-library-id: Resolves a general library name into a Context7-compatible library ID.\n - query (required): The user\'s question or task (used to rank results by relevance)\n - libraryName (required): The name of the library to search for\n- query-docs: Retrieves documentation for a library using a Context7-compatible library ID.\n - libraryId (required): Exact Context7-compatible library ID (e.g., /mongodb/docs, /vercel/next.js)\n - query (required): The question or task to get relevant documentation for\n - language (optional): The programming language you write in, used to rank code examples in that language first\n\nMore Documentation\n\n- CLI Reference - Full CLI documentation\n- MCP Clients - Manual MCP installation for 30+ clients\n- Adding Libraries - Submit your library to Context7\n- Troubleshooting - Common issues and solutions\n- API Reference - REST API documentation\n- Developer Guide - Run Context7 MCP locally\n\nPackages\n\n- @upstash/context7-mcp - MCP server\n- ctx7 - CLI\n- @upstash/context7-sdk - TypeScript SDK\n- @upstash/context7-tools-ai-sdk - Vercel AI SDK tools\n- @upstash/context7-pi - pi.dev extension\n\nDisclaimer\n\n1- Context7 projects are community-contributed and while we strive to maintain high quality, we cannot guarantee the accuracy, completeness, or security of all library documentation. Projects listed in Context7 are developed and maintained by their respective owners, not by Context7. If you encounter any suspicious, inappropriate, or potentially harmful content, please use the "Report" button on the project page to notify us immediately. We take all reports seriously and will review flagged content promptly to maintain the integrity and safety of our platform. By using Context7, you acknowledge that you do so at your own discretion and risk.\n\n2- This repository hosts the MCP server’s source code. The supporting components — API backend, parsing engine, and crawling engine — are private and not part of this repository.\n\n🤝 Connect with Us\n\nStay updated and join our community:\n\n- 📢 Follow us on X for the latest news and updates\n- 🌐 Visit our Website\n- 💬 Join our Discord Community\n\n📺 Context7 In Media\n\n- Better Stack: "Free Tool Makes Cursor 10x Smarter"\n- Cole Medin: "This is Hands Down the BEST MCP Server for AI Coding Assistants"\n- Income Stream Surfers: "Context7 + SequentialThinking MCPs: Is This AGI?"\n- Julian Goldie SEO: "Context7: New MCP AI Agent Update"\n- JeredBlu: "Context 7 MCP: Get Documentation Instantly + VS Code Setup"\n- Income Stream Surfers: "Context7: The New MCP Server That Will CHANGE AI Coding"\n- AICodeKing: "Context7 + Cline & RooCode: This MCP Server Makes CLINE 100X MORE EFFECTIVE!"\n- Sean Kochel: "5 MCP Servers For Vibe Coding Glory (Just Plug-In & Go)"\n\n📄 License\n\nMIT', 'readme_raw_len': 5621, 'one_line_zh': '向 LLM 和 AI 代码编辑器提供最新代码文档', 'one_line_plain': 'Provide up-to-date code documentation for LLMs and AI code editors'}, {'id': 1431, 'key': 'glips-figma-context-mcp', 'kind': 'mcp', 'name': 'GLips/Figma-Context-MCP', 'one_line': '📇 🏠 - Gives coding agents direct access to Figma data to implement designs in one shot', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server to provide Figma layout information to AI coding agents like Cursor', 'url': 'https://github.com/GLips/Figma-Context-MCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/GLips/Figma-Context-MCP', 'downloads': None, 'stars': 16012, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/GLips.png?size=64', 'official_desc': '📇 🏠 - Gives coding agents direct access to Figma data to implement designs in one shot.', 'stars_own': 16012, 'stars_creator': None, 'stars_own_repo': 'GLips/Figma-Context-MCP', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Framelink MCP for Figma \n Give your coding agent access to your Figma data. Implement designs in any framework in one-shot. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n\nGive Cursor and other AI-powered coding tools access to your Figma files with this Model Context Protocol server.\n\nWhen Cursor has access to Figma design data, it's **way** better at one-shotting designs accurately than alternative approaches like pasting screenshots.\n\n See quickstart instructions → \n\nDemo\n\nWatch a demo of building a UI in Cursor with Figma design data\n\n \n\nHow it works\n\n1. Open your IDE's chat (e.g. agent mode in Cursor).\n2. Paste a link to a Figma file, frame, or group.\n3. Ask Cursor to do something with the Figma file—e.g. implement the design.\n4. Cursor will fetch the relevant metadata from Figma and use it to write your code.\n\nThis MCP server is specifically designed for use with Cursor. Before responding with context from the Figma API, it simplifies and translates the response so only the most relevant layout and styling information is provided to the model.\n\nReducing the amount of context provided to the model helps make the AI more accurate and the responses more relevant.\n\nGetting Started\n\nMany code editors and other AI clients use a configuration file to manage MCP servers.\n\nThe figma-developer-mcp server can be configured by adding the following to your configuration file.\n\n> NOTE: You will need to create a Figma access token to use this server. Instructions on how to create a Figma API access token can be found here.\n\nMacOS / Linux\n\n \n\nWindows\n\n \n\nOr you can set FIGMA_API_KEY and PORT in the env field.\n\nIf you need more information on how to configure the Framelink MCP for Figma, see the Framelink docs.\n\nStar History\n\n \n\nLearn More\n\nThe Framelink MCP for Figma is simple but powerful. Get the most out of it by learning more at the Framelink site.", 'readme_raw_len': 1859, 'one_line_zh': '让编码 Agent 直接访问 Figma 数据，一步到位实现设计还原', 'one_line_plain': 'Give coding agents direct access to Figma data to implement designs instantly'}, {'id': 813, 'key': 'wonderwhy-er-desktopcommandermcp', 'kind': 'mcp', 'name': 'wonderwhy-er/DesktopCommanderMCP', 'one_line': '📇 🏠 🍎 🪟 🐧 - Manage and execute programs, and read, write, search and edit code and text files', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'This is MCP server for Claude that gives it terminal control, file system search and diff file editing capabilities', 'url': 'https://github.com/wonderwhy-er/DesktopCommanderMCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/wonderwhy-er/DesktopCommanderMCP', 'downloads': None, 'stars': 10102, 'installs': None, 'mentions': 22, 'score': 2203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/wonderwhy-er.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - A swiss-army-knife that can manage/execute programs and read/write/search/edit code and text files.', 'stars_own': 10102, 'stars_creator': None, 'stars_own_repo': 'wonderwhy-er/DesktopCommanderMCP', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Desktop Commander MCP\nSearch, update, manage files and run terminal commands with AI\n\n \n \n \n \n \n\n \n\nWork with code and text, run processes, and automate tasks, going far beyond other AI editors - while using host client subscriptions instead of API token costs.\n\n \n \n \n\nTable of Contents\n- Features\n- How to install\n- Getting Started\n- Usage\n- File Preview UI & Markdown Editor\n- Handling Long-Running Commands\n- Work in Progress and TODOs\n- Sponsors and Supporters\n- Website\n- Media\n- Testimonials\n- Frequently Asked Questions\n- Contributing\n- License\n\nAll of your AI development tools in one place.\nDesktop Commander puts all dev tools in one chat.\nExecute long-running terminal commands on your computer and manage processes through Model Context Protocol (MCP). Built on top of MCP Filesystem Server to provide additional search and replace file editing capabilities.\n\nFeatures\n\n- **Remote AI Control** - Use Desktop Commander from ChatGPT, Claude web, and other AI services via Remote MCP\n- **File Preview UI** - Visual file previews in Claude Desktop with rendered markdown, inline images, expandable content, built-in markdown editor, and quick "Open in folder" access\n- **Enhanced terminal commands with interactive process control**\n- **Execute code in memory (Python, Node.js, R) without saving files**\n- **Instant data analysis - just ask to analyze CSV/JSON/Excel files**\n- **Native Excel file support** - Read, write, edit, and search Excel files (.xlsx, .xls, .xlsm) without external tools\n- **PDF support** - Read PDFs with text extraction, create new PDFs from markdown, modify existing PDFs\n- **DOCX support** - Read, create, edit, and search Word documents (.docx) with surgical XML editing and markdown-to-DOCX conversion\n- **Interact with running processes (SSH, databases, development servers)**\n- Execute terminal commands with output streaming\n- Command timeout and background execution support\n- Process management (list and kill processes)\n- Session management for long-running commands\n- **Process output pagination** - Read terminal output with offset/length controls to prevent context overflow\n- Server configuration management:\n - Get/set configuration values\n - Update multiple settings at once\n - Dynamic configuration changes without server restart\n- Full filesystem operations:\n - Read/write files (text, Excel, PDF, DOCX)\n - Create/list directories\n - **Recursive directory listing** with configurable depth and context overflow protection for large folders\n - Move files/directories\n - Search files and content (including Excel content)\n - Get file metadata\n - **Negative offset file reading**: Read from end of files using negative offset values (like Unix tail)\n- Code editing capabilities:\n - Surgical text replacements for small changes\n - Full file rewrites for major changes\n - Multiple file support\n - Pattern-based replacements\n - vscode-ripgrep based recursive code or text search in folders\n- Local tool-call history and audit logs:\n - Tool calls and arguments are recorded locally on the machine running Desktop Commander\n - Recent call history with bounded output previews is available through get_recent_tool_calls\n - Size-based rotation/trimming keeps the active history files bounded\n- Safety guardrails (not a sandbox — see SECURITY.md):\n - Symlink traversal prevention on file operations\n - Command blocklist for accidental execution\n - Docker isolation for complete isolation\n\nHow to install\n\nInstall in Claude Desktop\n\nDesktop Commander offers multiple installation methods for Claude Desktop.\n\n> **📋 Update & Uninstall Information:** Options 1, 2, 3, 4, and 6 have automatic updates. Option 5 requires manual updates. See below for details.\n\n \n Option 1: Install through npx ⭐ Auto-Updates (Requires Node.js) \n\nJust run this in terminal:\n \n\nFor debugging mode (allows Node.js inspector connection):\n \n\n**Command line options during setup:**\n- --debug: Enable debugging mode for Node.js inspector\n- --no-onboarding: Disable onboarding prompts for new users\n\nRestart Claude if running.\n\n**✅ Auto-Updates:** Yes - automatically updates when you restart Claude \n**🔄 Manual Update:** Run the setup command again \n**🗑️ Uninstall:** Run npx @wonderwhy-er/desktop-commander@latest remove\n\n \n\n \n Option 2: Using bash script installer (macOS) ⭐ Auto-Updates (Installs Node.js if needed) \n\n \nThis script handles all dependencies and configuration automatically.\n\n**✅ Auto-Updates:** Yes \n**🔄 Manual Update:** Re-run the bash installer command above \n**🗑️ Uninstall:** Run npx @wonderwhy-er/desktop-commander@latest remove\n\n \n\n \n Option 3: Installing via Smithery ⭐ Auto-Updates (Requires Node.js) \n\n1. **Visit:** https://smithery.ai/server/@wonderwhy-er/desktop-commander\n2. **Login to Smithery** if you haven\'t already\n3. **Select your client** (Claude Desktop) on the right side\n4. **Install with the provided key** that appears after selecting your client\n5. **Restart Claude Desktop**\n\n**✅ Auto-Updates:** Yes - automatically updates when you restart Claude \n**🔄 Manual Update:** Visit the Smithery page and reinstall \n\n \n\n \n Option 4: Add to claude_desktop_config manually ⭐ Auto-Updates (Requires Node.js) \n\nAdd this entry to your claude_desktop_config.json:\n\n- On Mac: ~/Library/Application Support/Claude/claude_desktop_config.json\n- On Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n- On Linux: ~/.config/Claude/claude_desktop_config.json\n\n \nRestart Claude if running.\n\n**✅ Auto-Updates:** Yes - automatically updates when you restart Claude \n**🔄 Manual Update:** Run the setup command again \n**🗑️ Uninstall:** Run npx @wonderwhy-er/desktop-commander@latest remove or remove the entry from your claude_desktop_config.json\n\n \n\n \n Option 5: Checkout locally ❌ Manual Updates (Requires Node.js) \n\n \nRestart Claude if running.\n\nThe setup command will install dependencies, build the server, and configure Claude\'s desktop app.\n\n**❌ Auto-Updates:** No - requires manual git updates \n**🔄 Manual Update:** cd DesktopCommanderMCP && git pull && npm run setup \n**🗑️ Uninstall:** Run npx @wonderwhy-er/desktop-commander@latest remove or remove the cloned directory and MCP server entry from Claude config\n\n \n\n \n Option 6: Docker Installation 🐳 ⭐ Auto-Updates (No Node.js Required) \n\nPerfect for users who want isolation or don\'t have Node.js installed. Runs in a sandboxed Docker container with a persistent work environment.\n\n**Prerequisites:** Docker Desktop installed **and running**, Claude Desktop app installed.\n\n**macOS/Linux:**\n json\n{\n "mcpServers": {\n "desktop-commander-in-docker": {\n "command": "docker",\n "args": ["run", "-i", "--rm", "mcp/desktop-commander:latest"]\n }\n }\n}\n json\n{\n "mcpServers": {\n "desktop-commander-in-docker": {\n "command": "docker",\n "args": [\n "run", "-i", "--rm",\n "-v", "/Users/username/Desktop:/mnt/desktop",\n "-v", "/Users/username/Documents:/mnt/documents",\n "mcp/desktop-commander:latest"\n ]\n }\n }\n}\n json\n{\n "mcpServers": {\n "desktop-commander-in-docker": {\n "command": "docker",\n "args": [\n "run", "-i", "--rm",\n "-v", "dc-system:/usr",\n "-v", "dc-home:/root", \n "-v", "dc-workspace:/workspace",\n "-v", "dc-packages:/var",\n "-v", "/Users/username/Projects:/mnt/Projects",\n "-v", "/Users/username/Downloads:/mnt/Downloads",\n "mcp/desktop-commander:latest"\n ]\n }\n }\n}\n bash\nCheck status\nbash \n\n**✅ Auto-Updates:** Yes - latest tag automatically gets newer versions \n**🔄 Manual Update:** docker pull mcp/desktop-commander:latest then restart Claude \n\n \n\nInstall in Other Clients\n\nDesktop Commander works with any MCP-compatible client. The standard JSON configuration is:\n\n \n\nAdd this to your client\'s MCP configuration file at the locations below:\n\n \n Cursor \n\n \n\nView MCP Server in Directory\n\nOr add manually to ~/.cursor/mcp.json (global) or .cursor/mcp.json in your project folder (project-specific).\n\nSee Cursor MCP docs for more info.\n\n \n\n \n Windsurf \n\nAdd to ~/.codeium/windsurf/mcp_config.json. See Windsurf MCP docs for more info.\n\n \n\n \n VS Code / GitHub Copilot \n\nAdd to .vscode/mcp.json in your pr', 'readme_raw_len': 36608, 'one_line_zh': '📇 🏠 🍎 🪟 🐧 - 管理与执行程序，并读取、写入、搜索和编辑代码与文本文件', 'one_line_plain': 'Manage and execute programs, and read, write, search and edit code and text files'}, {'id': 3921, 'key': 'sooperset-mcp-atlassian', 'kind': 'mcp', 'name': 'sooperset/mcp-atlassian', 'one_line': '🐍 ☁️ - Search, read, create and manage Confluence and Jira content, supporting Confluence Cloud, Jira Cloud…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for Atlassian tools (Confluence, Jira)', 'url': 'https://github.com/sooperset/mcp-atlassian', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/sooperset/mcp-atlassian', 'downloads': None, 'stars': 6019, 'installs': None, 'mentions': 22, 'score': 2203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/sooperset.png?size=64', 'official_desc': '🐍 ☁️ - MCP server for Atlassian products (Confluence and Jira). Supports Confluence Cloud, Jira Cloud, and Jira Server/Data Center. Provides comprehensive tools for searching, reading, creating, and m', 'stars_own': 6019, 'stars_creator': None, 'stars_own_repo': 'sooperset/mcp-atlassian', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'MCP Atlassian\n\n \n \n \n \n \n \n\nModel Context Protocol (MCP) server for Atlassian products (Confluence and Jira). Supports both Cloud and Server/Data Center deployments.\n\nhttps://github.com/user-attachments/assets/35303504-14c6-4ae4-913b-7c25ea511c3e\n\n \n Confluence Demo \n\nhttps://github.com/user-attachments/assets/7fe9c488-ad0c-4876-9b54-120b666bb785\n\n \n\nQuick Start\n\n1. Get Your API Token\n\nGo to https://id.atlassian.com/manage-profile/security/api-tokens and create a token.\n\n> For Server/Data Center, use a Personal Access Token instead. See Authentication.\n\n2. Configure Your IDE\n\nAdd to your Claude Desktop or Cursor MCP configuration:\n\n \n\n> **Server/Data Center users**: Use JIRA_PERSONAL_TOKEN instead of JIRA_USERNAME + JIRA_API_TOKEN. See Authentication for details.\n\nAutohand Code\n\nUse the same uvx server with your Atlassian credentials:\n\n \n\nAdd --scope project after add to keep the configuration in the current\nproject. See Autohand Code for current\ninstallation and CLI details.\n\n3. Start Using\n\nAsk your AI assistant to:\n- **"Find issues assigned to me in PROJ project"**\n- **"Search Confluence for onboarding docs"**\n- **"Create a bug ticket for the login issue"**\n- **"Update the status of PROJ-123 to Done"**\n\nDocumentation\n\nFull documentation is available at **mcp-atlassian.soomiles.com**.\n\nDocumentation is also available in llms.txt format, which LLMs can consume easily:\n- llms.txt — documentation sitemap\n- llms-full.txt — complete documentation\n\n| Topic | Description |\n|-------|-------------|\n| Installation | uvx, Docker, pip, from source |\n| Authentication | API tokens, PAT, OAuth 2.0 |\n| Configuration | IDE setup, environment variables |\n| HTTP Transport | SSE, streamable-http, multi-user |\n| Tools Reference | All Jira & Confluence tools |\n| Troubleshooting | Common issues & debugging |\n\nCompatibility\n\n| Product | Deployment | Support |\n|---------|------------|---------|\n| Confluence | Cloud | Fully supported |\n| Confluence | Server/Data Center | Supported (v6.0+) |\n| Jira | Cloud | Fully supported |\n| Jira | Server/Data Center | Supported (v8.14+) |\n\nKey Tools\n\n| Jira | Confluence |\n|------|------------|\n| jira_search - Search with JQL | confluence_search - Search with CQL |\n| jira_get_issue - Get issue details | confluence_get_page - Get page content |\n| jira_create_issue - Create issues | confluence_create_page - Create pages |\n| jira_update_issue - Update issues | confluence_update_page - Update pages |\n| jira_transition_issue - Change status | confluence_add_comment - Add comments |\n\n**98 tools total** — See Tools Reference for the complete list.\n\nSecurity\n\nNever share API tokens. Keep .env files secure. See SECURITY.md.\n\nContributing\n\nSee CONTRIBUTING.md for development setup.\n\nLicense\n\nMIT - See LICENSE. Not an official Atlassian product.', 'readme_raw_len': 2796, 'one_line_zh': '🐍 ☁️ - 搜索、阅读、创建并管理 Confluence 与 Jira 内容，支持 Confluence Cloud、Jira Cloud…', 'one_line_plain': 'Search, view, add and update content in Confluence and Jira Cloud sites'}, {'id': 797, 'key': 'shashankss1205-codegraphcontext', 'kind': 'mcp', 'name': 'shashankss1205/codegraphcontext', 'one_line': '🐍 🏠 🍎 🪟 🐧 - Index local code into a graph database to give AI assistants context, with graphical code…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/CodeGraphContext/CodeGraphContext', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Shashankss1205/CodeGraphContext', 'downloads': None, 'stars': 4299, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Shashankss1205.png?size=64', 'official_desc': '🐍 🏠 🍎 🪟 🐧 - Index local code into a graph database to give AI assistants context, with graphical code visualizations for humans.', 'stars_own': 4299, 'stars_creator': None, 'stars_own_repo': 'CodeGraphContext/CodeGraphContext', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': '🏗️ CodeGraphContext (CGC)\n\n**Turn code repositories into a queryable graph for AI agents.**\n\n🌐 **Languages:**\n- 🇬🇧 English\n- 🇨🇳 中文\n- 🇰🇷 한국어\n- 🇺🇦 Українська\n- 🇷🇺 Русский\n- 🇯🇵 日本語\n- 🇮🇳 தமிழ்\n- 🇪🇸 Español (Soon)\n\n🌍 **Help translate CodeGraphContext to your language by raising an issue & PR on GitHub Issues!**\n\n \n \n Bridge the gap between deep code graphs and AI context. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\nA powerful **MCP server** and **CLI toolkit** that indexes local code into a graph database to provide context to AI assistants and developers. Use it as a standalone CLI for comprehensive code analysis or connect it to your favorite AI IDE via MCP for AI-powered code understanding.\n\n---\n\n📍 Quick Navigation\n* 🚀 Quick Start \n* 📋 Prerequisites\n* 🏃 How to Run the Project Locally\n* 🌐 Supported Programming Languages \n* 🛠️ CLI Toolkit \n* 🤖 MCP Server \n* 🗄️ Database Options\n* 🔬 SCIP indexing (optional)\n\n---\n\n✨ Experience CGC\n\n👨🏻\u200d💻 Installation and CLI\n> Install in seconds with pip and unlock a powerful CLI for code graph analysis.\n \n\n🛠️ Indexing in Seconds\n> The CLI intelligently parses your tree-sitter nodes to build the graph.\n \n\n🤖 Powering your AI Assistant\n> Use natural language to query complex call-chains via MCP.\n \n\n---\n\nWhy CodeGraphContext?\n\nCodeGraphContext is built for the moment when plain text search stops being enough. It turns a repository into a graph of files, symbols, calls, inheritance, imports, and relationships so you can move from "where is this defined?" to "how does this actually connect?" without jumping between tools.\n\n \n\nComparison at a glance\n\n| Approach | Best for | Tradeoff |\n| --- | --- | --- |\n| grep / file search | Exact string lookup | Misses relationships and code structure |\n| RAG over code chunks | Natural-language retrieval | Can lose symbol-level precision |\n| CodeGraphContext | Repository-wide reasoning | Requires an indexing step |\n\nCommon use cases\n\n- Trace call chains across files and packages.\n- Understand class hierarchies, imports, and module boundaries.\n- Find dead code, hotspots, and unexpected dependencies.\n- Give an AI assistant the same structural context a maintainer would use.\n\nFAQ\n\n- **Do I need a remote service?** No. CGC works locally with embedded backends or with external graph databases when you want them.\n- **Is it only for one language?** No. It supports a broad mix of mainstream languages and keeps expanding.\n- **Can I use it with an AI assistant?** Yes. That is one of the main reasons the MCP server exists.\n\n---\n\nProject Details\n- **Version:** 0.5.1\n- **Authors:** Shashank Shekhar Singh \n- **License:** MIT License (See LICENSE for details)\n- **Website:** CodeGraphContext\n\n---\n\n👨\u200d💻 Maintainer\n**CodeGraphContext** is created and actively maintained by:\n\n**Shashank Shekhar Singh** \n- 📧 Email: shashankshekharsingh1205@gmail.com\n- 🐙 GitHub: @Shashankss1205\n- 🔗 LinkedIn: Shashank Shekhar Singh\n- 🌐 Website: codegraphcontext.vercel.app\n\n*Contributions and feedback are always welcome! Feel free to reach out for questions, suggestions, or collaboration opportunities.*\n\n---\n\nStar History\nStar History Chart ↗\n \n\n---\n\nFeatures\n- **Code Indexing:** Analyzes code and builds a knowledge graph of its components.\n- **Relationship Analysis:** Query for callers, callees, class hierarchies, call chains and more.\n- **Pre-indexed Bundles:** Load famous repositories instantly with .cgc bundles - no indexing required! (Learn more)\n- **Live File Watching:** Watch directories for changes and automatically update the graph in real-time (cgc watch).\n- **Interactive Setup:** A user-friendly command-line wizard for easy setup.\n- **Dual Mode:** Works as a standalone **CLI toolkit** for developers and as an **MCP server** for AI agents.\n- **Multi-Language Support:** Full support for 23 programming languages.\n- **Flexible Database Backend:** FalkorDB Lite (Default), KuzuDB, LadybugDB, FalkorDB Remote, Nornic DB, or Neo4j (all platforms via Docker/native).\n\n---\n\n🏗️ Architecture & Workflow\n\nCodeGraphContext parses your source code and builds a comprehensive knowledge graph. This graph can be queried directly via the CLI toolkit or exposed to AI assistants through the MCP server.\n\n \n\n---\n\nSupported Programming Languages\n\nCodeGraphContext provides comprehensive parsing and analysis for the following languages:\n\n| | Language | | Language | | Language |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 🐍 | **Python** | 📜 | **JavaScript** | 🔷 | **TypeScript** |\n| ☕ | **Java** | 🔵 | **C** | ➕ | **C++** |\n| #️⃣ | **C#** | 🐹 | **Go** | 🦀 | **Rust** |\n| 💎 | **Ruby** | 🐘 | **PHP** | 🍎 | **Swift** |\n| 🎨 | **Kotlin** | 🎯 | **Dart** | 🐪 | **Perl** |\n| 🌙 | **Lua** | 🚀 | **Scala** | λ | **Haskell** |\n| 💧 | **Elixir** | 📜 | **Emacs Lisp (elisp)** | 🌐 | **HTML** |\n| 🎨 | **CSS** | ⚛️ | **TSX** | ⛓️ | **Solidity** |\n| 🏗️ | **HCL (Terraform/Terragrunt)** | | | | |\n\nEach language parser extracts functions, classes, methods, parameters, inheritance relationships, function calls, and imports to build a comprehensive code graph.\n\n**HCL notes:** .tf, .tfvars and .hcl cover Terraform, OpenTofu and Terragrunt. HCL declares\nrather than calls, so it contributes blocks, variables and cross-configuration imports but no\nfunction calls. See docs/docs/contributing_languages.md § HCL.\n\n**Solidity notes:** .sol uses Tree-sitter via tree-sitter-language-pack (no SCIP). Supports Foundry remappings, modifier invocations, using Lib for T, and emit / custom-error revert as CALLS. See docs/docs/contributing_languages.md § Solidity.\n\n**Kotlin notes:** Annotations are recorded on functions and classes as decorators, retaining\ntheir arguments (@Preview(showBackground = true)). This makes find_dead_code\'s\nexclude_decorated_with usable on Kotlin and Android codebases — for example excluding\nComposable, Preview, Test and Provides. That filter matches on functions, so class-level\nannotations do not affect it. Annotations on interfaces, objects, constructors, parameters and\nproperties are not yet recorded.\n\n---\n\nDatabase Options\n\nCodeGraphContext supports multiple graph database backends to suit your environment:\n\n| Feature | KuzuDB | LadybugDB | FalkorDB Lite | Neo4j / Nornic DB |\n| :--- | :--- | :--- | :--- | :--- |\n| **Typical default** | Cross-platform fallback when FalkorDB Lite is unavailable | Optional embedded backend | **Default on Unix** (Python 3.12+, when falkordblite is installed) | When explicitly configured via cgc config db |\n| **Setup** | Zero-config / Embedded | Zero-config / Embedded | Zero-config / In-process | Docker / External |\n| **Platform** | **All (Windows Native, macOS, Linux)** | **All (Windows Native, macOS, Linux)** | Unix-only (Linux/macOS/WSL) | All Platforms |\n| **Use Case** | Desktop, IDE, Local development | Custom research projects | Specialized Unix development | Enterprise, Massive graphs |\n| **Requirement**| pip install kuzu | pip install ladybug | pip install falkordblite | Neo4j Server / Docker / Nornic Cloud |\n| **Speed** | ⚡ Extremely Fast | ⚡ Fast | 🚀 Scalable | 🌐 Network-dependent |\n| **Persistence**| Yes (to disk) | Yes (to disk) | Yes (to disk) | Yes (server-side) |\n\nEmbedded KuzuDB/LadybugDB default to a **4 GiB** buffer pool (CGC_EMBEDDED_BUFFER_POOL_MB). Set that env var to another MiB value, or 0 for the library default (~80% of system RAM).\n\n---\n\nSCIP indexing (optional)\n\nWhen SCIP_INDEXER=true in your CGC config (~/.codegraphcontext/.env), some languages use external **SCIP** indexers for more accurate calls and inheritance than Tree-sitter heuristics alone.\n\n**C and C++** use **scip-clang**, which requires a **compile_commands.json** file (a JSON compilation database): one entry per translation unit with the real compiler command (include paths, -D defines, -std, etc.). Without it, scip-clang cannot run; CGC logs a warning and **falls back to Tree-sitter** for that repo. Typical ways to produce the file: **CMake** with -DCMAKE_EXPORT_COMPILE_COMMANDS=ON, or wrap your real', 'readme_raw_len': 18076, 'one_line_zh': '将本地代码索引进图数据库，为 AI 助手提供上下文，并配有图形化代码浏览', 'one_line_plain': 'Index local code into a graph database to give AI assistants context, with graphical code…'}, {'id': 5867, 'key': 'pipeshub-ai-pipeshub-ai', 'kind': 'repo', 'name': 'pipeshub-ai/pipeshub-ai', 'one_line': 'The open-source context layer for AI agents', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '["开源项目"]', 'desc_raw': "The open-source context layer for AI agents. PipesHub turns your company's knowledge (Slack, Drive, Jira, GitHub, Microsoft 365 and 40+ connectors) into a permission-aware workspace that agents can search, grep, navigate and cite. MCP, SDKs and built-in agents. Self-hosted.", 'url': 'https://github.com/pipeshub-ai/pipeshub-ai', 'source': 'github', 'source_url': 'https://github.com/pipeshub-ai/pipeshub-ai', 'downloads': None, 'stars': 3840, 'installs': None, 'mentions': 2, 'score': 210.67, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-14', 'last_seen': '2026-10-19', 'times_seen': 7, 'icon': 'https://github.com/pipeshub-ai.png?size=64', 'official_desc': "The open-source context layer for AI agents. PipesHub turns your company's knowledge (Slack, Drive, Jira, GitHub, Microsoft 365 and 40+ connectors) into a permission-aware workspace that agents can search, grep, navigate and cite. MCP, SDKs and built-in agents. Self-hosted.", 'stars_own': 3840, 'stars_creator': None, 'stars_own_repo': 'pipeshub-ai/pipeshub-ai', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'The Open-Source Workplace AI Platform \n\n \n Website ·\n Docs ·\n Discord ·\n Roadmap \n \n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n**Translations:** Français · Deutsch · 简体中文 · 日本語 · Русский · עברית · 한국어 · Español · Português · Türkçe · Tiếng Việt · Italiano\n\n \n\n PipesHub - Explainable & Extensible \n\n PipesHub is an open-source platform for connecting AI applications to the knowledge stored across your company\'s business systems.\n\nConnect Slack, Google Drive, GitHub, Microsoft 365, Notion and 50+ other systems. Search across them and get answers with citations, filtered by what each person is allowed to see — or give that same context to your own agents, workflows and MCP clients.\n\n> [!TIP]\n> Deploy with a single command:\n> \n\n \n \n \n ▶ Watch the 2½-minute demo : Claude Code answering from Jira, Slack, GitHub and Drive, with each person\'s permissions\n \n\nWhat can you build with PipesHub?\n\nDon\'t just use PipesHub — build on it.\n\n**Give Claude Code, Cursor, or Codex secure access to your company\'s knowledge →**\nAbout ten minutes once PipesHub is running with data indexed. Mint a Personal Access Token (no admin needed), connect your assistant (one command for Claude Code, one config file for Cursor or Codex), and ask *"why was the retry logic in the billing worker changed?"* — it answers from the incident postmortem, the pull request, the chat thread, and the design doc, each cited, and only if you\'re allowed to see them.\n\nWant the same retrieval inside your own code, or behind a search box for your team? The SDK starter and search example cover both. Built something? Show us.\n\nPipesHub in Action\n\nConnectors\n \n\nCitations\n \n\nAll Records\n \n\nKnowledge Search\n \n\nFeatures\n\n**Answers you can trust**\n\n- 📝 **Explainable Answers:** PipesHub delivers grounded answers with precise block citations to the original documents.\n- 🔒 **Permission-Aware Search:** Enforces source-level access controls so users only see what they\'re authorized to.\n- 🧠 **Bring Your Own Model, Fully Self-Hostable:** Any LLM provider, deployed in your VPC — data never leaves your infrastructure.\n\n**Connected to your systems**\n\n- 🔌 **Enterprise Connectors:** 50+ connectors with real-time and scheduled indexing out of the box.\n- 🕸️ **Knowledge Graph Retrieval:** Graph-backed retrieval that captures relationships across enterprise data.\n- 🎙️ **Multimodal Support:** Image, diagram, and scanned-file understanding plus voice-based interaction.\n\n**What you can build**\n\n- 🔍 **Unified Search, Deep Research, and Agents:** Search, Q&A, deep research, web search, and AI agents on one context layer.\n- 🤖 **No-Code Agents and Actions:** Build agents visually and execute actions across enterprise tools.\n- 📊 **Artifacts and Code Execution:** Generate reports, charts, and dashboards in a safe execution sandbox.\n- 🛠️ **Developer-First and Extensible:** APIs, SDKs, MCP tools, custom connectors, and independently scalable services.\n\nPipesHub Cloud\n\nPrefer a fully managed PipesHub without running your own infrastructure? PipesHub Cloud is coming soon.\n\n👉 **Join the Cloud Waitlist** to get early access.\n\nConnectors\n\n \n \n \n\n🚀 Deployment Guide\n\nPipesHub can be run locally or deployed on any server using Docker Compose. The interactive installer handles all configuration — including secrets, graph DB, broker, and image tag selection — and generates a .env for you.\n\n> **HTTPS on cloud servers:** If you deploy PipesHub on a cloud server, use an HTTPS endpoint. Browsers block certain requests over plain HTTP. Use Cloudflare, Nginx, or Traefik to terminate TLS. A white screen after HTTP-only deployment is typically caused by this restriction.\n\n---\n\n⚡ Quickstart (Recommended)\n\nRequires Docker with Compose v2. One command:\n\n \n\nThis downloads the deployment files for the latest release into ./pipeshub and\nlaunches the interactive installer. Open **http://localhost:3000** once it\nfinishes.\n\n> **Prefer to read before running?** Download and inspect the script first:\n>\n> \n\nThe installer will:\n- Check Docker, RAM, and disk prerequisites\n- Ask whether you want a **slim** or **full** deployment\n- Let you optionally customise the graph DB, message broker, and KV store\n- Generate randomised secrets and write a .env file\n- Pull images and start the stack\n- Wait for PipesHub to become healthy, verify it is reachable, and print the URL\n\n🛠️ From a cloned repository (developers)\n\nTo build from source, contribute, or pin the installer to your checkout:\n\n \n\nBuilding local images from source requires this cloned-repo path (./install.sh --build);\nthe one-command installer above always uses prebuilt images.\n\nInstaller options\n\n| Flag | Description |\n|------|-------------|\n| -y / --yes | Accept all defaults; skip interactive prompts (CI-friendly) |\n| --version TAG | Pin a specific image tag, e.g. --version 0.7.0 |\n| --reconfigure | Re-run the wizard and overwrite an existing .env |\n| --print-env-only | Write .env and print the compose command without starting containers |\n\n> **Advanced options:** CI environment variables, slim vs. full deployment types, manual Compose profile usage, and local source builds are covered in Advanced Deployment Options.\n\nBuild with PipesHub\n\nThe built-in search experience is one way to use PipesHub. The same connected,\npermission-filtered context is available to your own agents and applications —\nover MCP for any compatible client, or through the SDKs when you are calling it\nfrom your own code.\n\nAn agent connects as a specific person rather than as the application, so it\nretrieves exactly what that person is allowed to see. Access is resolved when\nthe query runs, against the source system\'s own permissions, instead of being\napproximated at build time.\n\nStep-by-step tutorials for the most common builds — an MCP for your coding\nassistant, private enterprise search, and SDK starters — live in\n**pipeshub-ai/examples**. The\nreference material for each building block is below.\n\nMCP Server\n\nUse PipesHub with any MCP-compatible client to bring your enterprise context into AI workflows. Check the README for setup and usage.\n\n**Repository:** pipeshub-ai/mcp-server\n\nConnecting an Omnigent agent\n\nFirst, mint a long-lived credential: **workspace → Developer settings →\nPersonal Access Tokens → New token**. Pick an expiry (30/90/365 days, or\nnever) — this runs as *you*, so results respect your own per-user\npermissions, unlike an OAuth app\'s client-credentials flow.\n\nThree ways to connect, from least to most setup:\n\n1. **Attach in the Omnigent web UI (fastest, no clone).** Open a session\'s\n info panel → Manage MCP Servers → add PipesHub\'s URL and an\n Authorization: Bearer header → restart the session.\n2. **Run the packaged example agent.** A ready-made agent bundle (tuned\n prompt + instructions) ships with Omnigent:\n PIPESHUB_MCP_URL=... PIPESHUB_MCP_TOKEN=... omnigent run examples/pipeshub/.\n3. **Use the connect kit** in integrations/omnigent/\n (setup.sh / run.sh) for CI, service accounts, or password/OAuth\n client-credentials auth instead of a personal token.\n\nSDKs\n\nPipesHub provides developer SDKs for Python, TypeScript, and Go to help you integrate quickly. Check the respective SDK repository README for setup and usage details.\n\n| Name | Description | Link |\n|------|-------------|------|\n| **Python SDK** | Python SDK for PipesHub | pipeshub-ai/pipeshub-sdk-python |\n| **TypeScript SDK** | TypeScript SDK for PipesHub | pipeshub-ai/pipeshub-sdk-typescript |\n| **Go SDK** | Go SDK for PipesHub | pipeshub-ai/pipeshub-sdk-go |\n\n> Need an SDK in another language? Reach out to us at developer@pipeshub.com\n\nRoadMap\n\n We ship in the open. Here\'s what\'s done and what\'s next: \n\n \n ✅ 🤖 Workplace AI agents : first-class no-code agent builder \n ✅ 🔗 MCP (Model Context Protocol) support, both server and client \n ✅ 🧰 Developers SDKs \n ✅ 🔍 Code search across GitHub and GitLab \n ⬜ 👤 Personalized search based on team, role, and history \n ✅ ☸️ Production Kubernetes deployment with HA defaults \n ⬜ 📈 PageRank-augmented relevance across the knowl', 'readme_raw_len': 12957, 'one_line_zh': '面向 AI Agent 的开源上下文层', 'one_line_plain': 'This open-source context layer helps AI agents manage information'}, {'id': 1803, 'key': 'zcaceres-markdownify-mcp', 'kind': 'mcp', 'name': 'zcaceres/markdownify-mcp', 'one_line': '📇 🏠 - Convert almost any file or web content into Markdown', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol server for converting almost anything to Markdown', 'url': 'https://github.com/zcaceres/markdownify-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/zcaceres/markdownify-mcp', 'downloads': None, 'stars': 2997, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/zcaceres.png?size=64', 'official_desc': '📇 🏠 - Convert almost any file or web content into Markdown.', 'stars_own': 2997, 'stars_creator': None, 'stars_own_repo': 'zcaceres/markdownify-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Markdownify MCP Server\n\n \n\nMarkdownify is a Model Context Protocol (MCP) server that converts various file types and web content to Markdown format. It provides a set of tools to transform PDFs, images, audio files, web pages, and more into easily readable and shareable Markdown text.\n\n \n\nFeatures\n\n- Convert multiple file types to Markdown:\n - PDF\n - Images\n - Audio (with transcription)\n - DOCX\n - XLSX\n - PPTX\n- Convert web content to Markdown:\n - YouTube video transcripts\n - Bing search results\n - General web pages\n- Retrieve existing Markdown files\n\nGetting Started\n\n1. Clone this repository\n2. Install dependencies:\n \n\n The preinstall step creates a Python virtual environment at .venv and installs markitdown[all].\n\n3. Build the project:\n \n4. Start the server:\n \n\nDevelopment\n\n- Use bun run dev to start the TypeScript compiler in watch mode\n- Modify src/server.ts to customize server behavior\n- Add or modify tools in src/tools.ts\n\nUsage with Desktop App\n\nTo integrate this server with a desktop app, add the following to your app\'s server configuration:\n\n \n\nEnvironment variables\n\nAll paths default to sensible values; override only when the defaults don\'t fit your install layout.\n\n| Variable | Default | Purpose |\n|---|---|---|\n| MARKITDOWN_PATH | /.venv/bin/markitdown, then markitdown on PATH | Absolute path to the markitdown executable. Set this when you\'ve installed markitdown system-wide (e.g. pipx install "markitdown[pdf]") instead of using the bundled venv. |\n| REPOMIX_PATH | /node_modules/.bin/repomix, then repomix on PATH | Absolute path to the repomix executable used by git-repo-to-markdown. |\n| MD_ALLOWED_PATHS | unset (unrestricted) | Path-delimiter-separated list (: on POSIX, ; on Windows) of directories the server is allowed to read. When set, all file-input tools (pdf-to-markdown, get-markdown-file, etc.) reject paths outside these directories. |\n| MD_SHARE_DIR | unset | Deprecated alias for MD_ALLOWED_PATHS (single directory). Still honored for backward compatibility. |\n\nUsage with Docker\n\nBuild and run:\n \n\nNotes for the Docker MCP catalog (mcp/markdownify):\n- Mount any host directories you want the server to read into the container, then pass the **container** paths to the tools (e.g. /data/foo.pdf, not /Users/you/Documents/foo.pdf).\n- Set MD_ALLOWED_PATHS to the colon-separated list of mounted directories so the server enforces a read boundary that matches the bind mount.\n- The published Docker image installs markitdown[pdf] only — audio transcription and image OCR (audio-to-markdown, image-to-markdown) require the [all] extras and will fail in the slim image. Use the local install (bun install) for the full feature set.\n\nAvailable Tools\n\n- youtube-to-markdown: Convert YouTube videos to Markdown\n- pdf-to-markdown: Convert PDF files to Markdown\n- bing-search-to-markdown: Convert Bing search results to Markdown\n- webpage-to-markdown: Convert web pages to Markdown\n- image-to-markdown: Convert images to Markdown with metadata\n- audio-to-markdown: Convert audio files to Markdown with transcription\n- docx-to-markdown: Convert DOCX files to Markdown\n- xlsx-to-markdown: Convert XLSX files to Markdown\n- pptx-to-markdown: Convert PPTX files to Markdown\n- get-markdown-file: Retrieve an existing Markdown file. File extension must end with: *.md, *.markdown.\n\n OPTIONAL: set MD_ALLOWED_PATHS to restrict every file-input tool to a list of directories, e.g. MD_ALLOWED_PATHS=/data/in:/data/out bun start.\n\nContributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\nLicense\n\nThis project is licensed under the MIT License - see the LICENSE file for details.', 'readme_raw_len': 3639, 'one_line_zh': '将几乎所有文件或网页内容转换为 Markdown', 'one_line_plain': 'Convert almost any file or web content into Markdown'}, {'id': 2551, 'key': 'bitbonsai-mcp-obsidian', 'kind': 'mcp', 'name': 'bitbonsai/mcp-obsidian', 'one_line': '📇 🏠 🍎 🪟 🐧 - Safe read/write access to Obsidian vaults: search, batch operations, tag management and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/bitbonsai/mcpvault', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/bitbonsai/mcp-obsidian', 'downloads': None, 'stars': 1695, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/bitbonsai.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Safe read/write access to Obsidian vaults: search, batch operations, tag management and frontmatter handling.', 'stars_own': 1695, 'stars_creator': None, 'stars_own_repo': 'bitbonsai/mcpvault', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'MCPVault\n\n \n\nA local MCP server that lets compatible clients read, search, and edit notes in an Obsidian vault. MCPVault works directly with vault files, restricts file operations to the configured vault root, and preserves formatting for unchanged frontmatter fields.\n\n \n \nhttps://mcpvault.org\n\nChangelog\n\n \n\n \n\n \n\n \n\nSupported clients\n\nConfiguration examples are available for Claude Desktop, Claude Code, ChatGPT Desktop (Enterprise+), OpenCode, Gemini CLI, OpenAI Codex, IntelliJ IDEA 2025.1+, Cursor, Windsurf, and Ontheia. Other clients can use MCPVault if they support local stdio MCP servers.\n\nhttps://github.com/user-attachments/assets/657ac4c6-1cd2-4cc3-829f-fd095a32f71c\n\nQuick start\n\n1. **Install Node.js runtime:**\n\n \n\n2. **Test the server:**\n\n If using the published package:\n\n \n\n3. **Configure your AI client:**\n\n **Claude Desktop** - Copy this to claude_desktop_config.json:\n\n \n\n **Claude Code** - Copy this to ~/.claude.json:\n\n \n\n **OpenCode** - Copy this to ~/.config/opencode/opencode.json\n\n \n\n Replace /path/to/your/vault with your actual Obsidian vault path.\n\n For other platforms, see detailed configuration guides below.\n\n4. **Test with your AI:**\n - "List files in my Obsidian vault"\n - "Read my note called \'project-ideas.md\'"\n - "Create a new note with today\'s date"\n\nTo verify the connection, ask your client to list MCPVault tools or read a known note.\n\nHow it connects\n\nAn MCP client starts MCPVault as a local stdio process and passes the vault path. MCPVault exposes the same tools to each supported client, so the server is not tied to one AI provider. Obsidian does not need to be running, and no Obsidian plugin is required.\n\nFeatures\n\n- AST-aware frontmatter updates preserve formatting for unchanged YAML fields.\n- Path checks block traversal, symlink escapes, dotfiles, .obsidian, .git, and node_modules.\n- Eighteen MCP tools cover note and file operations:\n - File operations: read_note, write_note, patch_note, delete_note, move_note, move_file\n - Partial reads: get_note_outline, read_note_lines\n - Directory and batch reads: list_directory, read_multiple_notes\n - Search: search_notes with multi-word matching and BM25 reranking\n - Metadata and tags: get_frontmatter, update_frontmatter, get_notes_info, get_vault_stats, manage_tags, list_all_tags\n - Wiki links: wiki_link resolves names and returns alternative paths when a name is ambiguous\n- write_note supports overwrite, append, and prepend modes.\n- delete_note and move_file require matching confirmation paths.\n- Path arguments are trimmed before validation.\n- Search and batch tools return compact fields by default; set prettyPrint: true for expanded output.\n- The package exports TypeScript declarations and public types.\n- MCPVault requires no Obsidian plugin.\n\nPrerequisites\n\n- Node.js runtime (v20.0.0 or later)\n- An Obsidian vault (local directory with .md, .markdown, .txt, .base, or .canvas files)\n- MCP-compatible AI client (Claude Desktop, ChatGPT Desktop, Claude Code, etc.)\n\nInstallation\n\nFor end users\n\nnpx downloads and runs the package:\n\n \n\nIf you omit the vault path, the server uses your current working directory as the vault root.\n\nFor developers\n\n1. Clone this repository\n2. Use the correct Node.js version:\n\n \n\n3. Install dependencies with npm:\n\n \n\n4. Test locally with MCP inspector:\n\n \n\nUse MCP Inspector to test the server before adding it to a client:\n\n \n\nUsage\n\nRunning the Server\n\n**End users:**\n\n \n\n**Developers:**\n\n \n\nAI Client Configuration\n\nClaude Desktop\n\nAdd to your Claude Desktop configuration file:\n\n**Single Vault:**\n\n \n\n**Multiple Vaults:**\n\n \n\n**Read-only mode:**\n\nAdd --read-only after the vault path to expose only read tools. Mutating tools are omitted from discovery and rejected if called directly.\n\n \n\nThe CLI also accepts --read-only true and --read-only=true for configuration systems that require explicit values. Omit the option, or set it to false, to keep normal read/write access.\n\n**Configuration File Locations:**\n\n- **macOS:** ~/Library/Application Support/Claude/claude_desktop_config.json\n- **Windows:** C:\\Users\\{username}\\AppData\\Roaming\\Claude\\claude_desktop_config.json\n- **Linux:** ~/.config/Claude/claude_desktop_config.json\n\n_You can also access this through Claude Desktop → Settings → Developer → Edit Config_\n\nChatGPT Desktop\n\n**Requirements:** ChatGPT Enterprise, Education, or Team subscription (not available for individual Plus users)\n\nChatGPT uses MCP through Deep Research and developer mode. Configuration is done through the ChatGPT interface:\n\n1. Access ChatGPT developer mode (beta feature)\n2. Configure MCP servers through the built-in MCP client\n3. Create custom connectors for your organization\n\n_Note: ChatGPT Desktop\'s MCP integration is currently limited to enterprise subscriptions and uses a different setup process than file-based configuration._\n\nClaude Code\n\nClaude Code uses .claude.json configuration file:\n\n**User-scoped (recommended):** Edit ~/.claude.json:\n\n \n\n**Project-scoped:** Edit .claude.json in your project or add to the projects section:\n\n \n\n**Using Claude Code CLI:**\n\n \n\nGoose Desktop\n\nOn Goose Desktop settings, click **Add custom extension**, and on the command field add:\n\n \n\nOther MCP-Compatible Clients (2025)\n\n**Confirmed MCP Support:**\n\n- **IntelliJ IDEA 2025.1+** - Native MCP client support\n- **Cursor IDE** - Built-in MCP compatibility\n- **Windsurf IDE** - Full MCP integration\n- **Ontheia** - Self-hosted, open-source AI agent platform\n- **Zed, Replit, Codeium, Sourcegraph** - In development\n- **Microsoft Copilot Studio** - Native MCP support with one-click server connections\n\nMost modern MCP clients use similar JSON configuration patterns. Refer to your specific client\'s documentation for exact setup instructions.\n\nExamples\n\nAsk your AI assistant about your notes:\n\n- "What files are in my Obsidian vault?"\n- "Read my note called \'project-ideas.md\'"\n- "Show me all notes with \'AI\' in the title"\n\nHave your AI assistant help with note management:\n\n- "Create a new note called \'meeting-notes.md\' with today\'s date in the frontmatter"\n- "Append today\'s journal entry to my daily note"\n- "Prepend an urgent task to my todo list"\n- "Add the tags \'project\' and \'urgent\' to my task note"\n- "List all tags in my research note"\n- "Remove the \'draft\' tag from my completed article"\n- "List all markdown files in my \'Projects\' folder"\n- "Delete the old draft note \'draft-ideas.md\' (with confirmation)"\n\nExample workflows\n\n- "Summarize my research notes tagged with \'machine-learning\' from the last month"\n- "Update the status in my project notes to \'completed\' and add today\'s date"\n- "Find notes that mention \'API design\' and draft a guide from them"\n- "Review my untagged notes and suggest tags based on their content"\n\nTroubleshooting\n\nCommon Issues\n\n"command not found: npx"\n\n- **Solution:** Install Node.js runtime from nodejs.org\n- **Alternative:** Use global install: npm install -g @bitbonsai/mcpvault\n\n"File not found" when paths look correct\n\n- **Cause:** The server is using the wrong vault root\n- **Solution:** Either run the command from your vault directory or pass the vault path explicitly\n\n"Permission denied" errors\n\n- **Cause:** Insufficient file system permissions\n- **Solution:** Ensure the vault directory is readable/writable by your user\n\n"Path traversal not allowed"\n\n- **Cause:** Trying to access files outside the vault\n- **Solution:** All file paths must be relative to the vault root\n\nAI client not recognizing the server\n\n1. Check the configuration file path is correct for your OS\n2. Ensure JSON syntax is valid (use a JSON validator)\n3. Restart your AI client after configuration changes\n4. Check your AI client\'s logs for error messages\n5. Verify your AI client supports MCP (Model Context Protocol)\n\n".obsidian files still showing up"\n\n- **Expected:** The path filter automatically excludes .obsidian/** patterns\n- **If still seeing them:** The filter is working as designed for security\n\nDebug Mode\n\nRun with error logging:\n\n \n\nGetting Help\n\n- Open an iss', 'readme_raw_len': 13696, 'one_line_zh': '安全读写 Obsidian 库：检索、批量操作与标签管理等。', 'one_line_plain': 'Safe read/write access to Obsidian vaults: search, batch operations, and tag management'}, {'id': 756, 'key': 'ezyang-codemcp', 'kind': 'mcp', 'name': 'ezyang/codemcp', 'one_line': '🐍 🏠 - Coding agent with basic read, write and command line tools', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Coding assistant MCP for Claude Desktop', 'url': 'https://github.com/ezyang/codemcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ezyang/codemcp', 'downloads': None, 'stars': 1606, 'installs': None, 'mentions': 22, 'score': 2203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ezyang.png?size=64', 'official_desc': '🐍 🏠 - Coding agent with basic read, write and command line tools.', 'stars_own': 1606, 'stars_creator': None, 'stars_own_repo': 'ezyang/codemcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'NOTICE: claude code is available with Anthropic\'s $20/mo subscription, so I consider\ncodemcp fully obsolete. However, there are some good design ideas (especially around\nthe Git-versioning scheme) that I eventually want to port into the current generation\nof agentic coding clis.\n\ncodemcp\n\nMake Claude Desktop a pair programming assistant by installing codemcp. With\nit, you can directly ask Claude to implement features, fix bugs and do\nrefactors on a codebase on your computer; Claude will directly edit files and\nrun tests. Say goodbye to copying code in and out of Claude\'s chat window!\n\n \n\ncodemcp offers similar functionality to other AI coding software (Claude Code,\nCursor, Cline, Aider), but it occupies a unique point in the design space:\n\n1. It\'s intended to be used with **Claude Pro**, Anthropic\'s $20/mo\n subscription offering. I like paying for my usage with a subscription plan\n because it means **zero marginal cost** for agent actions; no more feeling\n bad that you wasted five bucks on a changeset that doesn\'t work.\n\n Note that if you have Claude Max ($100/mo), Claude Code can also be used\n with subscription based pricing. The value proposition for codemcp is\n murkier in this case (and it is definitely inferior to Claude Code in some\n respects), but you can still use codemcp with Claude Max if you prefer some\n of the other UI decisions it makes. (Also, it\'s open source, so you can\n change it if you don\'t like it, unlike Claude Code!)\n\n2. It\'s built around **auto-accept by default**. I want my agent to get as\n far as it can without my supervision, so I can review everything in one go at\n the end. There are two key things that codemcp does differently than most\n coding agents: we **forbid unrestricted shell**, instead requiring you to\n predeclare commands the agent can use in codemcp.toml, and we **Git\n version all LLM edits**, so you can roll back agent changes on a\n fine-grained basis and don\'t have to worry about forgetting to commit\n changes.\n\n3. It\'s **IDE agnostic**: you ask Claude to make changes, it makes them, and\n then you can use your favorite IDE setup to review the changes and make\n further edits. I use vim as my daily driver editor, and coding environments\n that require VSCode or a specific editor are a turn off for me.\n\nIMPORTANT: For master users - Major changes for token efficiency\n\nTo improve codemcp\'s token efficiency, on master I am in the process of\nchanging codemcp back into a multi-tool tool (instead of a single tool whose\ninstructions are blatted into chat when you InitProject). This means you have\nto manually approve tool use. Because tool use approval is persistent across\nmultiple chats, I think this is a reasonable tradeoff to make, but if you\nreally don\'t like, file a bug at\nrefined-claude browser\nextension for supporting auto-approve tool use.\n\nInstallation\n\nI recommend this specific way of installing and using codemcp:\n\n1. Install uv and install git, if they are not installed already.\n\n2. Install claude-mcp on your browser.\n This enables you to connect to SSE MCP servers directly from the website,\n which means you don\'t need to use Claude Desktop and can easily have\n multiple chat windows going in parallel. We expect this extension should\n be soon obsoleted by the rollout of\n Integrations. At time of\n writing, however, Integrations have not yet arrived for Claude Pro subscribers.\n\n3. Run codemcp using uvx --from git+https://github.com/ezyang/codemcp@prod codemcp serve.\n You can add --port 1234 if you need it to listen on a non-standard port.\n\n Pro tip: if you like to live dangerously, you can change prod to main. If\n you want to pin to a specific release, replace it with 0.3.0 or similar.\n\n Pro tip: you can run codemcp remotely! If you use\n Tailscale and trust all devices on your Tailnet,\n you can do this securely by passing --host 100.101.102.103 (replace the\n IP with the Tailscale IP address of your node. This IP typically lives in\n the 100.64.0.0/10 range.) **WARNING:** Anyone with access to this MCP can perform\n arbitrary code execution on your computer, it is **EXTREMELY** unlikely you want to\n bind to 0.0.0.0.\n\n4. Configure claude-mcp with URL: http://127.0.0.1:8000/sse (replace the port if needed.)\n\n5. Unfortunately, the web UI inconsistently displays the hammer icon. However, you can verify\n that the MCP server is working by looking for "[MCP codemcp] SSE connection opened" in the\n Console, or by asking Claude what tools it has available (it should say\n tools from codemcp are available.)\n\nIf you prefer to use Claude Desktop or have unusual needs, check out INSTALL.md for\ninstallation instructions for a variety of non-standard situations.\n\nUsage\n\nFirst, you must create a codemcp.toml file in the Git repository checkout\nyou want to work on. If you want the agent to be able to do things like run\nyour formatter or run tests, add the commands to execute them in the commands\nsection (note: these commands need to appropriately setup any virtual\nenvironment they need):\n\n \n\nThe format command is special; it is always run after every file edit.\n\nNext, in Claude Desktop, we recommend creating a Project and putting this in\nthe Project Instructions:\n\n \n\nWhere $PROJECT_DIR is the path to the project you want to work on.\n\nThen chat with Claude about what changes you want to make to the project.\nEvery time codemcp makes a change to your code, it will generate a commit.\n\nTo see some sample transcripts using this tool, check out:\n\n- Implement a new feature\n- Fix failing tests\n- Do a refactor\n\ncodemcp will generate a commit per chat and amend it as it is working on your feature.\n\nPhilosophy\n\n- When you get rate limited, take the time to do something else (review\n Claude\'s code, review someone else\'s code, make plans, do some meetings)\n\n- This is *not* an autonomous agent. At minimum, you have to intervene after\n every chat to review the changes and request the next change. While you\n *can* ask for a long list of things to be done in a single chat, you will\n likely hit Claude Desktop\'s output limit and have to manually "continue" the\n agent anyway. Embrace it, and use the interruptions to make sure Claude is\n doing the right thing.\n\n- When Claude goes off the rails, it costs you time rather than dollars.\n Behave accordingly: if time is the bottleneck, watch Claude\'s incremental\n output carefully.\n\nConfiguration\n\nHere are all the config options supported by codemcp.toml:\n\n \n\nThe project_prompt will be loaded when you initialize the project in chats.\n\nThe commands section allows you to configure commands for specific tools. The\nnames are told to the LLM, who will decide when it wants to run them. You can add\ninstructions how to use tools in the project_prompt; we also support a more verbose\nsyntax where you can give specific instructions on a tool-by-tool basis:\n\n \n\nTroubleshooting\n\nTo run the server with inspector, use:\n\n \n\nLogs are written to ~/.codemcp/codemcp.log. The log level can be set in a global configuration file at ~/.codemcprc:\n\n \n\nLogging is not configurable on a per project basis, but this shouldn\'t matter\nmuch because it\'s difficult to use Claude Desktop in parallel on multiple\nprojects anyway.\n\nContributing\n\nSee CONTRIBUTING.md.', 'readme_raw_len': 7198, 'one_line_zh': '具备基础读写与命令行工具的编码 Agent', 'one_line_plain': 'Write, read, and run basic command-line tools as a coding agent'}, {'id': 3458, 'key': 'nickclyde-duckduckgo-mcp-server', 'kind': 'mcp', 'name': 'nickclyde/duckduckgo-mcp-server', 'one_line': '🐍 ☁️ - Web search using DuckDuckGo, with content fetching and parsing', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol (MCP) server that provides web search capabilities through DuckDuckGo, with additional features for content fetching and parsing.', 'url': 'https://github.com/nickclyde/duckduckgo-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/nickclyde/duckduckgo-mcp-server', 'downloads': None, 'stars': 1546, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/nickclyde.png?size=64', 'official_desc': '🐍 ☁️ - Web search using DuckDuckGo, with content fetching and parsing.', 'stars_own': 1546, 'stars_creator': None, 'stars_own_repo': 'nickclyde/duckduckgo-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'DuckDuckGo Search MCP Server\n\n \n \n \n\nA Model Context Protocol (MCP) server that provides web search capabilities through DuckDuckGo, with additional features for content fetching and parsing.\n\nQuick Start\n\n \n\nFeatures\n\n- **Web Search**: Search DuckDuckGo with advanced rate limiting and result formatting\n- **Content Fetching**: Retrieve and parse webpage content with intelligent text extraction\n- **Long URL Shortening**: Over-long result URLs become short ref:// tokens that fetch_content accepts directly, saving context\n- **Rate Limiting**: Built-in protection against rate limits for both search and content fetching\n- **Error Handling**: Comprehensive error handling and logging\n- **LLM-Friendly Output**: Results formatted specifically for large language model consumption\n\nInstallation\n\nInstall from PyPI using uv:\n\n \n\nUsage\n\nRunning with Claude Desktop\n\n1. Download Claude Desktop\n2. Create or edit your Claude Desktop configuration:\n - On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n - On Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n\nAdd the following configuration:\n\n**Basic Configuration (No SafeSearch, No Default Region):**\n \n\n**With SafeSearch and Region Configuration:**\n \n\n**Configuration Options:**\n- DDG_SAFE_SEARCH: SafeSearch filtering level (optional)\n - STRICT: Maximum content filtering (kp=1)\n - MODERATE: Balanced filtering (kp=-1, default if not specified)\n - OFF: No content filtering (kp=-2)\n- DDG_REGION: Default region/language code (optional, examples below)\n - us-en: United States (English)\n - cn-zh: China (Chinese)\n - jp-ja: Japan (Japanese)\n - wt-wt: No specific region\n - Leave empty for DuckDuckGo\'s default behavior\n- DDG_CA_CERTS: Path to a PEM CA bundle used to verify TLS certificates on outbound requests (optional). Needed behind TLS-intercepting proxies — see Running behind a TLS-intercepting proxy.\n- DDG_RATE_LIMIT_STRATEGY: sliding (default, historical 60s window) or token_bucket (burst, then smooth).\n- DDG_SEARCH_RPM: Search requests per minute (default: 30).\n- DDG_FETCH_RPM: Global fetch_content requests per minute (default: 20).\n- DDG_FETCH_HOST_RPM: Optional per-host fetch cap (default: 0, off). Set a positive number to enable.\n- DDG_CACHE_TTL: Seconds to keep a parsed page in the in-memory fetch_content cache (default: 300). Paginated reads of the same URL reuse one download. Set 0 to disable.\n- DDG_CACHE_MAX_ENTRIES: Maximum pages kept in that cache (default: 64). Least-recently-used eviction. Set 0 to disable.\n- DDG_PARSE_MODE: Default fetch_content extractor (text, main, or markdown). Default is text (historical flattened page). Per-call parse_mode overrides this.\n- DDG_REF_URL_THRESHOLD: Search-result URLs longer than this many characters are replaced with short ref:// tokens (default: 120). Set 0 to always show full URLs. Also --ref-url-threshold.\n\n3. Restart Claude Desktop\n\nRunning with Claude Code\n\n1. Download Claude Code\n2. Ensure uvenv is installed and the uvx command is available\n3. Add the MCP server: claude mcp add ddg-search uvx duckduckgo-mcp-server\n\nRunning with SSE or Streamable HTTP\n\nThe server supports alternative transports for use with other MCP clients:\n\n \n\nThe default transport is stdio, which is used by Claude Desktop and Claude Code.\n\nWhen running with sse or streamable-http, override the default bind address (127.0.0.1:8000) with the --host and --port flags:\n\n \n\nRunning behind a reverse proxy or in Docker\n\nThe MCP SDK enables DNS-rebinding protection for the HTTP transports and, by default, only accepts Host/Origin headers for localhost. Behind a reverse proxy or in a container the client\'s Host header won\'t match, so requests fail with **421 Misdirected Request**.\n\nFix it by allow-listing the host(s) and origin(s) clients actually use (preferred over disabling protection). Values support host, host:port, and wildcard-port host:*:\n\n \n\nEquivalent environment variables (comma-separated) are also available: DDG_ALLOWED_HOSTS, DDG_ALLOWED_ORIGINS.\n\nAs a last resort you can turn the check off entirely with --disable-dns-rebinding-protection (or DDG_DISABLE_DNS_REBINDING_PROTECTION=1). Prefer an allow-list — disabling protection removes a defense against DNS-rebinding attacks. When nothing is configured, the secure localhost-only default is preserved.\n\nRunning behind a TLS-intercepting proxy\n\nCorporate proxies that re-sign HTTPS traffic with their own CA (via HTTPS_PROXY) cause outbound requests to fail with certificate verification errors, because the HTTP clients don\'t trust the proxy\'s self-signed CA (and httpx no longer reads the SSL_CERT_FILE environment variable). Point the server at your proxy\'s CA bundle:\n\n \n\nOr set DDG_CA_CERTS=/path/to/proxy-ca.pem. The bundle is used by both the search and fetch_content tools, on the httpx and curl backends alike.\n\nAs a last resort, --no-ssl-verify (or DDG_SSL_VERIFY=0) disables certificate verification entirely. This exposes traffic to interception by anyone on the network path — prefer --ca-certs.\n\nBackends (bypassing bot detection)\n\nSome sites — and, as of recently, DuckDuckGo\'s own search endpoint (html.duckduckgo.com) — block the default httpx client because of its distinctive TLS fingerprint, regardless of User-Agent. Cloudflare Bot Management and similar filters key on the JA3/TLS handshake, not on headers, so html.duckduckgo.com may answer httpx with an empty **HTTP 202** page (silently yielding "no results"). An opt-in backend, curl (implemented via curl_cffi), impersonates a real Chrome browser\'s TLS handshake and passes through those checks.\n\nBoth the search tool and the fetch_content tool support these backends.\n\n**Installation:**\n\n \n\n**Backend options:**\n\n| Value | Behavior | Needs [browser] |\n| ------ | ----------------------------------------------------------------------------------------- | ----------------- |\n| httpx | Lightweight async HTTP. Default. Works on most sites. | no |\n| curl | Uses curl_cffi with Chrome 131 TLS impersonation. Passes TLS-fingerprint-based filters. | yes |\n| auto | Tries httpx first; on 403 or a Cloudflare challenge response, retries with curl. | yes |\n\n**Two ways to configure the backend:**\n\n1. **Server-wide default** via the --fetch-backend CLI flag (applies to every fetch_content call):\n\n \n\n2. **Per-call override** via the backend argument on the fetch_content tool (overrides the CLI default for that single call). The tool exposes backend in its input schema, so an MCP client can choose "httpx", "curl", or "auto" on a fetch-by-fetch basis.\n\nFor fetch_content, the default stays httpx so users who don\'t need the impersonation don\'t pay for the extra dependency.\n\nSearch backend\n\nBecause DuckDuckGo\'s search endpoint now fingerprint-blocks plain httpx, the search tool defaults to **auto**: it tries httpx first and falls back to curl when it detects a block (HTTP 202/403). The fallback only works if the [browser] extra is installed; otherwise search returns a message telling you to install it.\n\nConfigure the search backend with the --search-backend CLI flag or the DDG_SEARCH_BACKEND environment variable (auto (default) / httpx / curl):\n\n \n\nDevelopment\n\nFor local development:\n\n \n\nAvailable Tools\n\n1. Search Tool\n\n \n\nPerforms a web search on DuckDuckGo and returns formatted results.\n\n**Parameters:**\n- query: Search query string\n- max_results: Maximum number of results to return (default: 10)\n- region: (Optional) Region/language code to override the default. Leave empty to use the configured default region.\n\n**Region Code Examples:**\n- us-en: United States (English)\n- cn-zh: China (Chinese)\n- jp-ja: Japan (Japanese)\n- de-de: Germany (German)\n- fr-fr: France (French)\n- wt-wt: No specific region\n\n**Returns:**\nFormatted string containing search results with titles, URLs, and snippets.\n\n**Example Usage:**\n- Search with default settings: search("python tutorial")\n- Search with specific region: search("latest news", region="jp-ja") for Japanese news\n\n2. Content Fetching Tool\n\n \n\nFetches and parse', 'readme_raw_len': 12023, 'one_line_zh': '使用 DuckDuckGo 进行网页搜索，支持内容抓取与解析', 'one_line_plain': 'DuckDuckGo search tool lets users search the web with content fetching and parsing'}, {'id': 5749, 'key': 'skillsmp-com-creators-daymade-claude-code-skills-frontend-visual-qa', 'kind': 'skill', 'name': 'frontend-visual-qa', 'one_line': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'downloads': None, 'stars': 1452, 'installs': None, 'mentions': 5, 'score': 503.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/daymade.png?size=64', 'official_desc': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard, design-system, or Electron/native app — via real-browser/native-app journeys and a Playwright sweep. Use after UI implementation to find typography, overflow, responsive, routing, data-viz, browser-output', 'stars_own': None, 'stars_creator': 1477, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'daymade/claude-code-skills', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '审计已渲染的界面，以及 Markdown 与 Obsidian 中的图片排版。', 'one_line_plain': 'The tool audits already-rendered UI and Markdown/Obsidian image layout for web apps, decks, dashboards, and more'}, {'id': 3936, 'key': 'mbailey-voice-mcp', 'kind': 'mcp', 'name': 'mbailey/voice-mcp', 'one_line': '🐍 🏠 - Voice interaction: speech-to-text, text-to-speech and real-time voice conversations via local…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/mbailey/voicemode', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/mbailey/voice-mcp', 'downloads': None, 'stars': 1405, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/mbailey.png?size=64', 'official_desc': 'VoiceMode Natural voice conversations with Claude Code (and other MCP capable agents) VoiceMode enables natural voice conversations with Claude Code.', 'stars_own': 1405, 'stars_creator': None, 'stars_own_repo': 'mbailey/voicemode', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "VoiceMode\n\n> Natural voice conversations with Claude Code (and other MCP capable agents)\n\n \n \n \n\nVoiceMode enables natural voice conversations with Claude Code. Voice isn't about replacing typing - it's about being available when typing isn't.\n\n**Perfect for:**\n\n- Walking to your next meeting\n- Cooking while debugging\n- Giving your eyes a break after hours of screen time\n- Holding a coffee (or a dog)\n- Any moment when your hands or eyes are busy\n\nSee It In Action\n\n \n\nQuick Start\n\n**Requirements:** Computer with microphone and speakers\n\nOption 1: Claude Code Plugin (Recommended)\n\nThe fastest way for Claude Code users to get started:\n\n \n\nOption 2: Python installer package\n\nInstalls dependencies and the VoiceMode Python package.\n\n \n\nFor manual setup, see the Getting Started Guide.\n\nFeatures\n\n- **Natural conversations** - speak naturally, hear responses immediately\n- **Works offline** - optional local voice services (Whisper STT, Kokoro TTS)\n- **Low latency** - fast enough to feel like a real conversation\n- **Smart silence detection** - stops recording when you stop speaking\n- **Privacy options** - run entirely locally or use cloud services\n\nCompatibility\n\n**Platforms:** Linux, macOS, Windows (native or WSL), NixOS\n**Python:** 3.10-3.14\n\nConfiguration\n\nVoiceMode works out of the box. For customization:\n\n \n\nSee the Configuration Guide for all options.\n\nPermissions Setup (Optional)\n\nTo use VoiceMode without permission prompts, add to ~/.claude/settings.json:\n\n \n\nSee the Permissions Guide for more options.\n\nLocal Voice Services\n\nFor privacy or offline use, install local speech services:\n\n- **Whisper.cpp** - Local speech-to-text\n- **Kokoro** - Local text-to-speech with multiple voices\n\nThese provide the same API as OpenAI, so VoiceMode switches seamlessly between them.\n\nInstallation Details\n\n \n System Dependencies by Platform \n\nUbuntu/Debian\n\n \n\n**WSL2 users**: The pulseaudio packages above are required for microphone access.\n\nFedora/RHEL\n\n \n\nmacOS\n\n \n\nNixOS\n\n \n\n \n\n \n Alternative Installation Methods \n\nFrom source\n\n \n\nNixOS system-wide\n\n \n\n \n\nTroubleshooting\n\n| Problem | Solution |\n|---------|----------|\n| No microphone access | Check terminal/app permissions. WSL2 needs pulseaudio packages. |\n| UV not found | Run curl -LsSf https://astral.sh/uv/install.sh \\| sh |\n| OpenAI API error | Verify OPENAI_API_KEY is set correctly |\n| No audio output | Check system audio settings and available devices |\n\nSave Audio for Debugging\n\n \n\nDocumentation\n\n- Getting Started - Full setup guide\n- Configuration - All environment variables\n- Whisper Setup - Local speech-to-text\n- Kokoro Setup - Local text-to-speech\n- Development Setup - Contributing guide\n\nFull documentation: voicemode.dev\n\nLinks\n\n- **Website**: voicemode.dev\n- **GitHub**: github.com/mbailey/voicemode\n- **PyPI**: pypi.org/project/voice-mode\n- **YouTube**: @getvoicemode\n- **Twitter/X**: @getvoicemode\n- **Newsletter**: \n\nLicense\n\nMIT - A Failmode Project\n\n---\nmcp-name: dev.voicemode/voicemode", 'readme_raw_len': 2982, 'one_line_zh': '语音交互：语音转文字、文字转语音，并可经本地服务进行实时语音对话。', 'one_line_plain': 'Voice interaction lets users perform speech-to-text, text-to-speech and real-time voice conversations locally'}, {'id': 4192, 'key': 'caol64-wenyan-mcp', 'kind': 'mcp', 'name': 'caol64/wenyan-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Format Markdown articles with Wenyan and publish them to WeChat Official Accounts', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '文颜 MCP Server 可以让 AI 自动将 Markdown 文章排版后发布至微信公众号。', 'url': 'https://github.com/caol64/wenyan-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/caol64/wenyan-mcp', 'downloads': None, 'stars': 1345, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/caol64.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Format Markdown articles with Wenyan and publish them to WeChat Official Accounts.', 'stars_own': 1345, 'stars_creator': None, 'stars_own_repo': 'caol64/wenyan-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '文颜 MCP Server\n\n \n \n \n \n \n\n简介\n\n**文颜（Wenyan）** 是一款多平台 Markdown 排版与发布工具，支持将 Markdown 一键转换并发布至：\n\n- 微信公众号\n- 知乎\n- 今日头条\n- 以及其它内容平台（持续扩展中）\n\n文颜的目标是：**让写作者专注内容，而不是排版和平台适配**。\n\n文颜的不同版本\n\n文颜目前提供多种形态，覆盖不同使用场景：\n\n- macOS App Store 版 - MAC 桌面应用\n- 跨平台桌面版 - Windows/Linux\n- CLI 版本 - 命令行 / CI 自动化发布\n- 👉 MCP 版本 - 本项目\n\n文颜 MCP Server 是什么？\n\n简单来说，它打通了“AI 写作”与“公众号发文”的通道。\n\n基于 MCP 协议，Claude Desktop 等 AI 客户端现在可以直接调用文颜（Wenyan）的排版引擎。写完文章后，不需要再去第三方编辑器里来回复制粘贴，直接让 AI 帮你排版并塞进微信草稿箱。\n\n**核心特性：**\n\n- **绕过排版工具**：AI 生成的 Markdown 直接转成微信富文本并上传，省去中间步骤。\n- **对话式排版**：直接打字跟 AI 说“换个橙色风格主题”，样式自动生效。\n- **不出窗口完成闭环**：在同一个聊天框里，顺滑搞定“想选题 -> 写文章 -> 调排版 -> 存草稿”的所有操作。\n\n**实战演示**：\n* 让 AI 帮你管理公众号的排版和发布\n* Moraya MCP 使用案例：微信公众号全托管\n\n功能特性\n\n- 一键发布 Markdown 到微信公众号草稿箱\n- 自动上传本地图片与封面\n- 支持远程 Server 发布（绕过 IP 白名单限制）\n- 内置多套精美排版主题\n- 支持自定义主题\n- 提供标准 MCP Tool 接口\n- 支持 AI 自动调用：\n - 渲染 Markdown\n - 主题管理\n - 发布草稿\n\n快速开始\n\n**安装**\n\n \n\n**Claude Desktop 配置 (claude_desktop_config.json)：**：\n\n \n\n基本用法\n\n列出主题\n\n对AI说\n\n> 目前你可以使用哪些公众号主题？\n\nAI回复：\n\n \n\n注册主题\n\n对AI说\n\n> 帮我把这个css(https://wenyan.yuzhi.tech/manhua.css)注册为一个新的公众号主题，名称为：xiuluochang\n\nAI回复：\n\n \n\n删除主题\n\n对AI说\n\n> 把名为“xiuluochang”的公众号主题删除\n\nAI回复：\n\n \n\n发布文章\n\n对AI说\n\n> 使用phycat主题将这篇文章发布到微信公众号：./tests/publish.md\n\nAI回复：\n\n \n\n概念\n\n环境变量配置\n\n> [!IMPORTANT]\n>\n> 请确保 MCP 启动时已配置如下环境变量，否则上传接口将调用失败。\n\n- WECHAT_APP_ID\n- WECHAT_APP_SECRET\n\n微信公众号 IP 白名单\n\n> [!IMPORTANT]\n>\n> 请确保运行文颜的机器 IP 已加入微信公众号后台的 IP 白名单，否则上传接口将调用失败。\n\n配置说明文档：https://yuzhi.tech/docs/wenyan/upload\n\n文章格式\n\n为了正确上传文章，每篇 Markdown 顶部需要包含一段 frontmatter：\n\n \n\n字段说明：\n\n| 字段 | 必填 | 说明 |\n| ---------- | -- | ----------------- |\n| title | ✅ | 文章标题 |\n| cover | ❌ | 封面图片（本地路径或网络 URL） |\n| author | ❌ | 作者 |\n| source_url | ❌ | 原文链接 |\n| type | ❌ | 文章类型，设为 image 表示图片消息（小绿书） |\n| image_list | ❌ | 图片路径列表（小绿书专用，最多 20 张） |\n| need_open_comment | ❌ | 是否打开评论 |\n| only_fans_can_comment | ❌ | 是否仅粉丝可评论 |\n\n说明：\n\n* 如果未指定 cover，将自动使用正文第一张图片作为封面\n* cover 支持本地路径和网络 URL\n* type 和 image_list 用于图片消息发布\n\n**示例文章**\n\n文内图片和文章封面\n\n把文章发布到公众号之前，文颜会按照微信要求自动处理文章内的所有图片，将其上传到公众号素材库。目前文颜对于以下图片都能很好的支持：\n\n- 本地硬盘绝对路径（如：/Users/xxx/image.jpg）\n- 网络路径（如：https://example.com/image.jpg）\n- 当前文章的相对路径（如：./assets/image.png）\n\n发布图片消息（小绿书）\n\n发布图片消息与发布图文消息的区别在于 frontmatter 中需要指定图片列表。支持两种方式：\n\n方式一：使用 type: image（推荐）\n\n在 frontmatter 中设置 type: image，CLI 会自动从正文中提取所有图片：\n\n \n\n方式二：手动指定 image_list\n\n直接在 frontmatter 中列出图片路径：\n\n \n\nimage_list 最多 20 张，首张即为封面。\n\nServer 模式\n\n相较于纯本地运行的**本地模式（Stdio Mode）**，wenyan-mcp还提供了 **远程客户端模式（Client–Server Mode）**。两种模式运行效果完全一致，你可以根据运行环境和网络条件选择最合适的方式。\n\n在本地模式下，MCP 直接调用微信公众号 API 完成图片上传和草稿发布。\n\n \n\n在远程客户端模式下，MCP 作为客户端，将发布请求发送到部署在云服务器上的 Wenyan Server，由 Server 完成微信公众号 API 调用。\n\n \n\n**适用于：**\n\n* 无本地固定 IP，需频繁添加IP 白名单的用户\n* 需团队协作的用户\n* 支持 CI/CD 自动发布\n* 支持 AI Agent 自动发布\n\n**Server 模式部署**\n\n**Claude Desktop 配置：**：\n\n \n\n多公众号发布（进阶）\n\n> [!IMPORTANT]\n>\n> 必须使用server模式才能使用多公众号发布功能。\n\n步骤：\n\n- 首先部署server\n- 在server端配置多个微信凭据，参考文档\n- 让MCP 客户端连接server\n\n对AI说\n\n> 使用phycat主题将这篇文章发布到微信公众号(your_app_id)：./tests/publish.md\n\nDocker 部署\n\n适合不希望安装 Node.js 环境的用户。\n\n \n\n* **Claude Desktop 配置：**：\n\n \n\n> **Docker 配置特别说明：**\n>\n> * **挂载目录 (-v)**：必须将宿主机的文件/图片目录挂载到容器内的 /mnt/host-downloads。\n> * **环境变量 (HOST_FILE_PATH)**：必须与宿主机挂载的文件/图片目录路径保持一致。\n> * **原理**：你的 Markdown 文件/文章内所引用的本地图片应放置在该目录中，Docker 会自动将其映射，使容器能够读取并上传。\n\n如何调试\n\n推荐使用官方 Inspector 进行调试：\n\n \n\n启动成功出现类似提示：\n\n \n\n访问以上链接即可打开调试页面。\n\n \n\n1. 正确填写启动命令\n2. 添加环境变量\n3. 点击 Connect\n4. 选择 Tools -> List Tools\n5. 选择要调试的接口\n6. 填入参数并点击 Run Tool\n7. 查看完整参数\n\n赞助\n\n如果你觉得文颜对你有帮助，可以给我家猫咪买点罐头 ❤️\n\nhttps://yuzhi.tech/sponsor\n\nLicense\n\nApache License Version 2.0', 'readme_raw_len': 3399, 'one_line_zh': '📇 🏠 🍎 🪟 🐧 - 用 Wenyan 排版 Markdown 文章，并发布到微信公众号', 'one_line_plain': 'Convert Markdown articles into formatted posts for WeChat Official Accounts'}, {'id': 2541, 'key': 'apecloud-aperag', 'kind': 'mcp', 'name': 'apecloud/ApeRAG', 'one_line': '🐍 ☁️ 🏠 - RAG platform combining Graph RAG, vector search and full-text search for building knowledge graphs…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'ApeRAG: Production-ready GraphRAG with multi-modal indexing, AI agents, MCP support, and scalable K8s deployment', 'url': 'https://github.com/apecloud/ApeRAG', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/apecloud/ApeRAG', 'downloads': None, 'stars': 1322, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/apecloud.png?size=64', 'official_desc': '🐍 ☁️ 🏠 - RAG platform combining Graph RAG, vector search and full-text search for building knowledge graphs and context engineering.', 'stars_own': 1322, 'stars_creator': None, 'stars_own_repo': 'apecloud/ApeRAG', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'ApeRAG\n \n\n \n\n \n\nApeRAG is a production-ready RAG (Retrieval-Augmented Generation) platform that combines Graph RAG, vector search, and full-text search with advanced AI agents. Build sophisticated AI applications with hybrid retrieval, multimodal document processing, intelligent agents, and enterprise-grade management features.\n\nApeRAG is the best choice for building your own Knowledge Graph, Context Engineering, and deploying intelligent AI agents that can autonomously search and reason across your knowledge base.\n\n阅读中文文档\n\n- Quick Start\n- Key Features\n- Kubernetes Deployment (Recommended for Production)\n- Development\n- Build Docker Image\n- Acknowledgments\n- License\n\nQuick Start\n\n> Before installing ApeRAG, make sure your machine meets the following minimum system requirements:\n>\n> - CPU >= 2 Core\n> - RAM >= 4 GiB\n> - Docker & Docker Compose\n\nThe easiest way to start ApeRAG is through Docker Compose. Before running the following commands, make sure that Docker and Docker Compose are installed on your machine:\n\n \n\nAfter running, you can access ApeRAG in your browser at:\n- **Web Interface**: http://localhost:3000/web/\n- **API Documentation**: http://localhost:8000/docs\n\nMCP (Model Context Protocol) Support\n\nApeRAG supports MCP (Model Context Protocol) integration, allowing AI assistants to interact with your knowledge base directly. After starting the services, configure your MCP client with:\n\n \n\n**Authentication** (by priority):\n1. **HTTP Authorization Header** (Recommended): Authorization: Bearer your-api-key\n2. **Environment Variable** (Fallback): APERAG_API_KEY=your-api-key\n\n**Important**: Use your deployed API origin if not local (e.g. https://your-host/mcp/). Replace your-api-key-here with a valid API key from your ApeRAG settings.\n\nThe MCP server provides:\n- **Collection browsing**: List and explore your knowledge collections\n- **Hybrid search**: Search using vector, full-text, and graph methods\n- **Intelligent querying**: Ask natural language questions about your documents\n\nEnhanced Document Parsing\n\nFor enhanced document parsing capabilities, ApeRAG supports an **advanced document parsing service** powered by MinerU, which provides superior parsing for complex documents, tables, and formulas. \n\n \n Enhanced Document Parsing Commands \n\n \n\nOr use the Makefile shortcuts (requires GNU Make):\n \n\n \n\nDevelopment & Contributing\n\nFor developers interested in source code development, advanced configurations, or contributing to ApeRAG, please refer to our Development Guide for detailed setup instructions.\n\nKey Features\n\n**1. Advanced Index Types**:\nFive comprehensive index types for optimal retrieval: **Vector**, **Full-text**, **Graph**, **Summary**, and **Vision** - providing multi-dimensional document understanding and search capabilities.\n\n**2. Intelligent AI Agents**:\nBuilt-in AI agents with MCP (Model Context Protocol) tool support that can automatically identify relevant collections, search content intelligently, and provide web search capabilities for comprehensive question answering.\n\n**3. Enhanced Graph RAG with Entity Normalization**:\nDeeply modified LightRAG implementation with advanced entity normalization (entity merging) for cleaner knowledge graphs and improved relational understanding.\n\n**4. Multimodal Processing & Vision Support**:\nComplete multimodal document processing including vision capabilities for images, charts, and visual content analysis alongside traditional text processing.\n\n**5. Hybrid Retrieval Engine**:\nSophisticated retrieval system combining Graph RAG, vector search, full-text search, summary-based retrieval, and vision-based search for comprehensive document understanding.\n\n**6. MinerU Integration**:\nAdvanced document parsing service powered by MinerU technology, providing superior parsing for complex documents, tables, formulas, and scientific content with optional GPU acceleration.\n\n**7. Production-Grade Deployment**:\nFull Kubernetes support with Helm charts and KubeBlocks integration for simplified deployment of production-grade databases (PostgreSQL, Redis, Qdrant, Elasticsearch, Neo4j).\n\n**8. Enterprise Management**:\nBuilt-in audit logging, LLM model management, graph visualization, comprehensive document management interface, and agent workflow management.\n\n**9. MCP Integration**:\nFull support for Model Context Protocol (MCP), enabling seamless integration with AI assistants and tools for direct knowledge base access and intelligent querying.\n\n**10. Developer Friendly**:\nFastAPI backend, React frontend, async task processing with Celery, extensive testing, comprehensive development guides, and agent development framework for easy contribution and customization.\n\nKubernetes Deployment (Recommended for Production)\n\n> **Enterprise-grade deployment with high availability and scalability**\n\nDeploy ApeRAG to Kubernetes using our provided Helm chart. This approach offers high availability, scalability, and production-grade management capabilities.\n\nPrerequisites\n\n* Kubernetes cluster (v1.20+)\n* kubectl configured and connected to your cluster\n* Helm v3+ installed\n\nClone the Repository\n\nFirst, clone the ApeRAG repository to get the deployment files:\n\n \n\nStep 1: Deploy Database Services\n\nApeRAG requires PostgreSQL, Redis, Qdrant, and Elasticsearch. You have two options:\n\n**Option A: Use existing databases** - If you already have these databases running in your cluster, edit deploy/aperag/values.yaml to configure your database connection details, then skip to Step 2.\n\n**Option B: Deploy databases with KubeBlocks** - Use our automated database deployment (database connections are pre-configured):\n\n \n\nWait for all database pods to be in Running status before proceeding.\n\nStep 2: Deploy ApeRAG Application\n\n \n\nConfiguration Options\n\n**Resource Requirements**: By default, includes doc-ray service (requires 4+ CPU cores, 8GB+ RAM). To disable: set docray.enabled: false in values.yaml.\n\n**Advanced Settings**: Review values.yaml for additional configuration options including images, resources, and Ingress settings.\n\nAccess Your Deployment\n\nOnce deployed, access ApeRAG using port forwarding:\n\n \n\nFor production environments, configure Ingress in values.yaml for external access.\n\nTroubleshooting\n\n**Database Issues**: See deploy/databases/README.md for KubeBlocks management, credentials, and uninstall procedures.\n\n**Pod Status**: Check pod logs for any deployment issues:\n \n\nAcknowledgments\n\nApeRAG integrates and builds upon several excellent open-source projects:\n\nLightRAG\nThe graph-based knowledge retrieval capabilities in ApeRAG are powered by a deeply modified version of LightRAG:\n- **Paper**: "LightRAG: Simple and Fast Retrieval-Augmented Generation" (arXiv:2410.05779)\n- **Authors**: Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, Chao Huang\n- **License**: MIT License\n\nWe have extensively modified LightRAG to support production-grade concurrent processing, distributed task queues (Celery/Prefect), and stateless operations. See our LightRAG modifications changelog for details.\n\nCommunity\n\n* Discord\n* Feishu\n\n \n\nStar History\n\n \n\nLicense\n\nApeRAG is licensed under the Apache License 2.0. See the LICENSE file for details.', 'readme_raw_len': 7166, 'one_line_zh': '融合 Graph RAG、向量检索与全文检索的 RAG 平台，用于构建知识图谱', 'one_line_plain': 'Build knowledge graphs with Graph RAG, vector and full-text search'}, {'id': 3109, 'key': 'aidc-ai-pixelle-mcp', 'kind': 'mcp', 'name': 'AIDC-AI/Pixelle-MCP', 'one_line': '🐍 📇 🏠 - Omnimodal AIGC framework that turns ComfyUI workflows into MCP tools without code, for text, image,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/ATH-MaaS/Pixelle-MCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/AIDC-AI/Pixelle-MCP', 'downloads': None, 'stars': 1131, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/AIDC-AI.png?size=64', 'official_desc': 'or quick experience and testing** Temporary Run **View uvx CLI Reference →** Persistent Installation **View pip CLI Reference →** After startup, it will automatically enter the **configuration wizard** to guide you through execution engine selection (ComfyUI/RunningHub) and LLM configuration.', 'stars_own': 1131, 'stars_creator': None, 'stars_own_repo': 'ATH-MaaS/Pixelle-MCP', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "🎨 Pixelle MCP - Omnimodal Agent Framework \n\n English | 中文 \n\n ✨ An AIGC solution based on the MCP protocol, supporting both local ComfyUI and cloud ComfyUI (RunningHub) modes, seamlessly converting workflows into MCP tools with zero code. \n\n \n\nhttps://github.com/user-attachments/assets/65422cef-96f9-44fe-a82b-6a124674c417\n\n📋 Recent Updates\n\n- ✅ **2025-09-29**: Added RunningHub cloud ComfyUI support, enabling workflow execution without local GPU and ComfyUI environment\n- ✅ **2025-09-03**: Architecture refactoring from three services to unified application; added CLI tool support; published to PyPI\n- ✅ **2025-08-12**: Integrated the LiteLLM framework, adding multi-model support for Gemini, DeepSeek, Claude, Qwen, and more\n\n🚀 Features\n\n- ✅ 🔄 **Full-modal Support**: Supports TISV (Text, Image, Sound/Speech, Video) full-modal conversion and generation\n- ✅ 🚀 **Dual Execution Modes**: Local ComfyUI self-hosted environment + RunningHub cloud ComfyUI service, users can flexibly choose based on their needs\n- ✅ 🧩 **ComfyUI Ecosystem**: Built on ComfyUI, inheriting all capabilities from the open ComfyUI ecosystem\n- ✅ 🔧 **Zero-code Development**: Defines and implements the Workflow-as-MCP Tool solution, enabling zero-code development and dynamic addition of new MCP Tools\n- ✅ 🗄️ **MCP Server**: Based on the MCP protocol, supporting integration with any MCP client (including but not limited to Cursor, Claude Desktop, etc.)\n- ✅ 🌐 **Web Interface**: Developed based on the Chainlit framework, inheriting Chainlit's UI controls and supporting integration with more MCP Servers\n- ✅ 📦 **One-click Deployment**: Supports PyPI installation, CLI commands, Docker and other deployment methods, ready to use out of the box\n- ✅ ⚙️ **Simplified Configuration**: Uses environment variable configuration scheme, simple and intuitive configuration\n- ✅ 🤖 **Multi-LLM Support**: Supports multiple mainstream LLMs, including OpenAI, Ollama, Gemini, DeepSeek, Claude, Qwen, and more\n\n📁 Project Architecture\n\nPixelle MCP adopts a **unified architecture design**, integrating MCP server, web interface, and file services into one application, providing:\n\n- 🌐 **Web Interface**: Chainlit-based chat interface supporting multimodal interaction\n- 🔌 **MCP Endpoint**: For external MCP clients (such as Cursor, Claude Desktop) to connect\n- 📁 **File Service**: Handles file upload, download, and storage\n- 🛠️ **Workflow Engine**: Supports both local ComfyUI and cloud ComfyUI (RunningHub) workflows, automatically converts workflows into MCP tools\n\n \n\n \n\n🏃\u200d♂️ Quick Start\n\nChoose the deployment method that best suits your needs, from simple to complex:\n\n🎯 Method 1: One-click Experience\n\n> 💡 **Zero configuration startup, perfect for quick experience and testing**\n\n🚀 Temporary Run\n\n \n\n📚 **View uvx CLI Reference →**\n\n📦 Persistent Installation\n\n \n\n📚 **View pip CLI Reference →**\n\nAfter startup, it will automatically enter the **configuration wizard** to guide you through execution engine selection (ComfyUI/RunningHub) and LLM configuration.\n\n🛠️ Method 2: Local Development Deployment\n\n> 💡 **Supports custom workflows and secondary development**\n\n📥 1. Get Source Code\n\n \n\n🚀 2. Start Service\n\n \n\n📚 **View Complete CLI Reference →**\n\n🔧 3. Add Custom Workflows (Optional)\n\n \n\n**⚠️ Important**: Make sure to test workflows in ComfyUI first to ensure they run properly, otherwise execution will fail.\n\n🐳 Method 3: Docker Deployment\n\n> 💡 **Suitable for production environments and containerized deployment**\n\n📋 1. Prepare Configuration\n\n \n\n🚀 2. Start Container\n\n \n\n🌐 Access Services\n\nRegardless of which method you use, after startup you can access via:\n\n- **🌐 Web Interface**: http://localhost:9004 \n *Default username and password are both dev, can be modified after startup*\n- **🔌 MCP Endpoint**: http://localhost:9004/pixelle/mcp \n *For MCP clients like Cursor, Claude Desktop to connect*\n\n**💡 Port Configuration**: Default port is 9004, can be customized via environment variable PORT=your_port.\n\n⚙️ Initial Configuration\n\nOn first startup, the system will automatically detect configuration status:\n\n1. **🚀 Execution Engine Selection**: Choose between local ComfyUI or RunningHub cloud service\n2. **🤖 LLM Configuration**: Configure at least one LLM provider (OpenAI, Ollama, etc.)\n3. **📁 Workflow Directory**: System will automatically create necessary directory structure\n\n🌐 RunningHub Cloud Mode Advantages\n- ✅ **Zero Hardware Requirements**: No need for local GPU or high-performance hardware\n- ✅ **No Environment Setup**: No need to install and configure ComfyUI locally\n- ✅ **Ready to Use**: Register and get API key to start immediately\n- ✅ **Stable Performance**: Professional cloud infrastructure ensures stable execution\n- ✅ **Auto Scaling**: Automatically handles concurrent requests and resource allocation\n\n🏠 Local ComfyUI Mode Advantages\n- ✅ **Full Control**: Complete control over execution environment and model versions\n- ✅ **Privacy Protection**: All data processing happens locally, ensuring data privacy\n- ✅ **Custom Models**: Support for custom models and nodes not available in cloud\n- ✅ **No Network Dependency**: Can work offline without internet connection\n- ✅ **Cost Control**: No cloud service fees for high-frequency usage\n\n**🆘 Need Help?** Join community groups for support (see Community section below)\n\n🛠️ Add Your Own MCP Tool\n\n⚡ One workflow = One MCP Tool, supports two addition methods:\n\n📋 **Method 1: Local ComfyUI Workflow** - Export API format workflow files\n📋 **Method 2: RunningHub Workflow ID** - Use cloud workflow IDs directly\n\n \n\n🎯 1. Add the Simplest MCP Tool\n\n* 📝 Build a workflow in ComfyUI for image Gaussian blur (Get it here), then set the LoadImage node's title to $image.image! as shown below:\n \n\n* 📤 Export it as an API format file and rename it to i_blur.json. You can export it yourself or use our pre-exported version (Get it here)\n\n* 📋 Copy the exported API workflow file (must be API format), input it on the web page, and let the LLM add this Tool\n\n \n\n* ✨ After sending, the LLM will automatically convert this workflow into an MCP Tool\n\n \n\n* 🎨 Now, refresh the page and send any image to perform Gaussian blur processing via LLM\n\n \n\n🔌 2. Add a Complex MCP Tool\n\nThe steps are the same as above, only the workflow part differs (Download workflow: UI format and API format)\n\n> **Note:** When using RunningHub, you only need to input the corresponding workflow ID, no need to download and upload workflow files.\n\n \n\n🔧 ComfyUI Workflow Custom Specification\n\n🎨 Workflow Format\nThe system supports ComfyUI workflows. Just design your workflow in the canvas and export it as API format. Use special syntax in node titles to define parameters and outputs.\n\n📝 Parameter Definition Specification\n\nIn the ComfyUI canvas, double-click the node title to edit, and use the following DSL syntax to define parameters:\n\n \n\n🔍 Syntax Explanation:\n- param_name: The parameter name for the generated MCP tool function\n- ~: Optional, indicates URL parameter upload processing, returns relative path\n- field_name: The corresponding input field in the node\n- !: Indicates this parameter is required\n- description: Description of the parameter\n\n💡 Example:\n\n**Required parameter example:**\n\n- Set LoadImage node title to: $image.image!:Input image URL\n- Meaning: Creates a required parameter named image, mapped to the node's image field\n\n**URL upload processing example:**\n\n- Set any node title to: $image.~image!:Input image URL\n- Meaning: Creates a required parameter named image, system will automatically download URL and upload to ComfyUI, returns relative path\n\n> 📝 Note: LoadImage, VHS_LoadAudioUpload, VHS_LoadVideo and other nodes have built-in functionality, no need to add ~ marker\n\n🎯 Type Inference Rules\n\nThe system automatically infers parameter types based on the current value of the node field:\n- 🔢 int: Integer values (e.g. 512, 1024)\n- 📊 float: Floating-point values (e.g. 1.5, 3.14)\n- ✅ bool: Boolean values (e.g. true, false)\n- 📝 str: String values (default typ", 'readme_raw_len': 11185, 'one_line_zh': '全模态 AIGC 框架，无需写代码即可把 ComfyUI 工作流转成 MCP 工具，覆盖文本与图像等。', 'one_line_plain': 'Omnimodal AIGC framework converts ComfyUI workflows into MCP tools for text, image, and other media'}, {'id': 3409, 'key': 'jae-jae-fetcher-mcp', 'kind': 'mcp', 'name': 'jae-jae/fetcher-mcp', 'one_line': '📇 🏠 - Fetch web page content using a Playwright headless browser, supporting JavaScript rendering, content…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for fetch web page content using Playwright headless browser.', 'url': 'https://github.com/jae-jae/fetcher-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/jae-jae/fetcher-mcp', 'downloads': None, 'stars': 1088, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/jae-jae.png?size=64', 'official_desc': '📇 🏠 - Fetch web page content using a Playwright headless browser, supporting JavaScript rendering, content extraction and Markdown or HTML output.', 'stars_own': 1088, 'stars_creator': None, 'stars_own_repo': 'jae-jae/fetcher-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "中文 |\nDeutsch |\nEspañol |\nfrançais |\n日本語 |\n한국어 |\nPortuguês |\nРусский\n\nFetcher MCP\n\nMCP server for fetch web page content using Playwright headless browser.\n\n> 🌟 **Recommended**: OllaMan - Powerful Ollama AI Model Manager.\n\nAdvantages\n\n- **JavaScript Support**: Unlike traditional web scrapers, Fetcher MCP uses Playwright to execute JavaScript, making it capable of handling dynamic web content and modern web applications.\n\n- **Intelligent Content Extraction**: Built-in Readability algorithm automatically extracts the main content from web pages, removing ads, navigation, and other non-essential elements.\n\n- **Flexible Output Format**: Supports both HTML and Markdown output formats, making it easy to integrate with various downstream applications.\n\n- **Parallel Processing**: The fetch_urls tool enables concurrent fetching of multiple URLs, significantly improving efficiency for batch operations.\n\n- **Resource Optimization**: Automatically blocks unnecessary resources (images, stylesheets, fonts, media) to reduce bandwidth usage and improve performance.\n\n- **Robust Error Handling**: Comprehensive error handling and logging ensure reliable operation even when dealing with problematic web pages.\n\n- **Configurable Parameters**: Fine-grained control over timeouts, content extraction, and output formatting to suit different use cases.\n\nQuick Start\n\nRun directly with npx:\n\n \n\nFirst time setup - install the required browser by running the following command in your terminal:\n\n \n\nHTTP and SSE Transport\n\nUse the --transport=http parameter to start both Streamable HTTP endpoint and SSE endpoint services simultaneously:\n\n \n\nAfter startup, the server provides the following endpoints:\n\n- /mcp - Streamable HTTP endpoint (modern MCP protocol)\n- /sse - SSE endpoint (legacy MCP protocol)\n\nClients can choose which method to connect based on their needs.\n\nDebug Mode\n\nRun with the --debug option to show the browser window for debugging:\n\n \n\nConfiguration MCP\n\nConfigure this MCP server in Claude Desktop:\n\nOn MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n\nDocker Deployment\n\nRunning with Docker\n\n \n\nDeploying with Docker Compose\n\nCreate a docker-compose.yml file:\n\n \n\nThen run:\n\n \n\nFeatures\n\n- fetch_url - Retrieve web page content from a specified URL\n\n - Uses Playwright headless browser to parse JavaScript\n - Supports intelligent extraction of main content and conversion to Markdown\n - Supports the following parameters:\n - url: The URL of the web page to fetch (required parameter)\n - timeout: Page loading timeout in milliseconds, default is 30000 (30 seconds)\n - waitUntil: Specifies when navigation is considered complete, options: 'load', 'domcontentloaded', 'networkidle', 'commit', default is 'load'\n - extractContent: Whether to intelligently extract the main content, default is true\n - maxLength: Maximum length of returned content (in characters), default is no limit\n - returnHtml: Whether to return HTML content instead of Markdown, default is false\n - waitForNavigation: Whether to wait for additional navigation after initial page load (useful for sites with anti-bot verification), default is false\n - navigationTimeout: Maximum time to wait for additional navigation in milliseconds, default is 10000 (10 seconds)\n - disableMedia: Whether to disable media resources (images, stylesheets, fonts, media), default is true\n - debug: Whether to enable debug mode (showing browser window), overrides the --debug command line flag if specified\n\n- fetch_urls - Batch retrieve web page content from multiple URLs in parallel\n - Uses multi-tab parallel fetching for improved performance\n - Returns combined results with clear separation between webpages\n - Supports the following parameters:\n - urls: Array of URLs to fetch (required parameter)\n - Other parameters are the same as fetch_url\n\n- browser_install - Install Playwright Chromium browser binary automatically\n\n - Installs required Chromium browser binary when not available\n - Automatically suggested when browser installation errors occur\n - Supports the following parameters:\n - withDeps: Install system dependencies required by Chromium browser, default is false\n - force: Force installation even if Chromium is already installed, default is false\n\nTips\n\nHandling Special Website Scenarios\n\nDealing with Anti-Crawler Mechanisms\n\n- **Wait for Complete Loading**: For websites using CAPTCHA, redirects, or other verification mechanisms, include in your prompt:\n\n \n\n This will use the waitForNavigation: true parameter.\n\n- **Increase Timeout Duration**: For websites that load slowly:\n \n This adjusts both timeout and navigationTimeout parameters accordingly.\n\nContent Retrieval Adjustments\n\n- **Preserve Original HTML Structure**: When content extraction might fail:\n\n \n\n Sets extractContent: false and returnHtml: true.\n\n- **Fetch Complete Page Content**: When extracted content is too limited:\n\n \n\n Sets extractContent: false.\n\n- **Return Content as HTML**: When HTML format is needed instead of default Markdown:\n \n Sets returnHtml: true.\n\nDebugging and Authentication\n\nEnabling Debug Mode\n\n- **Dynamic Debug Activation**: To display the browser window during a specific fetch operation:\n \n This sets debug: true even if the server was started without the --debug flag.\n\nUsing Custom Cookies for Authentication\n\n- **Manual Login**: To login using your own credentials:\n\n \n\n Sets debug: true or uses the --debug flag, keeping the browser window open for manual login.\n\n- **Interacting with Debug Browser**: When debug mode is enabled:\n\n 1. The browser window remains open\n 2. You can manually log into the website using your credentials\n 3. After login is complete, content will be fetched with your authenticated session\n\n- **Enable Debug for Specific Requests**: Even if the server is already running, you can enable debug mode for a specific request:\n \n Sets debug: true for this specific request only, opening the browser window for manual login.\n\nDevelopment\n\nInstall Dependencies\n\n \n\nInstall Playwright Browser\n\nInstall the browsers needed for Playwright:\n\n \n\nBuild the Server\n\n \n\nDebugging\n\nUse MCP Inspector for debugging:\n\n \n\nYou can also enable visible browser mode for debugging:\n\n \n\nRelated Projects\n\n- g-search-mcp: A powerful MCP server for Google search that enables parallel searching with multiple keywords simultaneously. Perfect for batch search operations and data collection.\n\nLicense\n\nLicensed under the MIT License", 'readme_raw_len': 6505, 'one_line_zh': '使用 Playwright 无头浏览器抓取网页内容，支持 JavaScript 渲染与内容提取', 'one_line_plain': 'Playwright headless browser lets you fetch web page content, including JavaScript-rendered pages'}]/2026-10-20 https://anclum.com/en/[{'id': 312, 'key': 'ahujasid-blender-mcp', 'kind': 'mcp', 'name': 'ahujasid/blender-mcp', 'one_line': '🐍 - Connect AI assistants to Blender', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/ahujasid/mcp-for-blender', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ahujasid/blender-mcp', 'downloads': None, 'stars': 31675, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ahujasid.png?size=64', 'official_desc': 'MCP for Blender Connect Blender to any LLM** formerly blender-mcp — the PyPI package is now mcp-for-blender.* Existing setups keep working; no config change is required.', 'stars_own': 31675, 'stars_creator': None, 'stars_own_repo': 'ahujasid/mcp-for-blender', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'MCP for Blender\n\n**Connect Blender to any LLM**\n\n*formerly blender-mcp — the PyPI package is now mcp-for-blender.*\n*Existing setups keep working; no config change is required. Read more*\n\n**Disclaimer:** This is a third-party integration and not made by Blender\n\nPrompt-assisted 3D modeling, scene creation, and manipulation — driven by AI.\n\n \n \n \n \n \n \n\n \n\n \n\n**Supporters**\n\nCodeRabbit\nGuillermo Rauch\n\n**All supporters:** Support this project\n\n \n\n---\n\nQuickstart\n\n> **Note:** the PyPI package blender-mcp is now **mcp-for-blender**. Existing setups\n> keep working — uvx blender-mcp still runs the server and **no config change is\n> required**. New installs should use mcp-for-blender.\n> What changed and why\n\n**One command** sets up your AI apps and the Blender addon:\n\n**macOS / Linux**\n\n \n\n**Windows** (PowerShell)\n\n \n\nAlready have uv? Run uvx mcp-for-blender setup instead.\n\nIt installs uv if needed, finds the MCP clients on your machine (Claude Desktop, Claude Code, Codex, Cursor, VS Code, Devin Desktop/Windsurf, OpenCode, Antigravity), and lets you pick which to set up. Then it installs the Blender addon and enables it. Your existing config is kept: it only adds a blender entry and saves a .bak of each file it changes.\n\nThen **fully quit and reopen your AI app** (on Windows, quit it from the system tray), open Blender, and ask it to build something. The addon starts its server when Blender opens.\n\n \n Manual setup, in case the automatic setup doesn\'t work \n\nThree steps: install uv, point your MCP client at the server, install the Blender addon.\n\n**1. Install uv**\n\n \n\n> **Warning:** Do not proceed before installing uv. Use the official installer — *not* pip install uv.\n\n**2. Add the MCP server to your client**\n\n \n Claude Desktop — Settings → Developer → Edit Config \n\n \n \n\n \n Claude Code \n\n \n \n\n \n Codex \n\n \n \n\n \n Cursor / VS Code / OpenCode / Antigravity \n\nSee MCP Client Setup below for per-client instructions and one-click install buttons.\n \n\n**3. Install the Blender addon**\n\n \n\nThen in Blender: **Edit → Preferences → Add-ons** → enable **Interface: MCP for Blender**.\n\n**4. Connect**\n\nThe addon starts its server when Blender opens. To check, press N in the 3D viewport → open the **MCP for Blender** tab (click **Start MCP Server** if it isn\'t running). That\'s it — ask Claude to build something.\n\n \n\n> **Note:** Only run **one** instance of the MCP server (either Cursor or Claude Desktop), not both.\n\n---\n\nTable of Contents\n\n- Quickstart\n- Features\n- Premium\n- Components\n- Installation\n - Automatic setup\n - Prerequisites\n - Make your client find uvx\n - Pin the Python version\n - Install without uv\n - Run with Docker\n - Environment Variables\n- MCP Client Setup\n - Claude for Desktop\n - Codex\n - Cursor\n - Visual Studio Code\n - OpenCode\n - Antigravity\n- Installing the Blender Addon\n- Upgrading (existing users)\n- Usage\n - Starting the Connection\n - Using with Claude\n - Capabilities\n - Example Commands\n- Persistent API Credentials\n- Troubleshooting\n- Technical Details\n- Limitations & Security Considerations\n- Telemetry Control\n- Feedback\n- Contributing\n- Disclaimer\n- Star History\n\n---\n\nFeatures\n\n| | |\n|---|---|\n| **Two-way communication** | Connect Claude AI to Blender through a socket-based server |\n| **Object manipulation** | Create, modify, and delete 3D objects in Blender |\n| **Material control** | Apply and modify materials and colors |\n| **Visual verification** | Multi-angle, camera, wireframe, X-ray and animation-strip views, plus renders, so the AI checks its own work |\n| **Code execution** | The AI writes Blender Python directly |\n| **Asset & model generation** | Poly Haven assets, Sketchfab models, Poly Pizza low-poly models, and AI-generated 3D models via Tripo, Hyper3D Rodin and Hunyuan3D |\n\nPremium\n\nGenerate AI 3D models (Hunyuan3D, Tripo, Hyper3D Rodin) straight into Blender without bringing your own API keys. More details\n\nComponents\n\nThe system consists of two main components:\n\n1. **Blender Addon** (addon.py) — a Blender addon that creates a socket server within Blender to receive and execute commands\n2. **MCP Server** (src/blender_mcp/server.py) — a Python server that implements the Model Context Protocol and connects to the Blender addon\n\n---\n\nInstallation\n\nAutomatic setup\n\nThe quickest way is the one-line installer. It installs uv, configures your MCP clients, and installs and enables the Blender addon:\n\n \n\n \n\nAlready have uv? Run uvx mcp-for-blender setup instead. See Quickstart for what it changes. The rest of this section covers manual setup.\n\nPrerequisites\n\n- **Blender** 3.0 or newer\n- **Python** 3.10 or newer\n- **uv** package manager\n\n \n Installing uv, per platform \n\n**macOS**\n \n\n**Windows**\n \n\nThen add uv to the user path in Windows (you may need to restart Claude Desktop after):\n\n \n\n**Linux**\n \n\nIt lands in ~/.local/bin — open a new shell so it\'s on your PATH.\n\nOtherwise, installation instructions are on their website: Install uv\n\nOn every OS, use uv\'s **official installer above — not pip install uv**, which may not create the uvx command and can hide uv inside an environment your client can\'t see.\n \n\n> **Warning:** Do not proceed before installing uv.\n\nMake your client find uvx\n\nMCP clients started from a GUI (Claude Desktop, Cursor, VS Code from the Dock/Start menu) do **not** inherit your terminal\'s PATH, so a bare "command": "uvx" can fail with **spawn uvx ENOENT** even though uvx works in your terminal. If that happens:\n\n- Find uvx\'s full path — which uvx (macOS/Linux) or where uvx (Windows) — and use it as "command", e.g. /opt/homebrew/bin/uvx or C:\\Users\\ \\.local\\bin\\uvx.exe.\n- On Windows you can instead wrap it: "command": "cmd", "args": ["/c", "uvx", "mcp-for-blender"].\n- After any PATH or config change, **fully quit and relaunch** the client (Windows: quit from the system tray, not just the window; macOS: Cmd + Q ).\n\nPin the Python version\n\n*Avoid conda / pyenv / version conflicts.*\n\nuv chooses which Python runs the server. On machines with conda (auto-activated base), pyenv, or asdf — or with a newer CPython release that some dependencies do not have wheels for yet — uv can grab an interpreter that makes installation fail. Pin Python 3.11 and prefer uv-managed interpreters to avoid using whatever is on your PATH:\n\n \n\n--python 3.11 still satisfies this package\'s requires-python >=3.10, and UV_PYTHON_PREFERENCE=only-managed keeps uv from selecting conda, pyenv, asdf, or system Python first. (The repo\'s .python-version is only a hint for contributors and does **not** affect uvx.)\n\nIf a previous failed attempt keeps replaying after a fix, clear the cache:\n\n \n\nInstall without uv\n\nOn locked-down machines you can skip uvx entirely with pipx, then point your client at the installed command:\n\n \n\nUse the resulting absolute path as "command" (find it with which mcp-for-blender / where mcp-for-blender) and omit args.\n\nRun with Docker\n\nYou can run the MCP server in a container instead of installing it. Blender itself still runs on your machine — the container only hosts the MCP server, which connects out to the Blender addon.\n\nBuild the image from the repo root:\n\n \n\nThen point your MCP client at it (the -i flag is required — the server talks to the client over stdin/stdout):\n\n \n\nThe image defaults to BLENDER_HOST=host.docker.internal, which reaches the host\'s Blender out of the box with Docker Desktop on **macOS and Windows**.\n\nOn **Linux**, host.docker.internal doesn\'t exist and the addon only listens on localhost, so use host networking instead:\n\n \n\nTo enable safe mode in the container, add "-e", "BLENDER_MCP_SAFE_MODE=1" to args.\n\nEnvironment Variables\n\nThe following environment variables can be used to configure the Blender connection:\n\n| Variable | Default | Description |\n|---|---|---|\n| BLENDER_HOST | localhost | Host address for Blender socket server |\n| BLENDER_PORT | 9876 | Port number for Blender socket server |\n| BLENDER_MCP_SAFE_MODE | off | Set to 1 to validate scripts before they run in Blender (see below) |\n| BLENDER_MCP_APPS |', 'readme_raw_len': 25500, 'one_line_zh': '🐍 - 将 AI 助手连接到 Blender', 'one_line_plain': 'Lets AI assistants control Blender'}, {'id': 797, 'key': 'shashankss1205-codegraphcontext', 'kind': 'mcp', 'name': 'shashankss1205/codegraphcontext', 'one_line': '🐍 🏠 🍎 🪟 🐧 - Index local code into a graph database to give AI assistants context, with graphical code…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/CodeGraphContext/CodeGraphContext', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Shashankss1205/CodeGraphContext', 'downloads': None, 'stars': 4299, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Shashankss1205.png?size=64', 'official_desc': '🐍 🏠 🍎 🪟 🐧 - Index local code into a graph database to give AI assistants context, with graphical code visualizations for humans.', 'stars_own': 4299, 'stars_creator': None, 'stars_own_repo': 'CodeGraphContext/CodeGraphContext', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': '🏗️ CodeGraphContext (CGC)\n\n**Turn code repositories into a queryable graph for AI agents.**\n\n🌐 **Languages:**\n- 🇬🇧 English\n- 🇨🇳 中文\n- 🇰🇷 한국어\n- 🇺🇦 Українська\n- 🇷🇺 Русский\n- 🇯🇵 日本語\n- 🇮🇳 தமிழ்\n- 🇪🇸 Español (Soon)\n\n🌍 **Help translate CodeGraphContext to your language by raising an issue & PR on GitHub Issues!**\n\n \n \n Bridge the gap between deep code graphs and AI context. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\nA powerful **MCP server** and **CLI toolkit** that indexes local code into a graph database to provide context to AI assistants and developers. Use it as a standalone CLI for comprehensive code analysis or connect it to your favorite AI IDE via MCP for AI-powered code understanding.\n\n---\n\n📍 Quick Navigation\n* 🚀 Quick Start \n* 📋 Prerequisites\n* 🏃 How to Run the Project Locally\n* 🌐 Supported Programming Languages \n* 🛠️ CLI Toolkit \n* 🤖 MCP Server \n* 🗄️ Database Options\n* 🔬 SCIP indexing (optional)\n\n---\n\n✨ Experience CGC\n\n👨🏻\u200d💻 Installation and CLI\n> Install in seconds with pip and unlock a powerful CLI for code graph analysis.\n \n\n🛠️ Indexing in Seconds\n> The CLI intelligently parses your tree-sitter nodes to build the graph.\n \n\n🤖 Powering your AI Assistant\n> Use natural language to query complex call-chains via MCP.\n \n\n---\n\nWhy CodeGraphContext?\n\nCodeGraphContext is built for the moment when plain text search stops being enough. It turns a repository into a graph of files, symbols, calls, inheritance, imports, and relationships so you can move from "where is this defined?" to "how does this actually connect?" without jumping between tools.\n\n \n\nComparison at a glance\n\n| Approach | Best for | Tradeoff |\n| --- | --- | --- |\n| grep / file search | Exact string lookup | Misses relationships and code structure |\n| RAG over code chunks | Natural-language retrieval | Can lose symbol-level precision |\n| CodeGraphContext | Repository-wide reasoning | Requires an indexing step |\n\nCommon use cases\n\n- Trace call chains across files and packages.\n- Understand class hierarchies, imports, and module boundaries.\n- Find dead code, hotspots, and unexpected dependencies.\n- Give an AI assistant the same structural context a maintainer would use.\n\nFAQ\n\n- **Do I need a remote service?** No. CGC works locally with embedded backends or with external graph databases when you want them.\n- **Is it only for one language?** No. It supports a broad mix of mainstream languages and keeps expanding.\n- **Can I use it with an AI assistant?** Yes. That is one of the main reasons the MCP server exists.\n\n---\n\nProject Details\n- **Version:** 0.5.1\n- **Authors:** Shashank Shekhar Singh \n- **License:** MIT License (See LICENSE for details)\n- **Website:** CodeGraphContext\n\n---\n\n👨\u200d💻 Maintainer\n**CodeGraphContext** is created and actively maintained by:\n\n**Shashank Shekhar Singh** \n- 📧 Email: shashankshekharsingh1205@gmail.com\n- 🐙 GitHub: @Shashankss1205\n- 🔗 LinkedIn: Shashank Shekhar Singh\n- 🌐 Website: codegraphcontext.vercel.app\n\n*Contributions and feedback are always welcome! Feel free to reach out for questions, suggestions, or collaboration opportunities.*\n\n---\n\nStar History\nStar History Chart ↗\n \n\n---\n\nFeatures\n- **Code Indexing:** Analyzes code and builds a knowledge graph of its components.\n- **Relationship Analysis:** Query for callers, callees, class hierarchies, call chains and more.\n- **Pre-indexed Bundles:** Load famous repositories instantly with .cgc bundles - no indexing required! (Learn more)\n- **Live File Watching:** Watch directories for changes and automatically update the graph in real-time (cgc watch).\n- **Interactive Setup:** A user-friendly command-line wizard for easy setup.\n- **Dual Mode:** Works as a standalone **CLI toolkit** for developers and as an **MCP server** for AI agents.\n- **Multi-Language Support:** Full support for 23 programming languages.\n- **Flexible Database Backend:** FalkorDB Lite (Default), KuzuDB, LadybugDB, FalkorDB Remote, Nornic DB, or Neo4j (all platforms via Docker/native).\n\n---\n\n🏗️ Architecture & Workflow\n\nCodeGraphContext parses your source code and builds a comprehensive knowledge graph. This graph can be queried directly via the CLI toolkit or exposed to AI assistants through the MCP server.\n\n \n\n---\n\nSupported Programming Languages\n\nCodeGraphContext provides comprehensive parsing and analysis for the following languages:\n\n| | Language | | Language | | Language |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 🐍 | **Python** | 📜 | **JavaScript** | 🔷 | **TypeScript** |\n| ☕ | **Java** | 🔵 | **C** | ➕ | **C++** |\n| #️⃣ | **C#** | 🐹 | **Go** | 🦀 | **Rust** |\n| 💎 | **Ruby** | 🐘 | **PHP** | 🍎 | **Swift** |\n| 🎨 | **Kotlin** | 🎯 | **Dart** | 🐪 | **Perl** |\n| 🌙 | **Lua** | 🚀 | **Scala** | λ | **Haskell** |\n| 💧 | **Elixir** | 📜 | **Emacs Lisp (elisp)** | 🌐 | **HTML** |\n| 🎨 | **CSS** | ⚛️ | **TSX** | ⛓️ | **Solidity** |\n| 🏗️ | **HCL (Terraform/Terragrunt)** | | | | |\n\nEach language parser extracts functions, classes, methods, parameters, inheritance relationships, function calls, and imports to build a comprehensive code graph.\n\n**HCL notes:** .tf, .tfvars and .hcl cover Terraform, OpenTofu and Terragrunt. HCL declares\nrather than calls, so it contributes blocks, variables and cross-configuration imports but no\nfunction calls. See docs/docs/contributing_languages.md § HCL.\n\n**Solidity notes:** .sol uses Tree-sitter via tree-sitter-language-pack (no SCIP). Supports Foundry remappings, modifier invocations, using Lib for T, and emit / custom-error revert as CALLS. See docs/docs/contributing_languages.md § Solidity.\n\n**Kotlin notes:** Annotations are recorded on functions and classes as decorators, retaining\ntheir arguments (@Preview(showBackground = true)). This makes find_dead_code\'s\nexclude_decorated_with usable on Kotlin and Android codebases — for example excluding\nComposable, Preview, Test and Provides. That filter matches on functions, so class-level\nannotations do not affect it. Annotations on interfaces, objects, constructors, parameters and\nproperties are not yet recorded.\n\n---\n\nDatabase Options\n\nCodeGraphContext supports multiple graph database backends to suit your environment:\n\n| Feature | KuzuDB | LadybugDB | FalkorDB Lite | Neo4j / Nornic DB |\n| :--- | :--- | :--- | :--- | :--- |\n| **Typical default** | Cross-platform fallback when FalkorDB Lite is unavailable | Optional embedded backend | **Default on Unix** (Python 3.12+, when falkordblite is installed) | When explicitly configured via cgc config db |\n| **Setup** | Zero-config / Embedded | Zero-config / Embedded | Zero-config / In-process | Docker / External |\n| **Platform** | **All (Windows Native, macOS, Linux)** | **All (Windows Native, macOS, Linux)** | Unix-only (Linux/macOS/WSL) | All Platforms |\n| **Use Case** | Desktop, IDE, Local development | Custom research projects | Specialized Unix development | Enterprise, Massive graphs |\n| **Requirement**| pip install kuzu | pip install ladybug | pip install falkordblite | Neo4j Server / Docker / Nornic Cloud |\n| **Speed** | ⚡ Extremely Fast | ⚡ Fast | 🚀 Scalable | 🌐 Network-dependent |\n| **Persistence**| Yes (to disk) | Yes (to disk) | Yes (to disk) | Yes (server-side) |\n\nEmbedded KuzuDB/LadybugDB default to a **4 GiB** buffer pool (CGC_EMBEDDED_BUFFER_POOL_MB). Set that env var to another MiB value, or 0 for the library default (~80% of system RAM).\n\n---\n\nSCIP indexing (optional)\n\nWhen SCIP_INDEXER=true in your CGC config (~/.codegraphcontext/.env), some languages use external **SCIP** indexers for more accurate calls and inheritance than Tree-sitter heuristics alone.\n\n**C and C++** use **scip-clang**, which requires a **compile_commands.json** file (a JSON compilation database): one entry per translation unit with the real compiler command (include paths, -D defines, -std, etc.). Without it, scip-clang cannot run; CGC logs a warning and **falls back to Tree-sitter** for that repo. Typical ways to produce the file: **CMake** with -DCMAKE_EXPORT_COMPILE_COMMANDS=ON, or wrap your real', 'readme_raw_len': 18076, 'one_line_zh': '将本地代码索引进图数据库，为 AI 助手提供上下文，并配有图形化代码浏览', 'one_line_plain': 'Index local code into a graph database to give AI assistants context, with graphical code…'}, {'id': 5753, 'key': 'skillsmp-com-creators-code-yeongyu-lazycodex-plugins-omo-skills-ultimate-browsing', 'kind': 'skill', 'name': 'ultimate-browsing', 'one_line': "Reaches web pages the user's browser cannot open: WAF-blocked and bot-scored hosts, platform-native readers…", 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/code-yeongyu/lazycodex/plugins-omo-skills-ultimate-browsing', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/code-yeongyu/lazycodex/plugins-omo-skills-ultimate-browsing', 'downloads': None, 'stars': 3760, 'installs': None, 'mentions': 5, 'score': 503.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/code-yeongyu.png?size=64', 'official_desc': "Reaches web pages the user's browser cannot open: WAF-blocked and bot-scored hosts, platform-native readers (Chinese and social platforms), archive snapshots with provenance, and a browser you own for stealth or throwaway profiles. Not for plain search, an unblocked static fetch, or a page the user'", 'stars_own': None, 'stars_creator': 3797, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'code-yeongyu/lazycodex', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '访问用户浏览器打不开的网页，包括被防火墙拦截或经机器人评分的站点。', 'one_line_plain': "The tool reaches web pages a user's browser cannot open, including WAF-blocked and bot-scored hosts, and platform-native readers"}, {'id': 4118, 'key': 'taylorwilsdon-google-workspace-mcp', 'kind': 'mcp', 'name': 'taylorwilsdon/google_workspace_mcp', 'one_line': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace integration covering Calendar, Drive, Gmail, Docs, Forms, Chat, Slides and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search & Drive with AI - Comprehensive Google Workspace MCP Server & CLI Tool', 'url': 'https://github.com/taylorwilsdon/google_workspace_mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/taylorwilsdon/google_workspace_mcp', 'downloads': None, 'stars': 3376, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/taylorwilsdon.png?size=64', 'official_desc': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace integration covering Calendar, Drive, Gmail, Docs, Forms, Chat, Slides and Sheets.', 'stars_own': 3376, 'stars_creator': None, 'stars_own_repo': 'taylorwilsdon/google_workspace_mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Google Workspace MCP Server \n\n \n \n \n \n \n \n\n \n\n*Full natural language control over Google Calendar, Drive, Gmail, Docs, Sheets, Slides, Forms, Tasks, Contacts, and Chat through all MCP clients, AI assistants and developer tools.*\n*Includes a full featured CLI & Code Mode for use with tools like Claude Code and Codex!*\n\n**The most feature-complete Google Workspace MCP server** is in a class of it\'s own: it can do things that Google\'s own tooling and the built in integrations with Claude and ChatGPT can\'t come close to with multi-user support, rich fine-grained editing tools and the most extensive coverage of any Workspace AI integration in existence. \n\nBy leveraging native OAuth 2.1, stateless deployment capability and external auth server & gateway passthrough auth support, it\'s also the only Workspace MCP you can host for your whole organization centrally & securely!\n\nSupports all free Google accounts & Google Workspace plans with expanded app options like Chat & Spaces. Interested in a managed cloud instance? That can be arranged (starting at $5/mo).\n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n---\n\n**See it in action:**\n \n \n \n\n---\n\nWhat It Does\n\nWorkspace MCP connects AI assistants to all twelve major Google Workspace services - 120+ tools behind a single MCP server, with OAuth 2.1 multi-user auth, three progressive tool tiers, read-only mode, a full CLI, and stateless container deployment. It runs locally over stdio for legacy clients and remotely over streamable HTTP with full implementation of the latest MCP spec.\n\nThe README covers just enough to get you running, with extensive documentation on the website:\n\n| Where to go | What you\'ll find |\n|:---|:---|\n| **Quick Start** | Google Cloud setup, credentials, and client connection with screenshots |\n| **Full Documentation** | Every tool, parameter, and auth mode |\n| **Advanced Deployment** | Reverse proxy & nginx config, origin validation, credential store backends (GCS/CMEK), trusted-gateway identity, and the complete environment variable reference |\n| **Client Setup Guides** | Claude Desktop/web Connectors, ChatGPT Developer Mode, and more |\n| **FAQ & Troubleshooting** | Enabling Google APIs, OAuth errors, redirect URIs, Google Chat setup, client quirks |\n\nSecurity & Compliance \n\n \n \n \n\n**For Security Teams**\n\nBy default, this server sends no data anywhere except Google\'s APIs, on behalf of the authenticated user, using your own OAuth client credentials. There is no usage reporting, analytics, license server, or SaaS dependency outside optional OTel support for your own usage.\n\n- **Fully open source** — every line is auditable in this repo\n- **Your OAuth client, your GCP project** — credentials never leave your environment & you control scopes\n- **You control the network** — deploy behind your reverse proxy, in your VPC, on your own terms\n- **Stateless mode** — zero disk writes for locked-down container environments\n- **Sensitive path blocking** — local file reads default to the managed attachment directory, and validate_file_path() still blocks .env* files plus common home-directory credential stores such as ~/.ssh/ and ~/.aws/ even if ALLOWED_FILE_DIRS is broadened\n\nFull dependency tree in pyproject.toml, pinned in uv.lock.\n\n \n \n\n**For Legal & Procurement**\n\nThis project is MIT licensed — not "open core," not "source available," not "free with a CLA." There is no dual licensing, no commercial tier gating features, and no contributor license agreement.\n\n- **Use commercially without restriction** — build products, sell services, deploy internally\n- **Fork, embed, redistribute** — MIT requires only attribution\n- **No CLA** — contributions remain under MIT\n- **No built-in telemetry to disclose** — optional tracing is off unless you configure it\n- **No network effects** — the server never contacts any endpoint you didn\'t configure\n- **Standard dependency licenses** — MIT, Apache 2.0, and BSD throughout the dependency chain; no copyleft, no AGPL\n \n \n \n\nServices\n\n \n \n \n 📧 Gmail \n 15 tools - search, send, draft, labels, filters, attachments \n \n \n 📁 Drive \n 16 tools - search, create, share, import Office files \n \n \n 📅 Calendar \n 7 tools - events, free/busy, Out of Office, Focus Time \n \n \n 📝 Docs \n 19 tools - edit, style, tables, tabs, comments, export \n \n \n \n \n 📊 Sheets \n 14 tools - ranges, tables, formatting, conditional rules \n \n \n 🖼️ Slides \n 7 tools - create, batch update, thumbnails, comments \n \n \n 📋 Forms \n 6 tools - build forms, publish, read responses \n \n \n ✅ Tasks \n 6 tools - tasks & lists with hierarchy \n \n \n \n \n 👤 Contacts \n 8 tools - people, groups, batch operations \n \n \n 💬 Chat \n 6 tools - spaces, messages, search, reactions \n \n \n 🔍 Custom Search \n 2 tools - programmable web search \n \n \n ⚡ Apps Script \n 15 tools - write, deploy, run & debug scripts \n \n \n \n\nEach page lists every tool with its tier, parameters, required scopes, and example prompts. The complete reference covers all twelve in one place.\n\n> 💬 **Google Chat** needs a one-time Chat app configuration and a Workspace account - see the Chat setup FAQ.\n\nQuick Start\n\n> Set credentials → pick a launch command → connect your client. Full walkthrough with screenshots: **workspacemcp.com/quick-start**\n\nYou\'ll need an OAuth client from Google Cloud Console in a project with the Google APIs enabled for the services you plan to use. The docs have one-click enable links for every API plus a single gcloud services enable command that covers them all, and the quick start guide walks through the whole setup in about five minutes.\n\n \n \n \n\n**Confidential Client**\n\n \n\n \n \n\n**OAuth 2.1 (PKCE)**\n\n \n\n \n \n \n\n**Tool tiers** keep context windows lean: core is the essential set, extended adds management operations, complete loads everything. Combine with --tools ..., --read-only, or per-service --permissions, and subtract individual tools with --disabled-tools ... - details in the server modes docs.\n\nConnect Your Client\n\n**Claude Desktop, web & mobile** - run the server in HTTP mode and add it as a **Connector** (Settings → Connectors → Add custom connector). This is the recommended path; the Connector guide has step-by-step screenshots. Legacy stdio configuration remains available for clients without Connector support - see the FAQ.\n\n**Claude Code**\n\n \n\n**ChatGPT** - connect via Developer Mode with the ChatGPT guide.\n\n**VS Code, LM Studio, Open WebUI, and everything else** - any MCP client works over streamable HTTP (recommended) or stdio. Client-specific walkthroughs live in the guides and FAQ.\n\nCLI\n\nworkspace-cli lists and calls tools against a running server with encrypted, disk-backed OAuth token caching - authenticate once, script forever:\n\n \n\nInstall globally with uv tool install . from this repo. ⚠️ Don\'t use uvx workspace-cli - an abandoned PyPI package squats that name.\n\nDeployment & Advanced Configuration\n\nEverything you need to run this in production lives in two places. The documentation covers auth modes and server configuration:\n\n- **OAuth 2.1 multi-user auth** - bearer tokens, required for remote or shared HTTP endpoints\n- **Stateless container mode** - zero disk writes for locked-down deployments\n- **OAuth proxy storage backends** - memory, disk, or Valkey/Redis for distributed setups\n- **External OAuth provider mode** - bring your own auth server, validate bearer tokens only\n- **Service accounts with domain-wide delegation** - per-request user impersonation with an optional domain allowlist\n- **Trusted-gateway identity** - proxy-verified per-user isolation with Pomerium, Cloudflare Access, oauth2-proxy, or any JWKS-verifiable gateway\n- **OpenTelemetry tracing** - optional, off unless you configure an OTLP endpoint\n- **Docker** - docker build -t workspace-mcp . && docker run -p 8000:8000 workspace-mcp\n\nThe **Advanced Deployment guide** covers self-hosting specifics: reverse proxy setup with WORKSPACE_EXTERNAL_URL (including the nginx Origin: null consent workaround, the WORKSPACE_MCP_ALLOW_NULL_ORIGIN_CONSENT escape', 'readme_raw_len': 11461, 'one_line_zh': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace 集成，覆盖 Calendar、Drive、Gmail、Docs、Forms、Chat、Slides 等', 'one_line_plain': 'Access Google Workspace tools including Calendar, Drive, Gmail, Docs, Forms, Chat, and Slides'}, {'id': 5045, 'key': 'mcpjam-inspector', 'kind': 'repo', 'name': 'MCPJam/inspector', 'one_line': 'Testing and evaluation platform to chat, inspect, and debug MCP servers, MCP apps, and ChatGPT apps.', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Testing and evaluation platform to chat, inspect, and debug MCP servers, MCP apps, and ChatGPT apps.', 'url': 'https://github.com/MCPJam/inspector', 'source': 'github', 'source_url': 'https://github.com/MCPJam/inspector', 'downloads': None, 'stars': 2272, 'installs': None, 'mentions': 1, 'score': 107.49, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-10', 'times_seen': 1, 'icon': 'https://github.com/MCPJam.png?size=64', 'official_desc': 'Testing and evaluation platform to chat, inspect, and debug MCP servers, MCP apps, and ChatGPT apps.', 'stars_own': 2272, 'stars_creator': None, 'stars_own_repo': 'MCPJam/inspector', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx @mcpjam/inspector@latest', 'install_src': 'HEAD/README.md', 'install_conf': 'high', 'desc_src': 'github-api', 'readme_body': "MCPJam \n\n \n\n \n Hosted app  · \n Docs  · \n Website \n \n\n \n \n \n\n \n\n**MCPJam** is the open-source **testing & evaluations platform for MCP server developers** who ship. Interactively test tools, prompts, resources, and authorization across 16 client configurations and 170+ models. Run evals on test cases, get insights on what's breaking, and gate regressions to secure every deployment.\n\n🚀 Quick start\n\nOpen the hosted app. No install needed.\n\n👉 **app.mcpjam.com**\n\n... or run MCPJam locally for HTTP/S and local STDIO servers:\n\n \n\n👋🏽 Why MCPJam\n\nClients like ChatGPT, Claude, and Cursor all read your server differently. MCPJam catches those differences, shows where your server breaks, and tells you how to fix it:\n\n- **Inspect & debug**: Explore your server's tools, resources, and prompts in one place. Test prompts from real LLMs, with full visibility & traces on every JSON-RPC message and OAuth exchange.\n- **Cross-client evals**: Score agent behavior across 16 client configurations — ChatGPT, Claude, Cursor, Copilot, and more — to understand how your MCP integrations perform across all users. Track accuracy, latency, and tool-call performance over time.\n- **Security & reliability**: Automate conformance & behavior checks in CI/CD to catch regressions before they reach production.\n\n \n\n \n\n_Trace view: every tool call, agent step, and JSON-RPC message in one timeline._\n\n \n\n🔨 Features\n\n| Capability | What it does |\n| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| **Playground** | Cross-client chat interface that emulates UIs, tool calls, and skills (with full traces). OpenAI Apps SDK and MCP app UIs, text tools, and a Chrome DevTools-style widget emulator. Read more |\n| **OAuth Debugger** | Visualize your OAuth & EMA requests step-by-step to find the source of errors. Guided MCP OAuth conformance checks across protocol versions 03-26, 06-18, 11-25, and 2026-07-28; DCR, client pre-registration, and CIMD. Read more |\n| **Server Debugging** | Manually run tools, resources, templates, prompts, and elicitation flows with full JSON-RPC logs. |\n| **Skills** | Extend models with reusable behaviors in Playground. Local skills are read from your filesystem and never leave your machine; a project can also carry hosted skills, available on accounts where that is enabled. Read more |\n| **Workspaces** | Shared server groups with real-time team sync, so everyone tests the same config. Read more |\n| **Evals** | Test cases with expected tool calls, run across LLMs, with accuracy metrics over time. Read more |\n| **CLI** | Probe servers, run doctor checks, exercise OAuth, and list tools/resources/prompts from your terminal. Read more |\n| **SDK** | Programmatically drive inspections, snapshot capabilities, and assert on tool/resource shapes from your own tests. Read more |\n| **CI/CD** | Run conformance, E2E tests, evals, and OAuth checks on every PR in GitHub Actions or any pipeline. Read more |\n\n---\n\nPlayground\n\nDebug your server against a model using tool calls or in-panel chat, with Chat, Trace, and Raw views. Supports the OpenAI Apps SDK and MCP app UIs, text tools, and a Chrome DevTools-style widget emulator so you can iterate on widgets locally.\n\n- Invoke a tool to render its widget instantly, or drive your server with an LLM.\n- Watch all JSON-RPC and window.openai messages in the logs.\n- Switch the emulator between Desktop, Tablet, and Mobile.\n- Test locale changes, CSP permissions, light/dark mode, hover & touch, and safe-area insets.\n\n \n\nOAuth Debugger\n\nGuided MCP OAuth conformance checks with step-by-step explanations. Test against every version of the OAuth spec (2025-03-26, 2025-06-18, 2025-11-25, and the 2026-07-28 draft), with support for client pre-registration, Dynamic Client Registration (DCR), and Client ID Metadata Documents (CIMD).\n\n \n\nServer Debugging\n\nEverything you need to test an MCP server by hand: run tools, resources, resource templates, prompts, and elicitation flows with full JSON-RPC observability. Every feature of the original inspector, and more.\n\n \n\nSkills\n\nUse Skills in Playground to extend models with reusable behaviors. Local skills are read from your filesystem and never leave your machine; a project can also carry hosted skills, available on accounts where that is enabled. Read more\n\nWorkspaces\n\nGroup servers into shared workspaces with real-time team sync, so everyone on your team tests against the same configuration. Read more\n\nEvals\n\nDefine test cases with expected tool calls and run them across multiple LLMs. Track accuracy over time to catch regressions early and improve your server with every iteration. Read more\n\nCLI\n\nRun MCPJam from the terminal for fast local dev loops and CI. Probe servers, run OAuth checks, inspect tools and resources, and execute evals without leaving your shell. Read more\n\nSDK\n\nProgrammatic access to MCPJam for custom tooling, scripting, and integrations. Build your own workflows on top of MCPJam's inspection and evaluation primitives. Read more\n\nCI/CD\n\nWire MCPJam into GitHub Actions, GitLab CI, or any CI system to run conformance, E2E tests, and evals on every PR — and catch MCP server regressions before they ship. Read more\n\n🔌 Install\n\nMCPJam Inspector runs three ways: a hosted web app, a desktop app for Mac and Windows, or via your terminal. The web app is HTTPS-only and has no install. Terminal and Desktop support HTTP/S and local STDIO servers.\n\n \n \n\n> Node.js 20+ is only required for the terminal install (npx). The hosted and desktop apps have no local runtime requirements.\n\n**Hosted web app**: Open app.mcpjam.com. No install, always latest, and you can share server links with teammates like a Google Doc. HTTPS server URLs only; no STDIO, tunneling, skills, or tasks (those need the local inspector). Hosted docs\n\n**Desktop app**: Download the installer. Supports HTTP/S and local STDIO, no Node.js required.\nInstall Mac · Install Windows\n\n**Terminal**: Supports HTTP/S and local STDIO. Open the printed localhost URL after it starts.\n\n \n\nThe launcher opens a private access link automatically. To sign in from another browser, use the link printed in your terminal. Docker users can retrieve it with docker logs . Keep it private; a new link is generated on restart unless MCPJAM_SESSION_TOKEN is configured. See the network access instructions for remote addresses and remapped ports.\n\n**Docker**: Bound to localhost for security. Available at http://127.0.0.1:6274. Always use -p 127.0.0.1:6274:6274 (not -p 6274:6274) to keep the inspector local-only. On macOS/Windows, reach host MCP servers via http://host.docker.internal:PORT.\n\n \n\n👨\u200d💻 Contributing\n\nWe're grateful you're considering it. Read the contributing guide to get started, and come say hi in Discord.\n\n🌍 Community & links\n\nWebsite · Blog · Pricing · Docs · Discord · 𝕏 (Twitter) · LinkedIn\n\n📄 License\n\nLicensed under the **Apache License 2.0**. See LICENSE.", 'readme_raw_len': 7082, 'one_line_zh': '用于聊天、检查与调试 MCP 服务器、MCP 应用及 ChatGPT 应用的测试评估平台', 'one_line_plain': 'Let users chat, inspect, and debug MCP servers, apps, and ChatGPT apps'}, {'id': 867, 'key': 'chigwell-telegram-mcp', 'kind': 'mcp', 'name': 'chigwell/telegram-mcp', 'one_line': '🐍 🏠 - Telegram API integration: access user data, manage dialogs (chats, channels, groups), retrieve and send…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Telegram MCP server powered by Telethon to let MCP clients read chats, manage groups, and send/modify messages, media, contacts, and settings.', 'url': 'https://github.com/chigwell/telegram-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/chigwell/telegram-mcp', 'downloads': None, 'stars': 1857, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/chigwell.png?size=64', 'official_desc': '🐍 🏠 - Telegram API integration: access user data, manage dialogs (chats, channels, groups), retrieve and send messages, and handle read status.', 'stars_own': 1857, 'stars_creator': None, 'stars_own_repo': 'chigwell/telegram-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'A Telegram integration for Claude, Cursor, and other MCP-compatible clients. It exposes Telegram account, chat, message, contact, media, folder, and admin operations through the Model Context Protocol using Telethon.\n\n🤖 MCP in Action\n\nBasic Telegram MCP usage in Claude:\n\n \n\nAsking Claude to analyze chat history and send a response:\n\n \n\nMessage sent successfully:\n\n \n\nContents\n\n- Skills & Practical Workflows\n- What It Can Do\n- Requirements\n- Quick Start\n- MCP Client Configuration\n- Multi-Account Setup\n- Device Identity\n- Expected Account Check\n- Proxy Support\n- File Path Security\n- Chat Access Privacy (Allowlist)\n- Docker\n- Development\n- Security Notes\n- Troubleshooting\n- License\n\nSkills & Workflows\n\nLooking for ready-to-use workflows, prompt examples, or integration recipes?\nExplore the **Skills Documentation** for step-by-step guides on:\n- Summarizing unread messages safely\n- Drafting replies without sending\n- Triaging action items & urgent requests\n- Searching chat history and expanding context\n\nWhat It Can Do\n\nThe server currently includes 80+ MCP tools grouped into these areas:\n\n- **Accounts:** list configured accounts and route tool calls by account label.\n- **Chats and groups:** list chats, inspect metadata, create groups/channels, join or leave chats, invite or remove users, manage admins, bans, default permissions, slow mode, topics, invite links, common chats, read receipts, and message links.\n- **Messages:** send, schedule, edit, delete, forward, pin, unpin, mark read, reply, search, inspect context, create polls, manage reactions, inspect inline buttons, and press inline callbacks. send_message, reply_to_message, and edit_message support classic formatting (parse_mode=\'md\'/\'html\') and server-side rich formatting (parse_mode=\'rich\'/\'rich_markdown\'/\'rich_html\' — full Markdown/HTML with tables, headings, formulas, and collapsible sections). Rich modes require Telegram Premium on the account; Premium is re-checked on every call, and without it nothing is sent — the tool returns a structured telegram_premium_required result so the agent can reformat with classic modes and retry. send_message, reply_to_message, and edit_message also accept format_date to render a date as a tappable chip.\n\nget_message_reactions returns an empty list for a message with no reactions. To reuse a custom reaction, pass the returned custom: value to send_reaction.\n\nvote_poll(chat_id, message_id, option_indices, account=None) votes in an existing poll.\nRead the poll with get_messages first, then select zero-based answer indices: [0]\nfor the first answer, or [0, 2] for the first and third in a multiple-choice poll.\nThis may replace an existing vote; empty selections, duplicate or out-of-range\nindices, closed polls, and multiple answers in single-choice polls are rejected.\nTelegram enforces voting permissions and restrictions on changing quiz votes.\n\n- **Contacts:** list, search, add, delete, block, unblock, import, export, inspect direct chats, find recent contact interactions, and remember contacts by the names you actually use (see below).\n\nRemembered contacts\n\nset_contact_alias teaches the server what you call someone, and every tool that takes a chat_id understands it from then on — send_message("андрей бекендер", ...) just works. A contact can carry any number of aliases, which is how tags work: save both андрей бекендер and бекендер for the same person and either resolves.\n\n**Only an exact saved wording ever sends.** Similar wording (Андрею бекендеру for a saved андрей бекендер) is matched too, but only to *suggest*: the tool sends nothing and asks you to confirm the contact by name. This is deliberate — Лена/Леня and Иван/Иванов differ exactly as much as a case ending does, so a matcher confident enough to handle declensions is also confident enough to message the wrong person whenever the one you meant is not saved yet. Confirming saves that wording as its own alias, so each new phrasing costs one yes/no the first time and nothing ever again. Set TELEGRAM_CONTACT_FUZZY=0 to drop the suggestions too.\n\nWhen a reference is unknown, resembles one contact, matches several, or points at a contact that no longer resolves, tools send nothing and return a structured instruction telling the agent exactly what to ask you, to save the answer with set_contact_alias, and to retry once. list_contact_aliases shows one row per person with all their aliases (use it to spot a wrong memory), delete_contact_alias forgets one, and repointing an alias at someone else requires replace=True. The save path itself refuses a target it would have to guess at: contacts are saved by @username, phone, numeric ID, or an alias already confirmed for them.\n\nAliases live in ${XDG_STATE_HOME:-~/.local/state}/telegram-mcp/aliases.json (owner-only, written atomically); TELEGRAM_ALIASES_FILE overrides the path, and a pre-existing aliases.json next to the code is still read as a fallback.\n- **Media:** send files, download media, upload files, send voice notes, stickers, GIFs, inspect message media, and transcribe voice messages/video notes (see below).\n\nVoice transcription\n\ntranscribe_voice(chat_id, message_id, engine=None) turns a voice message or video note into text. Four engines are available:\n\n- groq (default): uploads the recording to Groq\'s hosted whisper-large-v3-turbo. Leaves the server and costs a download+upload per call, but doesn\'t drop the recording\'s last few words the way native transcription does. Requires GROQ_API_KEY. Groq caps the size of a single upload, so a recording above TELEGRAM_TRANSCRIBE_GROQ_MAX_MB (default 25, the free-tier limit) is refused locally with a too_large error naming its size instead of being downloaded and rejected by the API. Raise the limit if your Groq tier allows bigger files, or transcribe that message with engine=\'telegram\', which has no such cap.\n- telegram: native Telegram Premium transcription (messages.TranscribeAudioRequest). Free and never leaves Telegram, but empirically drops the last speech segment in roughly 2 of 3 recordings and requires Telegram Premium on the account. Long recordings come back pending and are polled automatically.\n- openai: any OpenAI-compatible /audio/transcriptions endpoint — OpenAI itself, a self-hosted Parakeet or speaches server, LocalAI, a vLLM Whisper deployment, and so on. Set TELEGRAM_TRANSCRIBE_OPENAI_URL to the API base URL (e.g. https://api.openai.com/v1; a full .../audio/transcriptions URL also works), TELEGRAM_TRANSCRIBE_OPENAI_API_KEY for the bearer token (optional for keyless local servers), and TELEGRAM_TRANSCRIBE_OPENAI_MODEL (default whisper-1). Size cap: TELEGRAM_TRANSCRIBE_OPENAI_MAX_MB (default 25). For Parakeet use TELEGRAM_TRANSCRIBE_OPENAI_URL=http://localhost:5092/v1, the API key only if the server sets PARAKEET_API_KEY, and set TELEGRAM_TRANSCRIBE_LANGUAGE for anything that isn\'t English — Parakeet assumes en when no language is sent.\n- whisper: a local faster-whisper model loaded inside the MCP server process. The audio never leaves the machine. Install the extra with pip install \'telegram-mcp[whisper]\' (or uv sync --extra whisper). TELEGRAM_TRANSCRIBE_WHISPER_MODEL picks the model (default small; e.g. large-v3-turbo for better quality), TELEGRAM_TRANSCRIBE_WHISPER_DEVICE (auto/cpu/cuda), TELEGRAM_TRANSCRIBE_WHISPER_COMPUTE_TYPE (e.g. int8 on CPU) and TELEGRAM_TRANSCRIBE_WHISPER_MODEL_DIR (where models are downloaded) tune it. The model is loaded once on first use and recordings are transcribed one at a time. Not available in the Alpine Docker image; use openai against a Parakeet or other OpenAI-compatible server next to the container instead.\n\nTELEGRAM_TRANSCRIBE_LANGUAGE (ISO-639-1, e.g. nl) is passed as a language hint to every engine except telegram; unset, the engines auto-detect. TELEGRAM_TRANSCRIBE_TIMEOUT (default 120 seconds) bounds a single request to the HTTP engines (groq, openai).\n\nThe engine is chosen per call via the engine argument, or otherwise defaults to TELEGRAM_TRANSCRIBE_ENGIN', 'readme_raw_len': 38333, 'one_line_zh': '🐍 🏠 - Telegram API 集成：访问用户数据，管理对话（聊天、频道、群组），获取并发送…', 'one_line_plain': 'Access Telegram user data and manage chats, channels, and groups'}, {'id': 1828, 'key': 'stack-chan-stack-chan', 'kind': 'mcp', 'name': 'stack-chan/stack-chan', 'one_line': '📇 📟 - M5Stack-based robot with an MCP server for AI-controlled interactions and emotions', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A JavaScript-driven M5Stack-embedded super-kawaii robot.', 'url': 'https://github.com/stack-chan/stack-chan', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/stack-chan/stack-chan', 'downloads': None, 'stars': 1796, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/stack-chan.png?size=64', 'official_desc': '📇 📟 - M5Stack-based robot with an MCP server for AI-controlled interactions and emotions.', 'stars_own': 1796, 'stars_creator': None, 'stars_own_repo': 'stack-chan/stack-chan', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Stack-chan\n\n \n \n\n日本語\n\n \n\nStack-chan is an open-source, JavaScript-driven, super-kawaii robot built with M5Stack.\n\nThis repository provides firmware with M5StackChan CoreS3 as its standard configuration, user applications called MODs, browser development tools, cases, and schematics.\n\n- Video (with English subtitles): https://youtu.be/fZb_mF08xV0\n- Official hashtag: #stackchan | #ｽﾀｯｸﾁｬﾝ (JP)\n\nGetting started\n\nIf this is your first Stack-chan, start with a preassembled M5StackChan and the browser tools.\n\n1. Get an M5StackChan\n\nThe standard M5StackChan AI Desktop Robot (K151) package is enough to get started.\nThe joystick remote controller is optional.\n\n- Worldwide: M5Stack Official Store\n- Japan: M5StackChan\n- Japan: M5StackChan with joystick remote controller\n\n2. Flash the firmware\n\nConnect M5StackChan to a computer with a data-capable USB cable, then open the Web firmware installer in Chrome or Edge.\nSelect "M5StackChan CoreS3" as the device.\nYou do not need to install the Moddable SDK or ESP-IDF.\n\n> [!IMPORTANT]\n> Flashing this repository\'s firmware replaces the factory firmware supplied by M5Stack.\n> To restore it, follow the restore procedure in the M5Stack product documentation and use M5Burner.\n\n \n\n3. Try a MOD\n\nFind published MODs and mini apps in the MOD Gallery.\nChoose a MOD, then run it in the simulator or install it on a device.\nBlock-based samples can be opened directly in the block editor and changed.\n\n \n\nBrowser tools\n\nUse the Stack-chan browser tools to set up a device, change its preferences, and create MODs in a browser.\n\n \n\n| Tool | Purpose |\n| --- | --- |\n| Firmware installer | Flash firmware to a supported M5Stack over USB |\n| Preferences | Change Wi-Fi and device preferences over BLE |\n| MOD Gallery | Find, try, and install published MODs |\n| Block editor | Create MODs with Blockly and run them in the simulator or on a device |\n| Shape face editor | Arrange eyes and a mouth to create a custom Face |\n| Simulator | Run MODs with the WebAssembly firmware and a 3D model |\n| MediaPipe BLE tracking | Send tracked face and hand movement over BLE |\n\n \n\nSupported hardware\n\nThe distributed firmware supports M5Stack, M5Stack Core2, M5Stack CoreS3, and M5StackChan CoreS3.\nM5StackChan CoreS3 is the standard configuration and the target used for physical-device release validation.\n\nFirmware for Stack-chan RT and Takao Core2 + SG90 can be built from source.\nSee the firmware guide for target-specific commands and constraints.\n\nTo build the hardware yourself, see the case guide for parts and assembly instructions.\nSee the schematics for board information.\n\nRepository contents\n\n- firmware: Host firmware, modules, MODs, and development scripts\n- web: Firmware installer, preferences, editors, Gallery, and simulator\n- case: Case models for 3D printing\n- schematics: Schematics and board layouts\n- docs: Roadmap, specifications, operations guides, and release notes\n\nDevelopment\n\n- Firmware development\n- MOD development\n- Firmware API\n- Contribution guide\n- Latest release\n- Development roadmap\n\nContributing\n\nFeature requests, bug reports, and pull requests are welcome.\nRead the contribution guide, or open an issue.\n\nTo support the project financially, visit the GitHub Sponsors page.\n\nLicense\n\nThe resources in this repository are distributed under the Apache License 2.0.\nSee LICENSE.\n\nBibTeX', 'readme_raw_len': 3347, 'one_line_zh': '基于 M5Stack 的机器人，内置 MCP 服务器，可由 AI 控制交互与情绪表达', 'one_line_plain': 'M5Stack-based robot with an MCP server for AI-controlled interactions and emotions'}, {'id': 1516, 'key': 'lpigeon-ros-mcp-server', 'kind': 'mcp', 'name': 'lpigeon/ros-mcp-server', 'one_line': '🐍 🏠 🍎 🪟 🐧 - Control robots by converting natural language commands into ROS or ROS2 control commands', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/robotmcp/ros-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/lpigeon/ros-mcp-server', 'downloads': None, 'stars': 1501, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/lpigeon.png?size=64', 'official_desc': 'server Natural voice conversations with Claude Code (and other MCP capable agents)\n\n \n \n \n\nVoiceMode enables natural voice conversations with Claude Code. Voice isn't about replacing typing - it's about being available when typing isn't.\n\n**Perfect for:**\n\n- Walking to your next meeting\n- Cooking while debugging\n- Giving your eyes a break after hours of screen time\n- Holding a coffee (or a dog)\n- Any moment when your hands or eyes are busy\n\nSee It In Action\n\n \n\nQuick Start\n\n**Requirements:** Computer with microphone and speakers\n\nOption 1: Claude Code Plugin (Recommended)\n\nThe fastest way for Claude Code users to get started:\n\n \n\nOption 2: Python installer package\n\nInstalls dependencies and the VoiceMode Python package.\n\n \n\nFor manual setup, see the Getting Started Guide.\n\nFeatures\n\n- **Natural conversations** - speak naturally, hear responses immediately\n- **Works offline** - optional local voice services (Whisper STT, Kokoro TTS)\n- **Low latency** - fast enough to feel like a real conversation\n- **Smart silence detection** - stops recording when you stop speaking\n- **Privacy options** - run entirely locally or use cloud services\n\nCompatibility\n\n**Platforms:** Linux, macOS, Windows (native or WSL), NixOS\n**Python:** 3.10-3.14\n\nConfiguration\n\nVoiceMode works out of the box. For customization:\n\n \n\nSee the Configuration Guide for all options.\n\nPermissions Setup (Optional)\n\nTo use VoiceMode without permission prompts, add to ~/.claude/settings.json:\n\n \n\nSee the Permissions Guide for more options.\n\nLocal Voice Services\n\nFor privacy or offline use, install local speech services:\n\n- **Whisper.cpp** - Local speech-to-text\n- **Kokoro** - Local text-to-speech with multiple voices\n\nThese provide the same API as OpenAI, so VoiceMode switches seamlessly between them.\n\nInstallation Details\n\n \n System Dependencies by Platform \n\nUbuntu/Debian\n\n \n\n**WSL2 users**: The pulseaudio packages above are required for microphone access.\n\nFedora/RHEL\n\n \n\nmacOS\n\n \n\nNixOS\n\n \n\n \n\n \n Alternative Installation Methods \n\nFrom source\n\n \n\nNixOS system-wide\n\n \n\n \n\nTroubleshooting\n\n| Problem | Solution |\n|---------|----------|\n| No microphone access | Check terminal/app permissions. WSL2 needs pulseaudio packages. |\n| UV not found | Run curl -LsSf https://astral.sh/uv/install.sh \\| sh |\n| OpenAI API error | Verify OPENAI_API_KEY is set correctly |\n| No audio output | Check system audio settings and available devices |\n\nSave Audio for Debugging\n\n \n\nDocumentation\n\n- Getting Started - Full setup guide\n- Configuration - All environment variables\n- Whisper Setup - Local speech-to-text\n- Kokoro Setup - Local text-to-speech\n- Development Setup - Contributing guide\n\nFull documentation: voicemode.dev\n\nLinks\n\n- **Website**: voicemode.dev\n- **GitHub**: github.com/mbailey/voicemode\n- **PyPI**: pypi.org/project/voice-mode\n- **YouTube**: @getvoicemode\n- **Twitter/X**: @getvoicemode\n- **Newsletter**: \n\nLicense\n\nMIT - A Failmode Project\n\n---\nmcp-name: dev.voicemode/voicemode", 'readme_raw_len': 2982, 'one_line_zh': '语音交互：语音转文字、文字转语音，并可经本地服务进行实时语音对话。', 'one_line_plain': 'Voice interaction lets users perform speech-to-text, text-to-speech and real-time voice conversations locally'}, {'id': 4192, 'key': 'caol64-wenyan-mcp', 'kind': 'mcp', 'name': 'caol64/wenyan-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Format Markdown articles with Wenyan and publish them to WeChat Official Accounts', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '文颜 MCP Server 可以让 AI 自动将 Markdown 文章排版后发布至微信公众号。', 'url': 'https://github.com/caol64/wenyan-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/caol64/wenyan-mcp', 'downloads': None, 'stars': 1345, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/caol64.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Format Markdown articles with Wenyan and publish them to WeChat Official Accounts.', 'stars_own': 1345, 'stars_creator': None, 'stars_own_repo': 'caol64/wenyan-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '文颜 MCP Server\n\n \n \n \n \n \n\n简介\n\n**文颜（Wenyan）** 是一款多平台 Markdown 排版与发布工具，支持将 Markdown 一键转换并发布至：\n\n- 微信公众号\n- 知乎\n- 今日头条\n- 以及其它内容平台（持续扩展中）\n\n文颜的目标是：**让写作者专注内容，而不是排版和平台适配**。\n\n文颜的不同版本\n\n文颜目前提供多种形态，覆盖不同使用场景：\n\n- macOS App Store 版 - MAC 桌面应用\n- 跨平台桌面版 - Windows/Linux\n- CLI 版本 - 命令行 / CI 自动化发布\n- 👉 MCP 版本 - 本项目\n\n文颜 MCP Server 是什么？\n\n简单来说，它打通了“AI 写作”与“公众号发文”的通道。\n\n基于 MCP 协议，Claude Desktop 等 AI 客户端现在可以直接调用文颜（Wenyan）的排版引擎。写完文章后，不需要再去第三方编辑器里来回复制粘贴，直接让 AI 帮你排版并塞进微信草稿箱。\n\n**核心特性：**\n\n- **绕过排版工具**：AI 生成的 Markdown 直接转成微信富文本并上传，省去中间步骤。\n- **对话式排版**：直接打字跟 AI 说“换个橙色风格主题”，样式自动生效。\n- **不出窗口完成闭环**：在同一个聊天框里，顺滑搞定“想选题 -> 写文章 -> 调排版 -> 存草稿”的所有操作。\n\n**实战演示**：\n* 让 AI 帮你管理公众号的排版和发布\n* Moraya MCP 使用案例：微信公众号全托管\n\n功能特性\n\n- 一键发布 Markdown 到微信公众号草稿箱\n- 自动上传本地图片与封面\n- 支持远程 Server 发布（绕过 IP 白名单限制）\n- 内置多套精美排版主题\n- 支持自定义主题\n- 提供标准 MCP Tool 接口\n- 支持 AI 自动调用：\n - 渲染 Markdown\n - 主题管理\n - 发布草稿\n\n快速开始\n\n**安装**\n\n \n\n**Claude Desktop 配置 (claude_desktop_config.json)：**：\n\n \n\n基本用法\n\n列出主题\n\n对AI说\n\n> 目前你可以使用哪些公众号主题？\n\nAI回复：\n\n \n\n注册主题\n\n对AI说\n\n> 帮我把这个css(https://wenyan.yuzhi.tech/manhua.css)注册为一个新的公众号主题，名称为：xiuluochang\n\nAI回复：\n\n \n\n删除主题\n\n对AI说\n\n> 把名为“xiuluochang”的公众号主题删除\n\nAI回复：\n\n \n\n发布文章\n\n对AI说\n\n> 使用phycat主题将这篇文章发布到微信公众号：./tests/publish.md\n\nAI回复：\n\n \n\n概念\n\n环境变量配置\n\n> [!IMPORTANT]\n>\n> 请确保 MCP 启动时已配置如下环境变量，否则上传接口将调用失败。\n\n- WECHAT_APP_ID\n- WECHAT_APP_SECRET\n\n微信公众号 IP 白名单\n\n> [!IMPORTANT]\n>\n> 请确保运行文颜的机器 IP 已加入微信公众号后台的 IP 白名单，否则上传接口将调用失败。\n\n配置说明文档：https://yuzhi.tech/docs/wenyan/upload\n\n文章格式\n\n为了正确上传文章，每篇 Markdown 顶部需要包含一段 frontmatter：\n\n \n\n字段说明：\n\n| 字段 | 必填 | 说明 |\n| ---------- | -- | ----------------- |\n| title | ✅ | 文章标题 |\n| cover | ❌ | 封面图片（本地路径或网络 URL） |\n| author | ❌ | 作者 |\n| source_url | ❌ | 原文链接 |\n| type | ❌ | 文章类型，设为 image 表示图片消息（小绿书） |\n| image_list | ❌ | 图片路径列表（小绿书专用，最多 20 张） |\n| need_open_comment | ❌ | 是否打开评论 |\n| only_fans_can_comment | ❌ | 是否仅粉丝可评论 |\n\n说明：\n\n* 如果未指定 cover，将自动使用正文第一张图片作为封面\n* cover 支持本地路径和网络 URL\n* type 和 image_list 用于图片消息发布\n\n**示例文章**\n\n文内图片和文章封面\n\n把文章发布到公众号之前，文颜会按照微信要求自动处理文章内的所有图片，将其上传到公众号素材库。目前文颜对于以下图片都能很好的支持：\n\n- 本地硬盘绝对路径（如：/Users/xxx/image.jpg）\n- 网络路径（如：https://example.com/image.jpg）\n- 当前文章的相对路径（如：./assets/image.png）\n\n发布图片消息（小绿书）\n\n发布图片消息与发布图文消息的区别在于 frontmatter 中需要指定图片列表。支持两种方式：\n\n方式一：使用 type: image（推荐）\n\n在 frontmatter 中设置 type: image，CLI 会自动从正文中提取所有图片：\n\n \n\n方式二：手动指定 image_list\n\n直接在 frontmatter 中列出图片路径：\n\n \n\nimage_list 最多 20 张，首张即为封面。\n\nServer 模式\n\n相较于纯本地运行的**本地模式（Stdio Mode）**，wenyan-mcp还提供了 **远程客户端模式（Client–Server Mode）**。两种模式运行效果完全一致，你可以根据运行环境和网络条件选择最合适的方式。\n\n在本地模式下，MCP 直接调用微信公众号 API 完成图片上传和草稿发布。\n\n \n\n在远程客户端模式下，MCP 作为客户端，将发布请求发送到部署在云服务器上的 Wenyan Server，由 Server 完成微信公众号 API 调用。\n\n \n\n**适用于：**\n\n* 无本地固定 IP，需频繁添加IP 白名单的用户\n* 需团队协作的用户\n* 支持 CI/CD 自动发布\n* 支持 AI Agent 自动发布\n\n**Server 模式部署**\n\n**Claude Desktop 配置：**：\n\n \n\n多公众号发布（进阶）\n\n> [!IMPORTANT]\n>\n> 必须使用server模式才能使用多公众号发布功能。\n\n步骤：\n\n- 首先部署server\n- 在server端配置多个微信凭据，参考文档\n- 让MCP 客户端连接server\n\n对AI说\n\n> 使用phycat主题将这篇文章发布到微信公众号(your_app_id)：./tests/publish.md\n\nDocker 部署\n\n适合不希望安装 Node.js 环境的用户。\n\n \n\n* **Claude Desktop 配置：**：\n\n \n\n> **Docker 配置特别说明：**\n>\n> * **挂载目录 (-v)**：必须将宿主机的文件/图片目录挂载到容器内的 /mnt/host-downloads。\n> * **环境变量 (HOST_FILE_PATH)**：必须与宿主机挂载的文件/图片目录路径保持一致。\n> * **原理**：你的 Markdown 文件/文章内所引用的本地图片应放置在该目录中，Docker 会自动将其映射，使容器能够读取并上传。\n\n如何调试\n\n推荐使用官方 Inspector 进行调试：\n\n \n\n启动成功出现类似提示：\n\n \n\n访问以上链接即可打开调试页面。\n\n \n\n1. 正确填写启动命令\n2. 添加环境变量\n3. 点击 Connect\n4. 选择 Tools -> List Tools\n5. 选择要调试的接口\n6. 填入参数并点击 Run Tool\n7. 查看完整参数\n\n赞助\n\n如果你觉得文颜对你有帮助，可以给我家猫咪买点罐头 ❤️\n\nhttps://yuzhi.tech/sponsor\n\nLicense\n\nApache License Version 2.0', 'readme_raw_len': 3399, 'one_line_zh': '📇 🏠 🍎 🪟 🐧 - 用 Wenyan 排版 Markdown 文章，并发布到微信公众号', 'one_line_plain': 'Convert Markdown articles into formatted posts for WeChat Official Accounts'}, {'id': 5218, 'key': 'autonomous-ai-openharness', 'kind': 'repo', 'name': 'autonomous-ai/openharness', 'one_line': 'The ultimate harness for coding agents and beyond. All your agents. All your machines. One command center. Start with code, then follow your curiosity and build across disciplines: CAD, circuits, robots, games and music.', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'The ultimate harness for coding agents and beyond. All your agents. All your machines. One command center. Start with code, then follow your curiosity and build across disciplines: CAD, circuits, robots, games and music.', 'url': 'https://github.com/autonomous-ai/openharness', 'source': 'github', 'source_url': 'https://github.com/autonomous-ai/openharness', 'downloads': None, 'stars': 1320, 'installs': None, 'mentions': 1, 'score': 105.45, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-12', 'times_seen': 2, 'icon': 'https://github.com/autonomous-ai.png?size=64', 'official_desc': 'The ultimate harness for coding agents and beyond. All your agents. All your machines. One command center. Start with code, then follow your curiosity and build across disciplines: CAD, circuits, robots, games and music.', 'stars_own': 1320, 'stars_creator': None, 'stars_own_repo': 'autonomous-ai/openharness', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Harness like a boss. \n\n \n The ultimate harness for coding agents and beyond. \n Claude Code at work. Codex at home. Hermes in the cloud. One command center. \n Start with code. Then follow your curiosity and build across disciplines: CAD, circuits, robots, games and music.\n \n\n \n Download ·\n How it works ·\n Beyond code ·\n Harnesses ·\n Device \n \n\nEvery agent, side by side\n\nClaude Code, Codex, Cursor, OpenCode, Devin, Amp, Copilot and seven more.\n\n \n\nEvery machine, side by side\n\nYour laptop, home server and GPU box in one window. Link each one with a password.\nNo SSH keys. No Tailscale. No port forwarding.\n\n \n\nKeyboard first\n\n⌘P finds any harness on any machine. ⇧⌘I jumps to the harness waiting on you. ⌘D splits. Every key remaps.\n\n \n\nEnd-to-end encrypted\n\nCode, keys and keystrokes are sealed on your machine. The relay forwards bytes it can't read.\n\n \n\nFast and light\n\nA native app, not Electron. Close it and your agents keep working.\n\nIdle measurements on an M2 Max:\n\n- **⌘N, ⌘O, ⌘T UI:** 11–13 ms median, 15–18 ms p95.\n- **Local terminal echo:** 1.1 ms median, 7.2 ms p95, excluding UI rendering.\n- **Desktop resources:** ~0.1% of one CPU core and 304 MiB, including terminal rendering and scrollback; agent CLIs and the daemon are excluded.\n\nDesktop results use a Release fixture with 16 terminals and 1,000 scrollback lines each.\nSee the workflow and resource benchmarks\nand remote P2P, TURN and relay results\nfor workloads, slow tails, connection failures and raw data.\n\nBuilt the way developers work\n\n- **Real terminals.** Every agent runs in its own tmux pane. Scrollback, colors and keys just work.\n- **Your CLIs, as they are.** Harness never wraps an agent. It reads transcripts and uses the vendor's own hooks.\n- **A worktree per harness.** Start an agent on its own branch. Your working copy stays clean.\n- **Remap every key.** One JSONC file, reloaded on save. Chords up to four strokes.\n- **Local models.** Run open-weight models on your own machines with Grid, Ollama, MLX-LM and vLLM.\n- **Bytes, not pixels.** Remote terminals stream text peer to peer. No remote desktop.\n- **Open source, all of it.** App, CLI, daemon, relay and device, in this repo.\n\nHow it works\n\nIn the workspace, a **harness** is one running session of an agent such as Codex\nor Claude Code, with its own conversation and working context. A **tab** groups\nharnesses. Use **New Harness** to start one and **New Tab** to group work.\nHarnesses in the same tab can message each other with harness send: **Agent Messages**,\nan experiment of your account, off by default and turned on in Settings ▸ Experimental.\n\nThe Store offers **harnesses** with instructions, tools, and optional viewers for\nspecific crafts. Install a harness, then start it in your workspace. See the\nNaming System for the complete naming rules.\n\nOne daemon per machine runs your agents in tmux. It dials out, so no machine opens a port.\n\n \n\nEvery path is sealed end to end: ChaCha20-Poly1305, X25519 session keys, pinned Ed25519 identities.\nHarness picks the best path on its own.\n\n**Direct.** A WebRTC channel between your machines. No server in the path.\n\n \n\n**Through Cloudflare.** When a firewall blocks the direct path, the same encrypted channel runs over\nCloudflare's TURN network. Harness keeps trying for a direct path.\n\n \n\n**Through our relay.** A fallback while WebRTC negotiates. The relay holds no keys and forwards ciphertext.\n\n \n\nThe architecture guide has the details.\n\n \n\nGet started\n\n**Download the app** for macOS or Linux.\n\nAdd a machine. Run this on it, then **Machines → Link Machine** in the app:\n\n \n\n**In a terminal:** the same line installs hn — tmux's keys and your ~/.tmux.conf, with every\nharness on every machine (hn). The first time, hn signs in and connects the\ncomputer:\n\n \n\n**For agents:** Claude Code and Codex can start, wait for and read other harnesses with the harness skill\n(harness.autonomous.ai/agents, which has the Codex lines too):\n\n \n\n \n Build from source \n\nNeeds Node.js 20+, tmux, Xcode and Flutter 3.47+ / Dart 3.13+:\n\n \n\nmake install-cli installs this checkout's CLI and restarts the local daemon. See the\ndevelopment guide.\n\n \n\n \n\nBeyond code: Build across disciplines\n\n> “World-class entrepreneurs are polymaths.” — Peter Thiel\n\nCoding agents can build far more than software. Give one a harness and it works with the real\ntools of a craft. You steer in a live viewer. Every clip below is a real session.\n\nBeyond code: Design\n\n**Blender.** Ask for a lamp and the sliders that matter. Turn them and Blender rebuilds the geometry. Keep the versions you love.\n\n \n\nBeyond code: Circuits\n\n**CircuitJS.** Build a filter. Change one resistor. Overlay the new trace, measure the difference and keep both.\n\n \n\nBeyond code: Robotics\n\n**MuJoCo.** Pin a moment in a robot's run. Shove it with 100 N. Watch two futures split.\n\n \n\nBeyond code: Games\n\n**Godogen.** Your agent makes a playable game. Miss a jump, rewind, try again. Pin the moment so the agent sees what you mean.\n\n \n\nBeyond code: Music\n\n**Strudel.** The track is code you can perform. Bring voices in and out, mark the good parts, keep the WAV. Hear it.\n\n \n\nBeyond code: Chemistry\n\n**RDKit.** Turn a bond and watch the molecule move. Follow the real energy curve. Keep the pose worth a closer look.\n\n \n\nBeyond code: Documents\n\n**Typst.** Your agent writes a real PDF. Circle a detail, quote a line, leave a note. The next draft answers it.\n\n \n\nDomain-specific harnesses (DSH)\n\nCode is the common medium. Geometry scripts make parts. Netlists make boards. Animation code makes film.\n\nA **domain-specific harness** turns a coding agent into a specialist. It brings the craft's\ninstructions and skills, a pinned toolchain, a project template, checks and a **live viewer**.\nYou chat on one side. The board, the part or the game takes shape on the other.\n\nThe agent does the reasoning. The harness supplies the tools and the view. It's a folder with a\nharness.json, so adding a craft never touches the app.\n\n \n49 harnesses in the Store\n\n| Category | Agents and harnesses |\n|---|---|\n| **Coding** | Claude Code, Codex, Cursor, OpenCode, Pi, Hermes, Command Code, Devin, Muse Code, Amp, Antigravity, GitHub Copilot, Grok Build, Kilo Code, Machine Monitor, Trail of Bits Skills |\n| Essentials | Harness Monitor, Memories |\n| Design | Autonomous Workshop, Blender, Bonsai MCP, Creative Direction, Excalidraw, FreeCAD, Generative Art, OpenSCAD, text-to-cad |\n| Engineering | Autonomous Circuit, CircuitJS, Home Assistant, KiCad, Orca Slicer, Yosys |\n| Media | Comfy MCP, Manim, OpenMontage, Remotion |\n| Music | Ableton AI, JUCE Agent Toolkit, Music Studio, Score, Strudel |\n| Productivity | Marp, Typst |\n| Science & Data | autoresearch-mlx, Data Studio, Lab Bench, marimo, RDKit |\n| Simulation | DimOS, Drone Pilot, Foam-Agent, MuJoCo, SimSkill |\n| Games | Game Master, Godogen, Phaser, Voxel Worlds |\n| Research | Roundtable |\n| Local AI | MLX-LM, Model Manager, Ollama, vLLM |\n\nUpstream open-source tools and original workflows. 10 shared viewers install alongside\nthe harnesses that need them. Unlisted experiments are not shown.\n \n\nBuild your own\n\nThe next harness is the one for your craft. Wrap a tool you love or your team's toolchain.\nIt can live here or in your own repo. From this repo:\n\n \n\n \n\nPress **⌘N → Hello World** and say hello. The authoring guide covers the rest.\n\nHarness device\n\n**Get a Harness device**, or build your own from the files below. The\nfirmware guide lists the supported boards and build\ncommands, and the hardware guide covers the design files.\n\n \n\nThe optional **Harness device** is a round, always-on display that sits beside\nyour keyboard and shows your agents at a glance: what each one is doing, which one has finished, and\nwhich one is waiting on you. Read a question and answer it on the screen, or tap and speak a new task,\nwithout switching windows.\n\nIt is open hardware, all the way down. This repository has everything it takes to build one:\n\n| Layer | What's here |\n|---|---|\n| Firmware | ESP32-S3", 'readme_raw_len': 8692, 'one_line_zh': '面向编程 Agent 及更广场景的终极框架：所有 Agent、所有机器、一个指挥中心，从代码出发探索 CAD、电路、机器人、游戏与音乐', 'one_line_plain': 'Run and control coding agents across disciplines like CAD, circuits, robots, games and music from a single command center'}, {'id': 688, 'key': 'tencentcloudbase-cloudbase-ai-toolkit', 'kind': 'mcp', 'name': 'TencentCloudBase/CloudBase-AI-ToolKit', 'one_line': '📇 ☁️ 🏠 🍎 🪟 🐧 - Backend services for WeChat Mini Programs and full-stack apps: serverless cloud functions,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Backend for AI coding agents on CloudBase — database, auth, functions via Plugin, Skills & MCP.', 'url': 'https://github.com/TencentCloudBase/CloudBase-AI-ToolKit', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/TencentCloudBase/CloudBase-AI-ToolKit', 'downloads': None, 'stars': 1140, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/TencentCloudBase.png?size=64', 'official_desc': '📇 ☁️ 🏠 🍎 🪟 🐧 - Backend services for WeChat Mini Programs and full-stack apps: serverless cloud functions, databases and one-click deployment.', 'stars_own': 1140, 'stars_creator': None, 'stars_own_repo': 'TencentCloudBase/CloudBase-AI-ToolKit', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "CloudBase AI Toolkit\n\n**AI writes the code. CloudBase runs the backend.**\n\nThe CloudBase integration layer for AI coding tools: Plugin installs the stack, Skills steer how code is written, MCP operates databases, functions, storage, and deploys from chat.\n\n**English** · 简体中文 · [Docs][docs] · [Changelog][changelog] · [Issues][github-issues-link]\n\n[![][npm-version-shield]][npm-link]\n[![][npm-downloads-shield]][npm-link]\n[![][github-stars-shield]][github-stars-link]\n[![][github-forks-shield]][github-forks-link]\n[![][github-issues-shield]][github-issues-link]\n![][github-license-shield]\n![][github-contributors-shield]\n[![][cnb-shield]][cnb-link]\n[![][deepwiki-shield]][deepwiki-link]\n\n \n\nRecent updates\n\n**v2.35.x** (2026-10)\n\n- Hosted / Auth: each hosted MCP request runs on its own transport with client metadata cached per credential, and a project-level credential now takes precedence over the account login state\n- Tools / Skills: MySQL provisioning is retired from the tool surface; every skill package ships a LICENSE.md next to SKILL.md; a new MCP reference skill documents the remote endpoints, the plugin flags and the tool schemas\n- Feedback: prepareFeedback returns the repository's new-issue page instead of an auto-filled error report, and a hosted server now enables the same default plugin set as a local one\n- Onboarding / Docs: every initialize response now carries server instructions — the call to make first, the order a first run needs, and prompts that can be copied verbatim; the remote setup docs now lead with OAuth, so the URL alone is the configuration and a key pair is the CI path\n\n**v2.34.x** (2026-09)\n\n- i18n / IDE: full tool-copy localization with an instance-level lang, plus auth site / region params so international-site login and region routing resolve correctly\n- Cloud API: callCloudApi service allowlist widened to 57 with built-in version mapping (multi-version services such as tke / mongodb / vod require an explicit version)\n- Deploy / Env: new appBuild tool with hosting build neutralization; queryEnv reports the region actually applied and domains honors a passed envId\n- Skills / Docs: skill fallback reads now point at the official distribution repo with a references address list; SDK-first database decision gate for cloudrun; site doc links moved to the current Markdown addresses; post-deployment share offered after delivery in the expert packs and the deploy skills (opt-in, redacted, at most once)\n- Deploy / Apps: the cloud upload channel now completes end to end (deployApp accepts the timestamp getUploadUrl returns, getBuildLog accepts the build ID a deploy returns), and gateway route creation verifies the upstream exists before writing\n- Skills / Context: new PostgreSQL access-pattern best-practices skill (batching, indexes, RLS role gating, launch capacity); searchKnowledgeBase's inline skill / OpenAPI catalogs now load on demand, cutting the 43-tool surface by 9.1% on every tools/list\n- Runtime / Hosting: CloudRun Function mode must not bind PORT (the function framework does) and gets a credential decision gate naming CLOUDBASE_APIKEY; hosting paths and prefixes are normalized so a leading slash can no longer read as an empty directory\n- Connectors: the international-site WorkBuddy connector (cloudbase-intl) is built from the same config/source/** corpus as the domestic one — remote streamableHttp with standard MCP OAuth instead of local stdio, with China-site hosts rewritten to their international equivalents\n\n[Releases][changelog] · [Star][github-stars-link] · Watch → Releases\n\nWhat it is\n\nAI IDEs (Cursor, Claude Code, Codex, CodeBuddy, and others) are strong at generating code. What usually blocks you is the backend: schemas, permissions, functions, storage, environments, and release.\n\nCloudBase is Tencent Cloud’s AI-native all-in-one backend (database, storage, auth, cloud functions, Cloud Run, and more). This repo is the Toolkit that connects that backend to AI tools:\n\n| Piece | Role |\n|------|------|\n| **Plugin** | Installs MCP Server, Agent Skills, and Hooks together—less per-IDE wiring |\n| **Agent Skills** | Scenario skills (Web / Mini Program / database / auth / functions, etc.) toward workable CloudBase practice |\n| **MCP** | Login, query and change data, manage functions and hosting, read logs—from the conversation |\n\nThis repository ships the npm package @cloudbase/cloudbase-mcp, Skills, and AI plugins.\n\nYou still need your own CloudBase environment, and you should confirm sensitive actions the AI proposes. The Toolkit provides capability and path—not judgment.\n\nRelated repositories\n\nPublishing and sync repos live under TencentCloudBase. Directly related to this Toolkit:\n\n| Repository | Contents | Typical entry |\n|------|------|----------|\n| CloudBase-AI-Toolkit (this repo) | MCP Server source; marketplace source for Claude Code / Codex | npx @cloudbase/cloudbase-mcp@latest |\n| dsh-plugin | DeepSeek Harness plugin (@cloudbase/dsh-plugin): MCP bridge + DB/Storage/Auth panel | dsh plugin add @cloudbase/dsh-plugin |\n| cloudbase-plugin | Open Plugin Spec publish repo (CI-synced): MCP + Skills + Hooks | npx plugins add TencentCloudBase/cloudbase-plugin · CNB fallback: npx plugins add https://cnb.cool/tencent/cloud/cloudbase/cloudbase-plugin.git |\n| cloudbase-sites-plugin | Sites plugin: Vite Web create & deploy | npx plugins add TencentCloudBase/cloudbase-sites-plugin · CNB: https://cnb.cool/tencent/cloud/cloudbase/cloudbase-sites-plugin.git |\n| cloudbase-skills | Agent Skills collection | npx skills add TencentCloudBase/cloudbase-skills · CNB: https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills.git |\n| skills | Per-skill install catalog (also on skills.sh) | npx skills add tencentcloudbase/skills --skill |\n| awesome-cloudbase-examples | CloudBase examples and case studies | Browse / clone examples |\n| OpenVibeCoding | Vibecoding template on CloudBase | Use as a project starter |\n\nPrefer Plugin for the full stack; Skills alone when you only need knowledge constraints. For marketplace IDEs use this repo—do not also run npx plugins add on the same tool.\n\nQuick start\n\nFastest way to get started\n\nCopy this AI prompt into your AI IDE. The agent reads skill.md and completes the setup:\n\n \n\n \n Pick one default path for your tool (Plugin / CLI / MCP) \n\n| Your tool | Suggested path |\n|----------|----------|\n| Claude Code / Codex (native marketplace) | Add this repo as marketplace, then install the cloudbase plugin (plugin docs) |\n| Open Plugin Spec tools | npx plugins add TencentCloudBase/cloudbase-plugin |\n| Prefer one CLI for many tools | CloudBase AI CLI: npm i -g @cloudbase/cli && tcb ai |\n| CodeBuddy / WorkBuddy / Doubao / MiniMax Code / DeepSeek Harness / ZCode / Kimi (built-in) | Use the IDE's built-in CloudBase plugin or connector; for CodeBuddy you can also install via plugin marketplace |\n| Other MCP-capable IDEs | MCP config only (below) |\n\nPlugin\n\n \n\nDetails and IDE differences: AI plugin docs.\n\nMCP only\n\n \n\nRemote HTTP, self-hosted Cloud Mode, and plugin scoping: Install & connect.\n\n \n\nFirst prompts\n\n \n\n \n\nSkills shape structure and practice; MCP handles environment and resources. You should be able to verify data and APIs in your own environment—not only get local source files.\n\nSupported AI IDEs\n\n \n\n| Tool | Platform | Guide |\n|------|------|------|\n| CloudBase AI CLI | CLI | Guide |\n| OpenClaw | CLI | Guide |\n| WorkBuddy | Standalone IDE | Guide |\n| Doubao | Desktop & web (plugin marketplace) | Guide |\n| MiniMax Code | Desktop app & terminal CLI (plugin marketplace) | Guide |\n| DeepSeek Harness | Desktop app (plugin) | Guide |\n| ZCode | Standalone IDE (≥ 3.4.1 built-in) | Guide |\n| Kimi Code | CLI (plugin marketplace) | Guide |\n| Kimi Work | Desktop app (plugin panel) | Guide |\n| Codex App | App | Guide |\n| Cursor | Standalone IDE | Guide |\n| WindSurf | IDE / plugins | Guide |\n| CodeBuddy | Standalone IDE (built-in) | Guide |\n| CLINE | VS Code plugin | Guide |\n| GitHub Copilot | VS Code plugin | Guide |\n| T", 'readme_raw_len': 17055, 'one_line_zh': '📇 ☁️ 🏠 🍎 🪟 🐧 - 微信小程序与全栈应用的后端服务：无服务器云函数、…', 'one_line_plain': 'Backend services for WeChat Mini Programs and full-stack apps: serverless cloud functions'}, {'id': 2565, 'key': 'chatmcp-mcp-server-chatsum', 'kind': 'mcp', 'name': 'chatmcp/mcp-server-chatsum', 'one_line': 'Query and summarize your chat messages with AI prompts', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Query and Summarize your chat messages.', 'url': 'https://github.com/chatmcp/mcp-server-chatsum', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/chatmcp/mcp-server-chatsum', 'downloads': None, 'stars': 1027, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/chatmcp.png?size=64', 'official_desc': 'Query and summarize your chat messages with AI prompts.', 'stars_own': 1027, 'stars_creator': None, 'stars_own_repo': 'chatmcp/mcp-server-chatsum', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'mcp-server-chatsum\n\nThis MCP Server is used to summarize your chat messages.\n\n中文说明\n\n \n\n> **Before you start**\n>\n> move to chatbot directory, follow the README to setup the chat database.\n>\n> start chatbot to save your chat messages.\n\nFeatures\n\nResources\n\nTools\n\n- query_chat_messages - Query chat messages\n - Query chat messages with given parameters\n - Summarize chat messages based on the query prompt\n\nPrompts\n\nDevelopment\n\n1. Set up environment variables:\n\ncreate .env file in the root directory, and set your chat database path.\n\n \n\n2. Install dependencies:\n\n \n\nBuild the server:\n\n \n\nFor development with auto-rebuild:\n\n \n\nInstallation\n\nTo use with Claude Desktop, add the server config:\n\nOn MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n\nDebugging\n\nSince MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:\n\n \n\nThe Inspector will provide a URL to access debugging tools in your browser.\n\nCommunity\n\n- MCP Server Telegram\n- MCP Server Discord\n\nAbout the author\n\n- idoubi', 'readme_raw_len': 1151, 'one_line_zh': '用 AI Prompt 查询并总结你的聊天消息', 'one_line_plain': 'Query and summarize your chat messages with AI prompts'}, {'id': 946, 'key': 'line-line-bot-mcp-server', 'kind': 'mcp', 'name': 'line/line-bot-mcp-server', 'one_line': '🎖 📇 ☁️ - Integrate with a LINE Official Account', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server that integrates the LINE Messaging API to connect an AI Agent to the LINE Official Account.', 'url': 'https://github.com/line/line-bot-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/line/line-bot-mcp-server', 'downloads': None, 'stars': 789, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/line.png?size=64', 'official_desc': '🎖 📇 ☁️ - Integrate with a LINE Official Account.', 'stars_own': 789, 'stars_creator': None, 'stars_own_repo': 'line/line-bot-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "日本語版 READMEはこちら\n\nLINE Bot MCP Server\n\n \n\nModel Context Protocol (MCP) server implementation that integrates the LINE Messaging API to connect an AI Agent to the LINE Official Account.\n\n \n\n> [!NOTE]\n> This repository is provided as a preview version. While we offer it for experimental purposes, please be aware that it may not include complete functionality or comprehensive support.\n\nTools\n\n1. **push_text_message**\n - Push a simple text message to a user via LINE.\n - **Inputs:**\n - userId (string?): The user ID to receive a message. Defaults to DESTINATION_USER_ID. Either userId or DESTINATION_USER_ID must be set.\n - message.text (string): The plain text content to send to the user.\n2. **push_flex_message**\n - Push a highly customizable flex message to a user via LINE.\n - **Inputs:**\n - userId (string?): The user ID to receive a message. Defaults to DESTINATION_USER_ID. Either userId or DESTINATION_USER_ID must be set.\n - message.altText (string): Alternative text shown when flex message cannot be displayed.\n - message.contents (any): The contents of the flex message. This is a JSON object that defines the layout and components of the message.\n - message.contents.type (enum): Type of the container. 'bubble' for single container, 'carousel' for multiple swipeable bubbles.\n3. **broadcast_text_message**\n - Broadcast a simple text message via LINE to all users who have followed your LINE Official Account.\n - **Inputs:**\n - message.text (string): The plain text content to send to the users.\n4. **broadcast_flex_message**\n - Broadcast a highly customizable flex message via LINE to all users who have added your LINE Official Account.\n - **Inputs:**\n - message.altText (string): Alternative text shown when flex message cannot be displayed.\n - message.contents (any): The contents of the flex message. This is a JSON object that defines the layout and components of the message.\n - message.contents.type (enum): Type of the container. 'bubble' for single container, 'carousel' for multiple swipeable bubbles.\n5. **get_profile**\n - Get detailed profile information of a LINE user including display name, profile picture URL, status message and language.\n - **Inputs:**\n - userId (string?): The ID of the user whose profile you want to retrieve. Defaults to DESTINATION_USER_ID.\n6. **get_message_quota**\n - Get the message quota and consumption of the LINE Official Account. This shows the monthly message limit and current usage.\n - **Inputs:**\n - None\n7. **get_rich_menu_list**\n - Get the list of rich menus associated with your LINE Official Account.\n - **Inputs:**\n - None\n8. **delete_rich_menu**\n - Delete a rich menu from your LINE Official Account.\n - **Inputs:**\n - richMenuId (string): The ID of the rich menu to delete.\n9. **set_rich_menu_default**\n - Set a rich menu as the default rich menu.\n - **Inputs:**\n - richMenuId (string): The ID of the rich menu to set as default.\n10. **cancel_rich_menu_default**\n - Cancel the default rich menu.\n - **Inputs:**\n - None\n11. **create_rich_menu**\n - Create a rich menu based on the given actions. Generate and upload an image. Set as default.\n - **Inputs:**\n - chatBarText (string): Text displayed in chat bar, also used as rich menu name.\n - actions (array): The actions of the rich menu. You can specify minimum 1 to maximum 6 actions. Each action can be one of the following types:\n - postback: For sending a postback action\n - message: For sending a text message\n - uri: For opening a URL\n - datetimepicker: For opening a date/time picker\n - camera: For opening the camera\n - cameraRoll: For opening the camera roll\n - location: For sending the current location\n - richmenuswitch: For switching to another rich menu\n - clipboard: For copying text to clipboard\n\n12. **get_follower_ids**\n - Get a list of user IDs of users who have added the LINE Official Account as a friend. This allows you to obtain user IDs for sending messages without manually preparing them.\n - **Inputs:**\n - start (string?): Continuation token to get the next array of user IDs. Returned in the next property of a previous response.\n - limit (number?): The maximum number of user IDs to retrieve in a single request.\n13. **get_group_summary**\n - Get the group chat summary including group ID, group name, and group icon URL, using the group ID.\n - **Inputs:**\n - groupId (string): The group ID of the target group chat.\n\nInstallation (Using npx)\n\nrequirements:\n- Node.js v22 or later\n\nStep 1: Create LINE Official Account\n\nThis MCP server utilizes a LINE Official Account. If you do not have one, please create it by following this instructions. \n\nIf you have a LINE Official Account, enable the Messaging API for your LINE Official Account by following this instructions.\n\nStep 2: Configure AI Agent\n\nPlease add the following configuration for an AI Agent like Claude Desktop or Cline. \n\nSet the environment variables or arguments as follows:\n\n- CHANNEL_ACCESS_TOKEN: (required) Channel Access Token. You can confirm this by following this instructions.\n- DESTINATION_USER_ID: (optional) The default user ID of the recipient. If the Tool's input does not include userId, DESTINATION_USER_ID is required. You can confirm this by following this instructions.\n\n \n\nInstallation (Using Docker)\n\nStep 1: Create LINE Official Account\n\nThis MCP server utilizes a LINE Official Account. If you do not have one, please create it by following this instructions.\n\nIf you have a LINE Official Account, enable the Messaging API for your LINE Official Account by following this instructions.\n\nStep 2: Build line-bot-mcp-server image\n\nClone this repository:\n\n \n\nBuild the Docker image:\n\n \n\nStep 3: Configure AI Agent\n\nPlease add the following configuration for an AI Agent like Claude Desktop or Cline.\n\nSet the environment variables or arguments as follows:\n\n- mcpServers.args: (required) The path to line-bot-mcp-server.\n- CHANNEL_ACCESS_TOKEN: (required) Channel Access Token. You can confirm this by following this instructions.\n- DESTINATION_USER_ID: (optional) The default user ID of the recipient. If the Tool's input does not include userId, DESTINATION_USER_ID is required.\nYou can confirm this by following this instructions.\n\n \n\nLocal Development with Inspector\n\nYou can use the MCP Inspector to test and debug the server locally.\n\nPrerequisites\n\n1. Clone the repository:\n \n\n2. Install dependencies:\n \n\n3. Build the project:\n \n\nRun the Inspector\n\nAfter building the project, you can start the MCP Inspector:\n\n \n\nThis will start the MCP Inspector interface where you can interact with the LINE Bot MCP Server tools and test their functionality.\n\nVersioning\n\nThis project respects semantic versioning\n\nSee http://semver.org/\n\nContributing\n\nPlease check CONTRIBUTING before making a contribution.", 'readme_raw_len': 6735, 'one_line_zh': '🎖 📇 ☁️ - 对接 LINE 官方账号', 'one_line_plain': 'Integrate with a LINE Official Account'}, {'id': 4309, 'key': 'tevonsb-homeassistant-mcp', 'kind': 'mcp', 'name': 'tevonsb/homeassistant-mcp', 'one_line': '📇 🏠 - Access Home Assistant data and control devices such as lights, switches and thermostats', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A MCP server for Home Assistant', 'url': 'https://github.com/tevonsb/homeassistant-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/tevonsb/homeassistant-mcp', 'downloads': None, 'stars': 576, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/tevonsb.png?size=64', 'official_desc': '📇 🏠 - Access Home Assistant data and control devices such as lights, switches and thermostats.', 'stars_own': 576, 'stars_creator': None, 'stars_own_repo': 'tevonsb/homeassistant-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol Server for Home Assistant\n\nThe server uses the MCP protocol to share access to a local Home Assistant instance with an LLM application.\n\nA powerful bridge between your Home Assistant instance and Language Learning Models (LLMs), enabling natural language control and monitoring of your smart home devices through the Model Context Protocol (MCP). This server provides a comprehensive API for managing your entire Home Assistant ecosystem, from device control to system administration.\n\n \n \n \n \n \n \n\nFeatures\n\n- 🎮 **Device Control**: Control any Home Assistant device through natural language\n- 🔄 **Real-time Updates**: Get instant updates through Server-Sent Events (SSE)\n- 🤖 **Automation Management**: Create, update, and manage automations\n- 📊 **State Monitoring**: Track and query device states\n- 🔐 **Secure**: Token-based authentication and rate limiting\n- 📱 **Mobile Ready**: Works with any HTTP-capable client\n\nReal-time Updates with SSE\n\nThe server includes a powerful Server-Sent Events (SSE) system that provides real-time updates from your Home Assistant instance. This allows you to:\n\n- 🔄 Get instant state changes for any device\n- 📡 Monitor automation triggers and executions\n- 🎯 Subscribe to specific domains or entities\n- 📊 Track service calls and script executions\n\nQuick SSE Example\n\n \n\nSee SSE_API.md for complete documentation of the SSE system.\n\nTable of Contents\n\n- Key Features\n- Prerequisites\n- Installation\n - Basic Setup\n - Docker Setup (Recommended)\n- Configuration\n- Development\n- API Reference\n - Device Control\n - Add-on Management\n - Package Management\n - Automation Management\n- Natural Language Integration\n- Troubleshooting\n- Project Status\n- Contributing\n- Resources\n- License\n\nKey Features\n\nCore Functionality 🎮\n- **Smart Device Control**\n - 💡 **Lights**: Brightness, color temperature, RGB color\n - 🌡️ **Climate**: Temperature, HVAC modes, fan modes, humidity\n - 🚪 **Covers**: Position and tilt control\n - 🔌 **Switches**: On/off control\n - 🚨 **Sensors & Contacts**: State monitoring\n - 🎵 **Media Players**: Playback control, volume, source selection\n - 🌪️ **Fans**: Speed, oscillation, direction\n - 🔒 **Locks**: Lock/unlock control\n - 🧹 **Vacuums**: Start, stop, return to base\n - 📹 **Cameras**: Motion detection, snapshots\n\nSystem Management 🛠️\n- **Add-on Management**\n - Browse available add-ons\n - Install/uninstall add-ons\n - Start/stop/restart add-ons\n - Version management\n - Configuration access\n\n- **Package Management (HACS)**\n - Integration with Home Assistant Community Store\n - Multiple package types support:\n - Custom integrations\n - Frontend themes\n - Python scripts\n - AppDaemon apps\n - NetDaemon apps\n - Version control and updates\n - Repository management\n\n- **Automation Management**\n - Create and edit automations\n - Advanced configuration options:\n - Multiple trigger types\n - Complex conditions\n - Action sequences\n - Execution modes\n - Duplicate and modify existing automations\n - Enable/disable automation rules\n - Trigger automation manually\n\nArchitecture Features 🏗️\n- **Intelligent Organization**\n - Area and floor-based device grouping\n - State monitoring and querying\n - Smart context awareness\n - Historical data access\n\n- **Robust Architecture**\n - Comprehensive error handling\n - State validation\n - Secure API integration\n - TypeScript type safety\n - Extensive test coverage\n\nPrerequisites\n\n- **Node.js** 20.10.0 or higher\n- **NPM** package manager\n- **Docker Compose** for containerization\n- Running **Home Assistant** instance\n- Home Assistant long-lived access token (How to get token)\n- **HACS** installed for package management features\n- **Supervisor** access for add-on management\n\nInstallation\n\nBasic Setup\n\n \n\nDocker Setup (Recommended)\n\nThe project includes Docker support for easy deployment and consistent environments across different platforms.\n\n1. **Clone the repository:**\n \n\n2. **Configure environment:**\n \n Edit the .env file with your Home Assistant configuration:\n \n\n3. **Build and run with Docker Compose:**\n \n\n4. **Verify the installation:**\n The server should now be running at http://localhost:3000. You can check the health endpoint at http://localhost:3000/health.\n\n5. **Update the application:**\n \n\nDocker Configuration\n\nThe Docker setup includes:\n- Multi-stage build for optimal image size\n- Health checks for container monitoring\n- Volume mounting for environment configuration\n- Automatic container restart on failure\n- Exposed port 3000 for API access\n\nDocker Compose Environment Variables\n\nAll environment variables can be configured in the .env file. The following variables are supported:\n- HASS_HOST: Your Home Assistant instance URL\n- HASS_TOKEN: Long-lived access token for Home Assistant\n- HASS_SOCKET_URL: WebSocket URL for Home Assistant\n- PORT: Server port (default: 3000)\n- NODE_ENV: Environment (production/development)\n- DEBUG: Enable debug mode (true/false)\n\nConfiguration\n\nEnvironment Variables\n\n \n\nConfiguration Files\n\n1. **Development**: Copy .env.example to .env.development\n2. **Production**: Copy .env.example to .env.production\n3. **Testing**: Copy .env.example to .env.test\n\nAdding to Claude Desktop (or other clients)\n\nTo use your new Home Assistant MCP server, you can add Claude Desktop as a client. Add the following to the configuration. Note this will run the MCP within claude and does not work with the Docker method.\n\n \n\nAPI Reference\n\nDevice Control\n\nCommon Entity Controls\n \n\nLight Control\n \n\nAdd-on Management\n\nList Available Add-ons\n \n\nInstall Add-on\n \n\nManage Add-on State\n \n\nPackage Management\n\nList HACS Packages\n \n\nInstall Package\n \n\nAutomation Management\n\nCreate Automation\n \n\nDuplicate Automation\n \n\nCore Functions\n\nState Management\n \n\nManages the current state of the system.\n\n**Example Request:**\n \n\nContext Updates\n \n\nUpdates the current context with new information.\n\n**Example Request:**\n \n\nAction Endpoints\n\nExecute Action\n \n\nExecutes a specified action with given parameters.\n\n**Example Request:**\n \n\nBatch Actions\n \n\nExecutes multiple actions in sequence.\n\n**Example Request:**\n \n\nQuery Functions\n\nGet Available Actions\n \n\nReturns a list of all available actions.\n\n**Example Response:**\n \n\nContext Query\n \n\nRetrieves context information.\n\n**Example Response:**\n \n\nWebSocket Events\n\nThe server supports real-time updates via WebSocket connections.\n\n \n\nSupported Events\n\n- state_change: Emitted when system state changes\n- context_update: Emitted when context is updated\n- action_executed: Emitted when an action is completed\n- error: Emitted when an error occurs\n\n**Example Event Data:**\n \n\nError Handling\n\nAll endpoints return standard HTTP status codes:\n\n- 200: Success\n- 400: Bad Request\n- 401: Unauthorized\n- 403: Forbidden\n- 404: Not Found\n- 500: Internal Server Error\n\n**Error Response Format:**\n \n\nRate Limiting\n\nThe API implements rate limiting to prevent abuse:\n\n- 100 requests per minute per IP for regular endpoints\n- 1000 requests per minute per IP for WebSocket connections\n\nWhen rate limit is exceeded, the server returns:\n\n \n\nExample Usage\n\nUsing curl\n \n\nUsing JavaScript\n \n\nDevelopment\n\n \n\nTroubleshooting\n\nCommon Issues\n\n1. **Node.js Version (toSorted is not a function)**\n - **Solution:** Update to Node.js 20.10.0+\n \n\n2. **Connection Issues**\n - Verify Home Assistant is running\n - Check HASS_HOST accessibility\n - Validate token permissions\n - Ensure WebSocket connection for real-time updates\n\n3. **Add-on Management Issues**\n - Verify Supervisor access\n - Check add-on compatibility\n - Validate system resources\n\n4. **HACS Integration Issues**\n - Verify HACS installation\n - Check HACS integration status\n - Validate repository access\n\n5. **Automation Issues**\n - Verify entity availability\n - Check trigger conditions\n - Validate service calls\n - Monitor execution logs\n\nProject Status\n\n✅ **Complete**\n- Entity, Floor, and Area access\n- Device control (Lights, Climate, Covers, Switches, Contacts)\n- Add-on management system\n- Package management through HACS\n- Advanced automation configuration\n- Basic stat', 'readme_raw_len': 8931, 'one_line_zh': '访问 Home Assistant 数据，控制灯光、开关和恒温器等设备', 'one_line_plain': 'Access Home Assistant data and control devices such as lights, switches and thermostats'}, {'id': 929, 'key': 'joinly-ai-joinly', 'kind': 'mcp', 'name': 'joinly-ai/joinly', 'one_line': '🐍 ☁️ - Send bots to browser-based meetings (Zoom, Teams, Google Meet) to gather live transcripts, speak text…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Make your meetings accessible to AI Agents', 'url': 'https://github.com/joinly-ai/joinly', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/joinly-ai/joinly', 'downloads': None, 'stars': 569, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/joinly-ai.png?size=64', 'official_desc': '🐍 ☁️ - Send bots to browser-based meetings (Zoom, Teams, Google Meet) to gather live transcripts, speak text and post in the meeting chat.', 'stars_own': 569, 'stars_creator': None, 'stars_own_repo': 'joinly-ai/joinly', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Make your meetings accessible to AI Agents 🤖 \n\n**joinly.ai** is a connector middleware designed to enable AI agents to join and actively participate in video calls. Through its MCP server, joinly.ai provides essential meeting tools and resources that can equip any AI agent with the skills to perform tasks and interact with you in real time during your meetings.\n\n> Want to dive right in? Jump to the Quickstart!\n> Want to know more? Visit our website!\n\n> [!IMPORTANT] \n> Don\'t want the hustle of setting everything up? Try our cloud first! ☁️🚀\n\n:sparkles: Features\n- **Live Interaction**: Lets your agents execute tasks and respond in real-time by voice or chat within your meetings\n- **Conversational flow**: Built-in logic that ensures natural conversations by handling interruptions and multi-speaker interactions\n- **Cross-platform**: Join Google Meet, Zoom, and Microsoft Teams (or any available over the browser)\n- **Bring-your-own-LLM**: Works with all LLM providers (also locally with Ollama)\n- **Choose-your-preferred-TTS/STT**: Modular design supports multiple services - Whisper/Deepgram for STT and Kokoro/ElevenLabs/Deepgram for TTS (and more to come...)\n- **100% open-source, self-hosted and privacy-first** :rocket:\n\n:video_camera: Demos\nGitHub\n \n> In this demo video, joinly answers the question \'What is Joinly?\' by accessing the latest news from the web. It then creates an issue in a GitHub demo repository.\nNotion\n \n> In this demo video, we connect joinly to our notion via MCP and let it edit the content of a page content live in the meeting. \n\nAny ideas what we should build next? Write us! :rocket:\n\n:zap: Quickstart\nRun joinly via Docker with a basic conversational agent client.\n\n> [!IMPORTANT]\n> **Prerequisites**: Docker installation\n\nCreate a new folder joinly or clone this repository (not mandatory for the following steps). In this directory, create a new .env file with a valid API key for the LLM provider you want to use, e.g. OpenAI:\n\n> [!TIP]\n> You can find the OpenAI API key here\n\n \n\n> [!NOTE]\n> See .env.example for complete configuration options including Anthropic (Claude) and Ollama setups. Replace the placeholder values with your actual API keys and adjust the model name as needed. Delete the placeholder values of the providers you don\'t use.\n\nPull the Docker image (~2.3GB since it packages browser and models):\n \n\nLaunch your meeting in Zoom, Google Meet or Teams and let joinly join the meeting using the meeting link as . Then, run the following command from the folder where you created the .env file:\n \n> :red_circle: Having trouble getting started? Let\'s figure it out together on our discord! \n\n:technologist: Run a custom client\nIn Quickstart, we ran the Docker Container directly as a client using --client. But we can also run it as a server and connect to it from outside the container, which allows us to connect other MCP servers. Here, we run a custom client using the joinly-client package and connect it to the joinly MCP server.\n\n> [!IMPORTANT]\n> **Prerequisites**: do the Quickstart (except the last command), install uv, and open two terminals\n\nStart the joinly server in the first terminal (note, we are not using --client here and publish port 8000 on localhost only):\n \n\n> [!WARNING]\n> The joinly MCP server has no authentication and accepts client-supplied configuration, so it is meant to run locally with a single trusted client. Bind it to localhost (as above) and do not expose the port to a network.\n\nWhile the server is running, start the example client implementation in the second terminal window to connect to it and join a meeting:\n \n\nAdd MCP servers to the client\nAdd the tools of any MCP server to the agent by providing a JSON configuration. The configuration file can contain multiple entries under "mcpServers" which will all be available as tools in the meeting (see fastmcp client docs for config syntax):\n\n \n\nAdd for example a Tavily config for web searching, then run the client using the config file, here named config.json:\n\n \n\n:wrench: Configurations\n\nConfigurations can be given via env variables and/or command line args. Here is a list of common configuration options, which can be used when starting the docker container:\n \n\nAlternatively, you can pass --name, --lang, and provider settings as command line arguments using joinly-client, which will override settings of the server:\n \n\nBasic Settings\n\nIn general, the docker image provides an MCP server which is started by default. But to quickly get started, we also include a client implementation that can be used via --client. Note, in this case no server is started and no other client can connect to it.\n\n \n\nProviders\n\nText-to-Speech\n \n\nTranscription\n \n\nDebugging\n \n\nGPU Support\n\nWe provide a Docker image with CUDA GPU support for running the transcription and TTS models on a GPU. To use it, you need to have the NVIDIA Container Toolkit installed and CUDA >= 12.6. Then pull the CUDA-enabled image:\n \n\nRun as client or server with the same commands as above, but use the joinly:{version}-cuda image and set --gpus all:\n \n\nBy default, the joinly image uses the Whisper model base for transcription, since it still runs reasonably fast on CPU. For cuda, it automatically defaults to distil-large-v3 for significantly better transcription quality. You can change the model by setting --stt-arg model_name= (e.g., --stt-arg model_name=large-v3). However, only the respective default models are packaged in the docker image, so it will start to download the model weights on container start.\n\n:test_tube: Create your own agent\n\nYou can also write your own agent and connect it to our joinly MCP server. See the code examples for the joinly-client package or the client_example.py if you want a starting point that doesn\'t depend on our framework.\n\nThe joinly MCP server provides following tools and resources:\n\nTools\n\n- **join_meeting** - Join meeting with URL, participant name, and optional passcode\n- **leave_meeting** - Leave the current meeting\n- **speak_text** - Speak text using TTS (requires text parameter)\n- **send_chat_message** - Send chat message (requires message parameter)\n- **mute_yourself** - Mute microphone\n- **unmute_yourself** - Unmute microphone\n- **get_chat_history** - Get current meeting chat history in JSON format\n- **get_participants** - Get current meeting participants in JSON format\n- **get_transcript** - Get current meeting transcript in JSON format, optionally filtered by minutes\n- **get_video_snapshot** - Get an image from the current meeting, e.g., view a current screenshare\n\nResources\n\n- **transcript://live** - Live meeting transcript in JSON format, including timestamps and speaker information. Subscribable for real-time updates when new utterances are added.\n\n:building_construction: Developing joinly.ai\n\nFor development we recommend using the development container, which installs all necessary dependencies. To get started, install the DevContainer Extension for Visual Studio Code, open the repository and choose **Reopen in Container**.\n\n \n\nThe installation can take some time, since it downloads all packages as well as models for Whisper/Kokoro and the Chromium browser. At the end, it automatically invokes the download_assets.py script. If you see errors like Missing kokoro-v1.0.onnx, run this script manually using:\n \n\nWe\'d love to see what you are using it for or building with it. Showcase your work on our discord\n:pencil2: Roadmap\n\n**Meeting**\n- [x] Meeting chat access\n- [ ] Camera in video call with status updates\n- [ ] Enable screen share during video conferences\n- [ ] Participant metadata and joining/leaving\n- [ ] Improve browser agent capabilities\n\n**Conversation**\n- [x] Speaker attribute for transcription\n- [ ] Improve client memory: reduce token usage, allow persistence across meetings\nevents\n- [ ] Improve End-of-Utterance/turn-taking detection\n- [ ] Human approval mechanism from inside the meeting\n\n**Integrations**\n- [ ] Showcase how to add agents using the A2A protoc', 'readme_raw_len': 8821, 'one_line_zh': '🐍 ☁️ - 派机器人加入浏览器会议（Zoom、Teams、Google Meet），收集实时转录、朗读文本…', 'one_line_plain': 'Send bots to browser-based meetings (Zoom, Teams, Google Meet) to gather transcripts and speak text'}, {'id': 4206, 'key': 'evalstate-mcp-hfspace', 'kind': 'mcp', 'name': 'evalstate/mcp-hfspace', 'one_line': '📇 ☁️ - Use Hugging Face Spaces for open-source image generation, chat, vision and more, with image, audio and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP Server to Use HuggingFace spaces, easy configuration and Claude Desktop mode.', 'url': 'https://github.com/evalstate/mcp-hfspace', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/evalstate/mcp-hfspace', 'downloads': None, 'stars': 389, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/evalstate.png?size=64', 'official_desc': '📇 ☁️ - Use Hugging Face Spaces for open-source image generation, chat, vision and more, with image, audio and text uploads and downloads.', 'stars_own': 389, 'stars_creator': None, 'stars_own_repo': 'evalstate/mcp-hfspace', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'mcp-hfspace MCP Server 🤗\n\n> [!TIP]\n>\n> You can access and configure Hugging Face MCP services directly at https://hf.co/mcp, including Gradio spaces.\n>\n> This project has been superceded by the official Hugging Face MCP Server and Gradio MCP Endpoints.\n> \n> Alternatively you can run hf-mcp-server locally as a STDIO Server, or with robust support for SSE, Streaming HTTP and Streaming HTTP JSON Mode. This also runs a local UI for selecting tools and endpoints and supports ToolListChangedNotifications too.\n\nhf.co/mcp\n\n \n\nmcp-hfspace\n\nRead the introduction here llmindset.co.uk/resources/mcp-hfspace/\n\nConnect to Hugging Face Spaces with minimal setup needed - simply add your spaces and go!\n\nBy default, it connects to black-forest-labs/FLUX.1-schnell providing Image Generation capabilities to Claude Desktop.\n\n \n\nGradio MCP Support\n\n> [!TIP]\n> Gradio 5.28 now has integrated MCP Support via SSE: https://huggingface.co/blog/gradio-mcp. Check out whether your target Space is MCP Enabled!\n\nInstallation\n\nNPM Package is @llmindset/mcp-hfspace.\n\nInstall a recent version of NodeJS for your platform, then add the following to the mcpServers section of your claude_desktop_config.json file:\n\n \n\nPlease make sure you are using Claude Desktop 0.78 or greater.\n\nThis will get you started with an Image Generator.\n\nBasic setup\n\nSupply a list of HuggingFace spaces in the arguments. mcp-hfspace will find the most appropriate endpoint and automatically configure it for usage. An example claude_desktop_config.json is supplied below.\n\nBy default the current working directory is used for file upload/download. On Windows this is a read/write folder at \\users\\ \\AppData\\Roaming\\Claude\\ Text Output: david bowie\n \n\n_If you are uploading something to Claude\'s context use the Paperclip Attachment button, otherwise specify the filename for the Server to send directly._\n\nWe can also supply a URL. For example : use paligemma to detect humans in https://e3.365dm.com/24/12/1600x900/skynews-taylor-swift-eras-tour_6771083.jpg?20241209000914 -> One person is detected in the image - Taylor Swift on stage.\n\nExample 3 - Text-to-Speech (Download Audio)\n\nIn _Claude Desktop Mode_, the audio file is saved in the WORK_DIR, and Claude is notified of the creation. If not in desktop mode, the file is returned as a base64 encoded resource to the Client (useful if it supports embedded Audio attachments).\n\n \n\nExample 4 - Speech-to-Text (Upload Audio)\n\nHere, we use hf-audio/whisper-large-v3-turbo to transcribe some audio, and make it available to Claude.\n\n \n\nExample 5 - Image-to-Image\n\nIn this example, we specify the filename for microsoft/OmniParser to use, and get returned an annotated Image and 2 separate pieces of text: descriptions and coordinates. The prompt used was use omniparser to analyse ./screenshot.png and use the analysis to produce an artifact that reproduces that screen. DawnC/Pawmatch is also good at this.\n\n \n\nExample 6 - Chat\n\nIn this example, Claude sets a number of reasoning puzzles for Qwen, and asks follow-up questions for clarification.\n\n \n\nSpecifying API Endpoint\n\nIf you need, you can specify a specific API Endpoint by adding it to the spacename. So rather than passing in Qwen/Qwen2.5-72B-Instruct you would use Qwen/Qwen2.5-72B-Instruct/model_chat.\n\nClaude Desktop Mode\n\nThis can be disabled with the option --desktop-mode=false or the environment variable CLAUDE_DESKTOP_MODE=false. In this case, content as returned as an embedded Base64 encoded Resource.\n\nRecommended Spaces\n\nSome recommended spaces to try:\n\nImage Generation\n\n- shuttleai/shuttle-3.1-aesthetic\n- black-forest-labs/FLUX.1-schnell\n- yanze/PuLID-FLUX\n- gokaygokay/Inspyrenet-Rembg (Background Removal)\n- diyism/Datou1111-shou_xin - Beautiful Pencil Drawings\n\nChat\n\n- Qwen/Qwen2.5-72B-Instruct\n- prithivMLmods/Mistral-7B-Instruct-v0.3\n\nText-to-speech / Audio Generation\n\n- fantaxy/Sound-AI-SFX\n- parler-tts/parler_tts\n\nSpeech-to-text\n\n- hf-audio/whisper-large-v3-turbo\n- (the openai models use unnamed parameters so will not work)\n\nText-to-music\n\n- haoheliu/audioldm2-text2audio-text2music\n\nVision Tasks\n\n- microsoft/OmniParser\n- merve/paligemma2-vqav2\n- merve/paligemma-doc\n- DawnC/PawMatchAI\n- DawnC/PawMatchAI/on_find_match_click - for interactive dog recommendations\n\nOther Features\n\nPrompts\n\nPrompts for each Space are generated, and provide an opportunity to input. Bear in mind that often Spaces aren\'t configured with particularly helpful labels etc. Claude is actually very good at figuring this out, and the Tool description is quite rich (but not visible in Claude Desktop).\n\nResources\n\nA list of files in the WORK_DIR is returned, and as a convenience returns the name as "Use the file..." text. If you want to add something to Claude\'s context, use the paperclip - otherwise specify the filename for the MCP Server. Claude does not support transmitting resources from within Context.\n\nPrivate Spaces\n\nPrivate Spaces are supported with a HuggingFace token. The Token is used to download and save generated content.\n\nUsing Claude Desktop\n\nTo use with Claude Desktop, add the server config:\n\nOn MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n\nKnown Issues and Limitations\n\nmcp-hfspace\n\n- Endpoints with unnamed parameters are unsupported for the moment.\n- Full translation from some complex Python types to suitable MCP formats.\n\nClaude Desktop\n\n- Claude Desktop 0.75 doesn\'t seem to respond to errors from the MCP Server, timing out instead. For persistent issues, use the MCP Inspector to get a better look at diagnosing what\'s going wrong. If something suddenly stops working, it\'s probably due to exhausting your HuggingFace ZeroGPU quota - try again after a short period, or set up your own Space for hosting.\n- Claude Desktop seems to use a hard timeout value of 60s, and doesn\'t appear to use Progress Notifications to manage UX or keep-alive. If you are using ZeroGPU spaces, large/heavy jobs may timeout. Check the WORK_DIR for results though; the MCP Server will still capture and save the result if it was produced.\n- Claude Desktops reporting of Server Status, logging etc. isn\'t great - use @modelcontextprotocol/inspector to help diagnose issues.\n\nHuggingFace Spaces\n\n- If ZeroGPU quotas or queues are too long, try duplicating the space. If your job takes less than sixty seconds, you can usually change the function decorator @spaces.GPU(duration=20) in app.py` to request less quota when running the job.\n- Passing HF_TOKEN will make ZeroGPU quotas apply to your (Pro) HF account\n- If you have a private space, and dedicated hardware your HF_TOKEN will give you direct access to that - no quota\'s apply. I recommend this if you are using for any kind of Production task.\n\nThird Party MCP Services', 'readme_raw_len': 6795, 'one_line_zh': '借助 Hugging Face Spaces 实现开源图像生成、对话、视觉等多项能力', 'one_line_plain': 'Create and share AI applications using Hugging Face Spaces for image, text, and audio tasks'}, {'id': 554, 'key': 'recursechat-mcp-server-apple-shortcuts', 'kind': 'mcp', 'name': 'recursechat/mcp-server-apple-shortcuts', 'one_line': '📇 🏠 🍎 - Integration with Apple Shortcuts', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/recursechat/mcp-server-apple-shortcuts', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/recursechat/mcp-server-apple-shortcuts', 'downloads': None, 'stars': 351, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/recursechat.png?size=64', 'official_desc': 'Apple Shortcuts MCP Server A Model Context Protocol (MCP) server that lets AI assistants like Claude control Apple Shortcuts automations.', 'stars_own': 351, 'stars_creator': None, 'stars_own_repo': 'recursechat/mcp-server-apple-shortcuts', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'Apple Shortcuts MCP Server 🤖\n\nA Model Context Protocol (MCP) server that lets AI assistants like Claude control Apple Shortcuts automations. This enables AI models to trigger shortcuts and automate tasks on macOS in a safe and controlled way.\n\n \n\n \n\nWhat is MCP? 🤔\n\nThe Model Context Protocol (MCP) is a system that lets AI apps, like Claude Desktop, connect to external tools and data sources. It gives a clear and safe way for AI assistants to work with local services and APIs while keeping the user in control.\n\nWhat does this server do? 🚀\n\nThe Apple Shortcuts MCP server:\n- Enables AI assistants to list available shortcuts\n- Allows running shortcuts by name with optional input parameters \n- Provides a simple interface for automation control\n\nPrerequisites 📋\n\nBefore you begin, ensure you have:\n\n- Node.js (v18 or higher)\n- Claude Desktop installed\n- macOS with Shortcuts app configured\n\nConfiguration to use Apple Shortcuts Server ⚙️\n\nHere\'s the Claude Desktop configuration to use the Apple Shortcuts server:\n \n\nBuild Apple Shortcuts Server and run locally 🛠️\n\n1. Clone this repository:\n\n \n\n2. Install dependencies:\n \n\n3. Build project\n \n\nHere\'s the Claude Desktop configuration to use the Apple Shortcuts server with a local build:\n \n\n \n\nUsage 🎯\n\nYou can ask Claude "list shortcuts" or run a specific shortcut with the shortcut name, for example "get word of the day" or "play a song".\n\nLicense ⚖️\n\nApache-2.0', 'readme_raw_len': 1419, 'one_line_zh': '📇 🏠 🍎 - 与 Apple Shortcuts 快捷指令集成', 'one_line_plain': 'Connect to Apple Shortcuts for integration'}, {'id': 4317, 'key': 'ttommyth-interactive-mcp', 'kind': 'mcp', 'name': 'ttommyth/interactive-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Interactive LLM workflows with local user prompts and chat directly in the MCP loop', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Vibe coding should have human in the loop interactive-mcp: Local, cross-platform MCP server for interact with your AI Agent', 'url': 'https://github.com/ttommyth/interactive-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ttommyth/interactive-mcp', 'downloads': None, 'stars': 351, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ttommyth.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Interactive LLM workflows with local user prompts and chat directly in the MCP loop.', 'stars_own': 351, 'stars_creator': None, 'stars_own_repo': 'ttommyth/interactive-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'interactive-mcp\n\n \n\n \n\n \n\nA MCP Server implemented in Node.js/TypeScript, facilitating interactive communication between LLMs and users. **Note:** This server is designed to run locally alongside the MCP client (e.g., Claude Desktop, VS Code), as it needs direct access to the user\'s operating system to display notifications and command-line prompts.\n\n_(Note: This project is in its early stages.)_\n\n**Want a quick overview?** Check out the introductory blog post: Stop Your AI Assistant From Guessing — Introducing interactive-mcp\n\nDemo Video\n\n \n \n \n \n \n\nTools\n\nThis server exposes the following tools via the Model Context Protocol (MCP):\n\n- request_user_input: Asks the user a question and returns their answer. Can display predefined options.\n- message_complete_notification: Sends a simple OS notification.\n- start_intensive_chat: Initiates a persistent command-line chat session.\n- ask_intensive_chat: Asks a question within an active intensive chat session.\n- stop_intensive_chat: Closes an active intensive chat session.\n\nDemo\n\nHere are demonstrations of the interactive features:\n\n| Normal Question | Completion Notification |\n| :--------------------------------------------------------: | :-----------------------------------------------------------------: |\n| | |\n\n| Intensive Chat Start | Intensive Chat End |\n| :------------------------------------------------------------------: | :--------------------------------------------------------------: |\n| | |\n\nUsage Scenarios\n\nThis server is ideal for scenarios where an LLM needs to interact directly with the user on their local machine, such as:\n\n- Interactive setup or configuration processes.\n- Gathering feedback during code generation or modification.\n- Clarifying instructions or confirming actions in pair programming.\n- Any workflow requiring user input or confirmation during LLM operation.\n\nClient Configuration\n\nThis section explains how to configure MCP clients to use the interactive-mcp server.\n\nBy default, user prompts will time out after 30 seconds. You can customize server options like timeout or disabled tools by adding command-line flags directly to the args array when configuring your client.\n\nPlease make sure you have the npx command available.\n\nUsage with Claude Desktop / Cursor\n\nAdd the following minimal configuration to your claude_desktop_config.json (Claude Desktop) or mcp.json (Cursor):\n\n \n\n**With specific version**\n\n \n\n**Example with Custom Timeout (30s):**\n\n \n\nUsage with VS Code\n\nAdd the following minimal configuration to your User Settings (JSON) file or .vscode/mcp.json:\n\n \n\nmacOS Recommendations\n\nFor a smoother experience on macOS using the default Terminal.app, consider this profile setting:\n\n- **(Shell Tab):** Under **"When the shell exits"** (**Terminal > Settings > Profiles > _[Your Profile]_ > Shell**), select **"Close if the shell exited cleanly"** or **"Close the window"**. This helps manage windows when the MCP server starts and stops.\n\nDevelopment Setup\n\nThis section is primarily for developers looking to modify or contribute to the server. If you just want to _use_ the server with an MCP client, see the "Client Configuration" section above.\n\nPrerequisites\n\n- **Node.js:** Check package.json for version compatibility.\n- **pnpm:** Used for package management. Install via npm install -g pnpm after installing Node.js.\n\nInstallation (Developers)\n\n1. Clone the repository:\n\n \n\n2. Install dependencies:\n\n \n\nRunning the Application (Developers)\n\n \n\nCommand-Line Options\n\nThe interactive-mcp server accepts the following command-line options. These should typically be configured in your MCP client\'s JSON settings by adding them directly to the args array (see "Client Configuration" examples).\n\n| Option | Alias | Description |\n| ----------------- | ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| --timeout | -t | Sets the default timeout (in seconds) for user input prompts. Defaults to 30 seconds. |\n| --disable-tools | -d | Disables specific tools or groups (comma-separated list). Prevents the server from advertising or registering them. Options: request_user_input, message_complete_notification, intensive_chat. |\n\n**Example:** Setting multiple options in the client config args array:\n\n \n\nDevelopment Commands\n\n- **Build:** pnpm build\n- **Lint:** pnpm lint\n- **Format:** pnpm format\n\nGuiding Principles for Interaction\n\nWhen interacting with this MCP server (e.g., as an LLM client), please adhere to the following principles to ensure clarity and reduce unexpected changes:\n\n- **Prioritize Interaction:** Utilize the provided MCP tools (request_user_input, start_intensive_chat, etc.) frequently to engage with the user.\n- **Seek Clarification:** If requirements, instructions, or context are unclear, **always** ask clarifying questions before proceeding. Do not make assumptions.\n- **Confirm Actions:** Before performing significant actions (like modifying files, running complex commands, or making architectural decisions), confirm the plan with the user.\n- **Provide Options:** Whenever possible, present the user with predefined options through the MCP tools to facilitate quick decisions.\n\nYou can provide these instructions to an LLM client like this:\n\n \n\nContributing\n\nContributions are welcome! Please follow standard development practices. (Further details can be added later).\n\nLicense\n\nMIT (See LICENSE file for details - if applicable, or specify license directly).', 'readme_raw_len': 5574, 'one_line_zh': '支持交互式 LLM 工作流，可读取本地用户输入并在 MCP 循环中直接对话', 'one_line_plain': 'Interactive LLM workflows with local user prompts and direct chat within the MCP loop'}, {'id': 866, 'key': 'chaindead-telegram-mcp', 'kind': 'mcp', 'name': 'chaindead/telegram-mcp', 'one_line': '🏎️ 🏠 - Telegram API integration: access user data, manage dialogs (chats, channels, groups), retrieve…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Telegram MCP for managing dialogs, messages, drafts, read statuses, and more.', 'url': 'https://github.com/chaindead/telegram-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/chaindead/telegram-mcp', 'downloads': None, 'stars': 349, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/chaindead.png?size=64', 'official_desc': '🏎️ 🏠 - Telegram API integration: access user data, manage dialogs (chats, channels, groups), retrieve messages and handle read status.', 'stars_own': 349, 'stars_creator': None, 'stars_own_repo': 'chaindead/telegram-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Telegram MCP server\n\nThe server is a bridge between the Telegram API and the AI assistants and is based on the Model Context Protocol.\n\n> [!IMPORTANT]\n> Ensure that you have read and understood the Telegram API Terms of Service before using this server.\n> Any misuse of the Telegram API may result in the suspension of your account.\n\nTable of Contents\n- What is MCP?\n- What does this server do?\n - Capabilities\n - Prompt examples\n - Message Management\n - Organization\n - Communication\n- Installation\n - Homebrew\n - NPX\n - From Releases\n - MacOS\n - Linux\n - Windows\n - From Source\n- Configuration\n - Authorization\n - Client Configuration\n - JSON Schema Version\n- Star History\n\nWhat is MCP?\n\nThe Model Context Protocol (MCP) is a system that lets AI apps, like Claude Desktop or Cursor, connect to external tools and data sources. It gives a clear and safe way for AI assistants to work with local services and APIs while keeping the user in control.\n\nWhat does this server do?\n\nCapabilities\n\n- [x] Get current account information (tool: tg_me)\n- [x] List dialogs with optional unread filter (tool: tg_dialogs)\n- [x] Mark dialog as read (tool: tg_read)\n- [x] Retrieve messages from specific dialog (tool: tg_dialog)\n- [x] Send draft messages to any dialog (tool: tg_send)\n\nPrompt examples\n\nHere are some example prompts you can use with AI assistants:\n\nMessage Management\n- "Check for any unread important messages in my Telegram"\n- "Summarize all my unread Telegram messages"\n- "Read and analyze my unread messages, prepare draft responses where needed"\n- "Check non-critical unread messages and give me a brief overview"\n\nOrganization\n- "Analyze my Telegram dialogs and suggest a folder structure"\n- "Help me categorize my Telegram chats by importance"\n- "Find all work-related conversations and suggest how to organize them"\n\nCommunication\n- "Monitor specific chat for updates about [topic]"\n- "Draft a polite response to the last message in [chat]"\n- "Check if there are any unanswered questions in my chats"\n\nInstallation\n\nHomebrew\n\nYou can install a binary release on macOS/Linux using brew:\n\n \n\nNPX\n\nYou can run the latest version directly using npx (supports macOS, Linux, and Windows):\n\n \n\nWhen using NPX, modify the standard commands and configuration as follows:\n\n- Authentication command becomes:\n \n\n- Claude MCP server configuration becomes:\n \n\nFor complete setup instructions, see Authorization and Client Configuration.\n\nFrom Releases\n\nMacOS\n\n \n\n> **Note:** The commands below install to /usr/local/bin. To install elsewhere, replace /usr/local/bin with your preferred directory in your PATH.\n\nFirst, download the archive for your architecture:\n\n \n\nThen install the binary:\n\n \n \n\nLinux\n \n\n> **Note:** The commands below install to /usr/local/bin. To install elsewhere, replace /usr/local/bin with your preferred directory in your PATH.\n\nFirst, download the archive for your architecture:\n\n \n\nThen install the binary:\n\n \n \n\nWindows\n\n \n\nWindows\n1. Download the latest release for your architecture:\n - Windows x64\n - Windows ARM64\n2. Extract the .zip file\n3. Add the extracted directory to your PATH or move telegram-mcp.exe to a directory in your PATH\n \n\nFrom Source\n\nRequirements:\n- Go 1.24 or later\n- GOBIN in PATH\n\n \n\nConfiguration\n\nAuthorization\n\nBefore you can use the server, you need to connect to the Telegram API.\n\n1. Get the API ID and hash from Telegram API\n2. Run the following command:\n > __Note:__\n > If you have 2FA enabled: add --password \n\n > __Note:__\n > If you want to override existing session: add --new\n\n \n\n 📩 Enter the code you received from Telegram to connect to the API.\n\n3. Done! Please give this project a ⭐️ to support its development.\n\nClient Configuration\n\nExample of Configuring Claude Desktop to recognize the Telegram MCP server.\n\n1. Open the Claude Desktop configuration file:\n - in MacOS, the configuration file is located at ~/Library/Application Support/Claude/claude_desktop_config.json\n - in Windows, the configuration file is located at %APPDATA%\\Claude\\claude_desktop_config.json\n\n > __Note:__\n > You can also find claude_desktop_config.json inside the settings of Claude Desktop app\n\n2. Add the server configuration\n \n for Claude desktop:\n \n\nJSON Schema Version\n\nSome MCP clients (e.g. VS Code) do not support JSON Schema Draft 2020-12 and will reject tools that use it. You can override the JSON Schema version by setting the --schema-version flag or the TG_SCHEMA_VERSION environment variable.\n\nCommon values:\n| Version | URL |\n|---------|-----|\n| Draft-07 (recommended for VS Code) | https://json-schema.org/draft-07/schema# |\n| Draft 2020-12 (default) | https://json-schema.org/draft/2020-12/schema |\n\nStar History', 'readme_raw_len': 4673, 'one_line_zh': '🏎️ 🏠 - Telegram API 集成：访问用户数据，管理对话（聊天、频道、群组），获取…', 'one_line_plain': 'Access Telegram user data and manage chats, channels, and groups'}, {'id': 865, 'key': 'carterlasalle-mac-messages-mcp', 'kind': 'mcp', 'name': 'carterlasalle/mac_messages_mcp', 'one_line': '🏠 🍎 - Query, analyze, send and receive iMessage conversations, with phone number validation, attachments,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/carterlasalle/mac_messages_mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/carterlasalle/mac_messages_mcp', 'downloads': None, 'stars': 333, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/carterlasalle.png?size=64', 'official_desc': 'ages MCP Read, search, and send macOS Messages from any local MCP client.** Quick start · Available tools · Agent skill · Command-line interface · Security · Changelog Use Claude, Codex, Cursor, VS Code, or any local MCP client to search, read, and send messages through the macOS Messages app.', 'stars_own': 333, 'stars_creator': None, 'stars_own_repo': 'carterlasalle/mac_messages_mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'Mac Messages MCP\n\n**Read, search, and send macOS Messages from any local MCP client.**\n\n \n \n \n \n \n \n\nQuick start · Available tools · Agent skill · Command-line interface · Security · Changelog\n\n \n\nUse Claude, Codex, Cursor, VS Code, or any local MCP client to search, read, and\nsend messages through the macOS Messages app.\n\nMac Messages MCP runs locally on your Mac. It opens the Messages and Contacts\ndatabases read-only, returns only the data a client asks for, and uses\nMessages.app automation only when the client explicitly calls the send tool.\n\n> [!IMPORTANT] This server is macOS-only. Reading messages requires Full Disk\n> Access. Sending requires a Mac signed into Messages plus permission for the\n> launching app to automate Messages.\n\nHow it works\n\n \n\nEverything the client reads comes from the local SQLite databases, opened\nread-only. Everything that leaves the machine goes out through Messages.app,\nunder an explicit send tool call. Message- and contact-derived text is fenced as\nuntrusted data on the way back to the model.\n\nCapabilities\n\n| Area | What Mac Messages MCP provides |\n|---|---|\n| Reading | Recent messages across all conversations or filtered by contact, group chat, date range, or unread state; paging with limit/offset and forward-cursor reads |\n| Conversations | Every conversation (1:1, business, and group) with kind, message and unread counts, last activity, and a blocking wait for new messages |\n| Search | Fuzzy message-body search across a time window or all history, scoped to a contact or conversation |\n| Context | Per-message service (iMessage/SMS/RCS), unread and delivery state, tapbacks, and threaded replies |\n| Contacts | Fuzzy contact lookup by approximate name, send-ready phone numbers, and contact creation |\n| Group chats | Named group chat discovery with stable chat IDs reused for reads and sends |\n| Sending | iMessage with SMS/RCS fallback, file attachments, optional human approval through MCP elicitation, and iMessage reachability checks |\n| Scheduling | Queue a message for later delivery while the server process stays alive |\n| Attachments | Metadata search, best-effort text/PDF content search, inline images (HEIC converted to PNG), and local paths for larger or non-image files |\n| Diagnostics | Messages and Contacts database permission checks from inside the MCP client |\n| Privacy | Read-only SQLite access, no message archive of its own, and a structural untrusted-output boundary |\n\nQuick start\n\n1. Install uv\n\n \n\nConfirm that the launcher is available:\n\n \n\nPython 3.10 or newer is required. uvx can provision a compatible Python and\ninstalls Mac Messages MCP in an isolated environment, so you do not need to\ncreate a virtual environment first.\n\n2. Grant macOS permissions\n\nOpen **System Settings → Privacy & Security → Full Disk Access** and enable the\napp that will launch the MCP server:\n\n- Claude Desktop, Cursor, VS Code, or the ChatGPT desktop app when configured in\n that app\n- Terminal, iTerm2, Ghostty, or another terminal when using Claude Code or Codex\n CLI from that terminal\n\nQuit and reopen the app after changing Full Disk Access. On the first contact\nlookup or send, macOS may separately ask for access to Contacts or permission to\ncontrol Messages. Allow those prompts.\n\nAlso make sure Messages.app is open, signed in, and already able to send a\nnormal message.\n\n3. Add the server to your MCP client\n\nThe server command is the same everywhere:\n\n \n\nChoose your client below.\n\nClaude Desktop\n\nOpen **Claude → Settings → Developer → Edit Config**, then add:\n\n \n\nPreserve any other servers already in claude_desktop_config.json, save the\nfile, and restart Claude Desktop.\n\nClaude Desktop also supports installable .mcpb extensions. See\nBuild the Claude Desktop extension if you\nwant to package this repository as one.\n\nClaude Code\n\nAdd it once at user scope so it is available in every project:\n\n \n\nVerify it:\n\n \n\nInside Claude Code, run /mcp to inspect the connection and tools.\n\nCodex CLI, Codex IDE extension, and ChatGPT desktop app\n\nCodex clients on the same Mac share MCP configuration. Add the server with:\n\n \n\nThen verify it:\n\n \n\nYou can also add it directly to ~/.codex/config.toml:\n\n \n\nRestart the desktop app or IDE extension after changing the configuration. In\nCodex CLI, use /mcp to view the active server.\n\nCursor\n\n \n\nOr open **Cursor Settings → Tools & MCP → New MCP Server** and use:\n\n \n\nRestart the server from Cursor\'s MCP settings after saving.\n\nVS Code / GitHub Copilot\n\nOpen the Command Palette and run **MCP: Add Server**. Choose **Command\n(stdio)**, enter uvx as the command, add mac-messages-mcp as the argument,\nand install it globally.\n\nOr add it from a terminal:\n\n \n\nThe equivalent user or workspace mcp.json entry is:\n\n \n\n> [!NOTE] VS Code uses a top-level servers object. Claude Desktop and Cursor\n> use mcpServers.\n\nOther stdio MCP clients\n\nUse this generic server definition:\n\n \n\nIf a GUI client reports that uvx cannot be found, run which uvx in Terminal\nand replace "uvx" with the returned absolute path. Homebrew commonly installs\nit at /opt/homebrew/bin/uvx on Apple silicon and /usr/local/bin/uvx on Intel\nMacs.\n\n4. Verify the connection\n\nAsk your client to call tool_check_db_access, then tool_check_addressbook.\nOnce both succeed, try prompts such as:\n\n \n\n \n\n \n\n \n\nThe first uvx launch can take longer while it downloads and caches Python\ndependencies.\n\n5. Optional: set the phone number region\n\nPhone numbers written in national format (06 39 98 00 01, (415) 555-1234)\nhave to be expanded to E.164 before they can be matched against the Messages\ndatabase, and that expansion needs to know which country they belong to. The\nserver reads your Mac\'s own region setting for this, so on a correctly\nconfigured Mac there is nothing to do.\n\nSet MAC_MESSAGES_REGION to an [ISO 3166-1 alpha-2][iso3166] code when your\nnumbers belong to a different region than your Mac is configured for — a French\nSIM on a Mac set to en_US, say:\n\n \n\nFor Claude Code:\n\n \n\nThe region is resolved once at startup, so restart the server after changing\nit. Resolution order: MAC_MESSAGES_REGION, then the macOS AppleLocale\npreference, then LC_ALL / LC_CTYPE / LANG, then US. Numbers already\nwritten in E.164 (+33639980001) are never reinterpreted and need none of\nthis.\n\n[iso3166]: https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2\n\nAvailable tools\n\n \n\n| Tool | Purpose | Side effect |\n| ---------------------------------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------- |\n| tool_get_recent_messages | Read recent messages, filtered by contact, group chat ID, date range, or unread state; page with limit/offset | Read-only |\n| tool_fuzzy_search_messages | Search message bodies by approximate text match, scoped to a contact or chat; defaults to 30 days, or hours=0 | Read-only |\n| tool_list_conversations | List every conversation with kind, message count, unread count, and last activity | Read-only |\n| tool_wait_for_new_messages | Block until a message newer than a ROWID cursor arrives, or the timeout expires | Read-only |\n| tool_find_contact | Fuzzy-match a name in Contacts and return phone numbers | Read-only |\n| tool_get_chats | List named group chats and their identifiers | Read-only |\n| tool_search_attachments | Find attachment metadata by date, contact, MIME type, and limit | Read-only |\n| tool_search_attachment_contents | Search inside attachment text/PDF contents by date, contact, and MIME type | Read-only |\n| tool_get_attachment | Fetch one attachment by ID, inline when supported or as a local path | Read-only |\n| tool_check_imessage_availability | Check likely iMessage availability for a phone number or email | Read-only |\n| tool_check_db_access | Diagnose access to ~/Library/Messages/chat.db | Read-only |\n| tool_check_contacts | Return a contact count and a small sample | Read-only |\n| tool_check_addressbook | Diagnose Contacts/AddressBook database access | Read-only', 'readme_raw_len': 18802, 'one_line_zh': '🏠 🍎 - 查询、分析并收发 iMessage 对话，支持手机号校验、附件…', 'one_line_plain': 'Query and send iMessages with phone validation and attachments'}, {'id': 4829, 'key': 'orgoai-bops', 'kind': 'repo', 'name': 'OrgoAI/bops', 'one_line': 'Bops: a team of AI bots that run your business ops, each with its own computer, email and phone number. bops.bot', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Bops: a team of AI bots that run your business ops, each with its own computer, email and phone number. bops.bot', 'url': 'https://github.com/OrgoAI/bops', 'source': 'github', 'source_url': 'https://github.com/OrgoAI/bops', 'downloads': None, 'stars': 326, 'installs': None, 'mentions': 1, 'score': 103.49, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-10', 'times_seen': 2, 'icon': 'https://github.com/OrgoAI.png?size=64', 'official_desc': 'Bops: a team of AI bots that run your business ops, each with its own computer, email and phone number. bops.bot', 'stars_own': 326, 'stars_creator': None, 'stars_own_repo': 'OrgoAI/bops', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx next', 'install_src': 'HEAD/README.md', 'install_conf': 'low', 'desc_src': 'github-api', 'readme_body': "Bops \n\n \n Business ops, run by bots. \n A team of AI bots that works for you on real computers. Chat with them like teammates, and they get the work done.\n \n\n \n Download for Mac \n  · \n Website \n  · \n Plans \n  · \n Roadmap \n  · \n Self-host \n \n\n \n \n \n \n\n \n Open code, one limit. Use, self-host, change and share Bops for free, at work or at home. \n The license (FSL) only rules out offering it as a competing product or hosted service, \n and each version becomes Apache 2.0, fully open source, two years after its release. Details \n \n\n \n \n Created by Nick Vasilescu ( @nickvasilescu ) \n Developed with Orgo \n \n\n \n \n \n \n \n \n \n \n \n\n \n\nBops is a Mac app that gives you a team of AI bots for your business ops: inbox, pipeline, invoices, reports. They work on cloud computers you can watch live and take over at any time. Reach them in the app and in Slack, Telegram and Discord, and on Pro and Max by text, call or email. They remember who you are and what they did yesterday.\n\n \n \n \n\n \n \n \n \n Watch or take over. Watch any screen live, take control, then hand it back. \n \n \n \n Asks before it acts. Reading runs at once. Sending, creating and paying wait for your OK. \n \n \n \n \n \n Your apps, your rules. About 1,400 apps, several accounts each. Each bot gets only the access you give it. \n \n \n \n Where your team works. Ask in Slack, Telegram or Discord, and the answer comes back there. \n \n \n \n\nWhat your bots can do\n\n- **Work on real computers.** Your bots work on a cloud computer from Orgo: 4 cores, 16 GB RAM and 4 screens, with Chrome, LibreOffice, GIMP and Inkscape. Watch any screen live, or take over. On Free and Pro all your bots share one computer. Max gives you up to 3.\n- **Work as a team.** A main bot (Boppy, unless you rename it) runs the team and hands work to the specialists you add. Bots talk to each other, keep threads and report back in the chat. Each workspace is its own team.\n- **Remember.** What you tell your bots, they remember, per workspace, on every channel.\n- **Use your apps.** Connect Gmail, Calendar and any of about 1,400 apps, with several accounts each. Reading runs at once. Sending, creating or paying asks you first. A bot set to Just do it sends and changes things without asking, and still asks before paying.\n- **Look things up.** Companies, people, work emails (checked before they're used), phones, company news, hiring and funding, and local businesses, for cents a lookup. A pricier lookup asks you first, and so does emailing someone a bot found, even on Just do it.\n- **Answer email.** On Pro and Max, your main bot has its own email address, ending in bops.bot. Email it a task, and it emails back when it's done.\n- **Take texts and calls.** On Pro and Max, your main bot has its own phone number. Text it like a person, or call it and talk live. It hands off work while you talk, and texts you the results. Only you can give it work: anyone else who calls gets a bot that takes a message.\n- **Talk in the app.** Call any bot by voice, or use the mic in the chat.\n- **Join your team's chat.** Add a bot to Slack, Telegram or Discord. It answers when it's mentioned and takes requests only from you.\n- **Work on your Mac, too.** A bot can do a task on your Mac in a Chrome of its own, so sites see your home connection. It can't touch your files or apps there until you turn on Full access in **Settings → This Mac**, which also gives its tasks a shell.\n- **Sign in safely.** Logins live in the Vault: passwords and 2FA keys stay in your Mac's Keychain, and no model ever sees them.\n- **Keep an eye on things.** Routines and reminders run on a schedule. A bot can watch a screen, like an inbox or a dashboard, and tell you when something there needs you.\n- **Make things.** Bots draw and edit pictures right in the chat, and files they link open in Bops.\n\nBops runs on your Mac: keep it on for routines, watches, and Telegram or Discord. While it's away, callers to your bot's number get a bot that takes a message.\n\nComing soon: WhatsApp, and a real phone for each bot that it drives like its computer.\n\nPlans\n\nEvery plan includes a Bops computer: 1 on Free and Pro, up to 3 on Max. On Free and Pro, all your bots share that one computer. AI credit pays for what your bots do, billed at cost.\n\n| | **Free** | **Pro** | **Max** |\n|---|---|---|---|\n| Price | $0 | $20 a month | $200 a month |\n| Computers | **1 computer included** 10 hours a month, 4 cores, 16 GB RAM, multi-screen | **1 computer included** Always on, 4 cores, 16 GB RAM, templates, multi-screen | **Up to 3 computers** Always on, 4 cores, 16 GB RAM each |\n| AI credit | $5 of AI credit, once | $20 of AI credit every month | $200 of AI credit every month |\n| Bots | As many as you like, all sharing that one computer | As many as you like, all sharing that one computer | As many as you like, across up to 3 computers |\n| Phone numbers and emails | No phone number or email | 1 phone number and 1 email | Up to 5 phone numbers and 5 emails |\n\n- Free's computer gets 10 hours a month. It sleeps when nobody is using it, so the hours last. Account and usage shows the hours used this month and when they start over.\n- Need more AI credit? Add some any time, $5 to $1,000, in Account and usage. It's a one-time charge, never automatic.\n- Pro and Max credit doesn't carry over from month to month. When credit runs out, tasks stop cleanly.\n- Pick or change a plan in the app, in Account and usage.\n\nRunning Bops yourself on your own keys? Bops plans don't apply: you pay each provider directly, and your Orgo plan sets how many computers you can run. See Run it yourself.\n\nDownload\n\n \n \n \n\n1. Download Bops from bops.bot. It runs on a Mac with Apple silicon.\n2. Open the DMG and drag Bops to Applications.\n3. Sign in with Google, with your email, or with Orgo. If you don't have an Orgo account yet, one is made for you.\n4. Say hi to Boppy. It takes it from there.\n\nEvery release is signed with Developer ID and notarized by Apple. When a newer version is out, Bops tells you, along with what's new in it.\n\nRoadmap\n\nWhat's coming next. Ideas are welcome: open an issue.\n\n- **Multi-harness support.** Today your bots run on OpenAI (its Agents API with Codex, and its computer tool). Soon you'll choose the agent harness they run on.\n- **Zero data retention.** An option where the AI providers your bots use don't store your requests or their replies.\n\nCredits\n\nBops was created by Nick Vasilescu (@nickvasilescu). It started as his open-source project and is now developed with Orgo. See AUTHORS and CITATION.cff.\n\nThanks to everyone who has reported a bug, sent a fix or shared an idea: see all contributors.\n\nBuilt with\n\n- Orgo: the bots' cloud computers\n- OpenAI: chat, tasks and voice\n- Honcho: long-term memory\n- Composio: connected apps\n- treg: business data\n- AgentMail: the bots' email\n- AgentPhone: phone numbers, texts and calls\n- TypeSafe: small yes or no judgments\n\nThe app itself is Electron and Next.js.\n\n---\n\nHow it works\n\n- **The app** (desktop/, Electron) starts the **Bops server** (Next.js: app/ and lib/server/) on your Mac, on port 3210. The server runs the team: chats, threads, routines, watches and the bots' tasks.\n- **The bots' computers** are Orgo cloud computers, launched from a template built from this repo (orgo/, with what runs on them in vm/).\n- **Tasks on the bots' computers** run on OpenAI's computer tool (Responses API). **Tasks on your Mac** run on OpenAI's Agents API, with the Codex CLI (codex exec-server) and a Chrome of the bots' own.\n- **Signed in with Orgo**, the app works through Bops Cloud (cloud/), the server Orgo runs for hosted Bops. Orgo's provider keys stay there, and your Mac gets only narrow ones: a key for your own mailboxes and a spend-capped key for tasks. Bops Cloud also takes the webhooks for texts, calls and Slack, and keeps your chats in your account.\n- **Self-hosted** (BOPS_SELF_HOSTED=1), the app calls each provider directly with the keys in .env.local and keeps its state in .data/", 'readme_raw_len': 20265, 'one_line_zh': 'Bops：一支运行你业务运维的 AI Bot 团队，每个都有自己的电脑、邮箱与电话号码', 'one_line_plain': 'Bops: a team of AI bots that run your business ops, each with its own computer, email and phone number. bops.bot'}]/2026-10-20 https://anclum.com/en/[{'id': 5585, 'key': 'paperclipai-paperclip', 'kind': 'repo', 'name': 'paperclipai/paperclip', 'one_line': 'The open-source app everyone uses to manage agents at work', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'The open-source app everyone uses to manage agents at work', 'url': 'https://github.com/paperclipai/paperclip', 'source': 'github', 'source_url': 'https://github.com/paperclipai/paperclip', 'downloads': None, 'stars': 104653, 'installs': None, 'mentions': 5, 'score': 703.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/paperclipai.png?size=64', 'official_desc': 'The open-source app everyone uses to manage agents at work', 'stars_own': 104653, 'stars_creator': None, 'stars_own_repo': 'paperclipai/paperclip', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx paperclipai@latest', 'install_src': 'HEAD/README.md', 'install_conf': 'high', 'desc_src': 'github-api', 'readme_body': 'Quickstart ·\n Docs ·\n GitHub ·\n Discord ·\n Twitter ·\n Website \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n\n \n \n \n\n \n Sign up for the Paperclip Cloud waitlist → \n \n\n \n\nPaperclip is the app people use to manage AI agents for work.\n\nOpen-source orchestration for teams of AI agents.\n\n**If OpenClaw is an _employee_, Paperclip is the _company_.**\n\nPaperclip is a Node.js server and React UI that orchestrates a team of AI agents to run a business. Bring your own agents, assign goals, and track work and costs from one dashboard. Choose models and harnesses per agent while keeping your team\'s tasks, skills, permissions, and history in one place.\n\nIt looks like a task manager. Under the hood: org charts, budgets, governance, goal alignment, and agent coordination.\n\n**Manage business goals, not pull requests.**\n\n| | Step | Example |\n| ------ | --------------- | ------------------------------------------------------------------ |\n| **01** | Define the goal | _"Build the #1 AI note-taking app to $1M MRR."_ |\n| **02** | Hire the team | CEO, CTO, engineers, designers, marketers — any bot, any provider. |\n| **03** | Approve and run | Review strategy. Set budgets. Hit go. Monitor from the dashboard. |\n\n \n\n \n \n \n Works with \n OpenClaw \n Claude Code \n Codex \n Cursor + Cloud \n Gemini CLI \n \n \n OpenCode \n Pi \n Hermes + Gateway \n Grok Build \n Kimi Code \n \n \n\n If it can receive a heartbeat, it\'s hired. \n\n \n\nCustom processes, HTTP endpoints, and external adapter packages extend the roster. See the adapter overview for setup and capabilities.\n\n \n\nPaperclip is right for you if\n\n- ✅ You want to build **autonomous AI organizations**\n- ✅ You **coordinate many different agents** (OpenClaw, Codex, Claude, Cursor) toward a common goal\n- ✅ You have **20 simultaneous Claude Code terminals** open and lose track of what everyone is doing\n- ✅ You want agents running **autonomously 24/7**, but still want to audit work and chime in when needed\n- ✅ You want to **monitor costs** and enforce budgets\n- ✅ You want a process for managing agents that **feels like using a task manager**\n- ✅ You want to manage your autonomous businesses **from your phone**\n\n \n\nThe four pillars\n\nFour things have to work for an organization of AI agents to actually produce: the tasks, the org, the training, and the infrastructure. Paperclip is built around exactly those four pillars.\n\n \n \n \n \n \n\n| Pillar | Built for | What it covers |\n| --- | --- | --- |\n| **Agentic Task Manager** — Declare intent. Agents work. You verify the output. | Everyone, daily | Tasks, approvals & review gates · proactive agent coworkers · auditable routines & workflows · verify from diffs, screenshots & tests |\n| **Org Chart for Agents** — Roles, permissions & boundaries for humans and agents. | Managers | Mixed human + agent org chart · responsibilities, delegation, specialization · governance: who can do what · scoped secrets & company boundaries · connection permissions & responsible-user identities |\n| **Agent Employee Training** — Design, train & evaluate your AI employees. | Enablers | Skill Studio & shared org-wide skills · evals & saved test runs · active learning loops & quality metrics · performance reviews for agents · saved test inputs · skill version history & restore · reusable team templates |\n| **Agentic OS** — The infrastructure that makes the work run. | IT & platform | Cross-provider runtime: any model, any agent · sandboxing, integrations & MCP servers · SSO, GRC, RBAC & cost controls · data privacy, internal trace collection, compounding data value · personal & shared app connections · run history & opt-in tracing |\n\n \n\nFeatures\n\n \n \n \n 🔌 Bring Your Own Agent \nAny agent, any runtime, one org chart. If it can receive a heartbeat, it\'s hired.\n \n \n 🎯 Goal Alignment \nLink tasks and projects to your organization goals. Agents receive the goal context behind their work.\n \n \n 💓 Heartbeats \nAgents wake for assigned work, follow-up messages, or configured schedules. Delegation flows up and down the org chart.\n \n \n \n \n 💰 Cost Control \nCompany, agent, and project budgets . Track reported spend, get threshold alerts, and pause work at configured limits.\n \n \n 🏢 Multi-Organization \nOne deployment, many organizations. Separate tasks, agents, permissions, and activity histories for each.\n \n \n 🎫 Task Threads \nKeep conversations, plans, blockers, files, and run history attached to the work. Assign tasks to agents or people.\n \n \n \n \n 🛡️ Governance \nConfigure review and approval stages , approve hires, and pause, reassign, or stop work when needed.\n \n \n 📊 Org Chart \nHierarchies, roles, reporting lines. Your agents have a boss, a title, and a job description.\n \n \n 📱 Mobile Ready \nMonitor and manage your autonomous businesses from anywhere.\n \n \n \n \n 🔗 Apps & Connections \n Connect services such as GitHub, Notion, and Railway, or your own MCP server. Set gateway actions to Allowed, Ask first, or Off.\n \n \n 👥 Shared Agents, Personal Accounts \nChoose who can use a connection and which agents can access it . Managed GitHub operations can use the account of the person directing the work.\n \n \n 🧠 Skills & Skill Studio \nInstall or write shared skills , test them with saved inputs, inspect results, and restore earlier versions.\n \n \n \n \n 📅 Scheduled Routines \nRun recurring work on a schedule or trigger it through an API or webhook. Each run has a task, an owner, and a history.\n \n \n 📎 Artifacts & Feedback \nFind the files and documents agents produce . Preview supported formats and leave comments on specific passages in documents.\n \n \n 📦 Ready-Made Teams \nPreview and install teams with roles, skills, projects, and routines. Choose their runtimes and make the setup your own.\n \n \n \n\nExperimental **Agent Chat** and **chat/email connectors** add conversations with agents in Paperclip and through configured services such as Slack, Discord, Telegram, and AgentMail. Enable the relevant instance settings to try them.\n\n \n\nProblems Paperclip solves\n\n| Without Paperclip | With Paperclip |\n| ------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- |\n| ❌ You have 20 Claude Code tabs open and can\'t track which one does what. On reboot you lose everything. | ✅ Tasks are ticket-based, conversations are threaded, sessions persist across reboots. |\n| ❌ You manually gather context from several places to remind your bot what you\'re actually doing. | ✅ Context flows from the task up through the project and company goals — your agent always knows what to do and why. |\n| ❌ Folders of agent configs are disorganized and you\'re re-inventing task management, communication, and coordination between agents. | ✅ Paperclip gives you org charts, ticketing, delegation, and governance out of the box — so you run a company, not a pile of scripts. |\n| ❌ Runaway loops waste hundreds of dollars of tokens and max your quota before you even know what happened. | ✅ Spend tracking, budget alerts, and automatic pauses help you control the cost of ongoing work. |\n| ❌ You have recurring jobs (customer support, social, reports) and have to remember to manually kick them off. | ✅ Routines create assigned tasks on a schedule, with outputs and run history you can inspect. |\n| ❌ You have an idea, you have to find your repo, fire up Claude Code, keep a tab open, and babysit it. | ✅ Add a task in Paperclip. Your coding agent works on it until it\'s done. Management reviews their work. |\n\n \n\nWhy Paperclip is special\n\nPaperclip handles the hard orchestration details correctly.\n\n| | |\n| --------------------------------- | ------------------------------------------------------------------------------------------------------------- |\n| **Atomic task checkout.** | A single assignee and execution locks prevent competing runs from claiming the same task. |\n| **Persistent work context.** ', 'readme_raw_len': 21727, 'one_line_zh': '用于在工作场景中管理 Agent 的开源应用。', 'one_line_plain': 'Manage agents at work with this open-source app'}, {'id': 4247, 'key': 'klavis-ai-klavis-tree-main-mcp-servers-youtube', 'kind': 'mcp', 'name': 'Klavis-AI/YouTube', 'one_line': '🐍 📇 - Extract and convert YouTube video information', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Klavis AI: MCP integration platforms that let AI agents use tools reliably at any scale', 'url': 'https://github.com/Klavis-AI/klavis/tree/main/mcp_servers/youtube', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Klavis-AI/klavis/tree/main/mcp_servers/youtube', 'downloads': None, 'stars': 5817, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Klavis-AI.png?size=64', 'official_desc': '🐍 📇 - Extract and convert YouTube video information.', 'stars_own': 5817, 'stars_creator': None, 'stars_own_repo': 'Klavis-AI/klavis', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '🎯 Choose Your Solution\n\n \n \n \n \n \n \n Strata \n Intelligent connectors for your AI agent, optimize context window \n \n \n \n \n \n \n \n \n \n \n \n MCP Integrations \n 100+ prebuilt integrations out-of-the-box, with OAuth support \n \n \n \n \n \n \n \n \n \n \n \n MCP Sandbox \n scalable MCP environments for LLM training and RL \n \n \n \n \n \n \n \n \n \n \n \n\nQuick Start\n\nOption 1: Cloud-hosted - klavis.ai\n\nQuickstart guide →\n\nOption 2: Self-host\n\n \n\nOption 3: SDK\n\n \n\n \n\nOption 4: REST API\n\n \n\nResources\n\n- 📖 Documentation\n- 💬 Discord Community\n- 🐛 Report Issues\n- 🌐 Klavis AI Website\n\n---\n\n \n Made with ❤️ by the Klavis Team', 'readme_raw_len': 597, 'one_line_zh': '提取并转换 YouTube 视频信息。', 'one_line_plain': 'Extract and convert YouTube video information'}, {'id': 375, 'key': 'samuelgursky-davinci-resolve-mcp', 'kind': 'mcp', 'name': 'samuelgursky/davinci-resolve-mcp', 'one_line': '🐍 - DaVinci Resolve integration for video editing, color grading, media management and project control', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server integration for DaVinci Resolve Studio', 'url': 'https://github.com/samuelgursky/davinci-resolve-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/samuelgursky/davinci-resolve-mcp', 'downloads': None, 'stars': 3646, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/samuelgursky.png?size=64', 'official_desc': '🐍 - DaVinci Resolve integration for video editing, color grading, media management and project control.', 'stars_own': 3646, 'stars_creator': None, 'stars_own_repo': 'samuelgursky/davinci-resolve-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'DaVinci Resolve MCP Server\n\nEnglish | 简体中文\n\n \n \n \n -blue.svg)\n -18%20tools-blueviolet.svg)\n \n \n \n \n\nA Model Context Protocol (MCP) server that lets AI assistants control DaVinci Resolve Studio through the official Scripting API. It provides full API coverage plus guarded workflow helpers for editing, media pool organization, render setup, review markers, grading, Fusion, Fairlight, project lifecycle tasks, extension authoring, and source-safe media analysis.\n\n \n\nA local browser control panel ships with the server for inspecting Resolve state, running source-safe analysis, drilling into analyzed clips and shots, and editing analysis output inline. See the Control Panel Guide for the full tour.\n\nQuick Start\n\n \n\nBefore connecting, open DaVinci Resolve Studio and set **Preferences > General > External scripting using** to **Local**. (On the **free edition** that preference does not help — see Free edition below.) The npm launcher installs a managed copy under your user application-data directory, then runs the universal Python installer. The installer creates a virtual environment, detects Resolve paths, and can configure Claude Desktop, Claude Code, Cursor, VS Code, Windsurf, Zed, Continue, Cline, Roo Code, OpenCode, Codex CLI, and JetBrains IDEs.\n\nFor source installs:\n\n \n\nFor platform paths, client-specific config, and manual setup, see Installation and Configuration.\n\nThe installer and server check the latest GitHub release for MCP updates. Checks are best-effort and throttled; the server never blocks MCP startup for a prompt. The installer can prompt, snooze, ignore a release, disable checks, or apply an opt-in safe auto-update for clean git checkouts.\n\nFree edition (in-app bridge)\n\nBlackmagic gates *external* scripting to Studio: on the free edition\nscriptapp("Resolve") refuses a foreign process, whatever the preference says.\nThrough Resolve 21.0.x the **Workspace ▸ Scripts** menu was not gated — a script\nlaunched from it is handed the live resolve object (measured on free 21.0.3.7) —\nso the server can reach the free edition through a small script that runs *inside*\nResolve and re-exports it over an authenticated loopback listener. **Resolve 21.1\nmoved Python scripting to Studio.** On free 21.1 the Scripts menu no longer lists\n.py files at all (reported on Fedora 44 in #203; a Lua script in the same folder\nlists normally). Whether the Console still runs Python there is unconfirmed, so\ntreat the bridge as a 21.0.x path until that is measured.\n\n \n\nThe installer and MCP client use ~/.config/davinci-resolve-mcp/bridge.json\nby default. To keep the authenticated bridge config elsewhere, set\nDAVINCI_RESOLVE_BRIDGE_CONFIG while running the installer and in the MCP\nclient environment; both sides will use that path.\n\nOnce that listener is running it is used **automatically** whenever external\nscripting is unavailable — no environment variable required. Setting\nDAVINCI_RESOLVE_BRIDGE=1 *forces* the bridge instead: it becomes the only\ntransport tried, so a bridge that stops answering reports its own fault rather\nthan quietly falling back to another transport. Use it when the bridge is the\npath you intend to depend on.\n\nOn **macOS**, Resolve looks for Python 3 in exactly two places: the\nPYTHON3HOME environment variable, then /usr/local/bin/python3. Homebrew,\npyenv, uv and conda land in neither, so the script silently never appears in the\nmenu. A python.org install works because its installer creates\n/usr/local/bin/python3 — but you do not need one: point Resolve at the\ninterpreter you already have, no sudo required.\n\n \n\nUse launchctl setenv, not export — Resolve is launched from the Dock and\nnever sees your shell\'s environment. Restart Resolve afterwards. A Lua canary is\ninstalled alongside so you can tell "Python not detected" apart from a wrong\nfolder.\n\nTwo things that bite (#182). The prefix must contain **both**\nlib/libpython3.X.dylib and bin/python3 under that exact **unversioned**\nname — Homebrew\'s framework builds often ship only bin/python3.13, which is\nhalf a Python as far as Resolve is concerned, and the installer\'s preflight now\nsays so instead of reporting a usable prefix. And launchctl setenv **does not\nsurvive a reboot**; if scripts stop listing weeks later with no error, that is\nwhy. For something persistent, put an interpreter where Resolve already looks\n(this one needs sudo, and check that /usr/local/bin does not precede your\nnormal Python on PATH):\n\n \n\nValidated on free 21.0.3.7 and Studio 19.1.3.7, both macOS. The Windows paths\nadded in v2.70.1 (issue #106) shipped unverified; reports on free 21.0.1.11\n(issue #109) and free 21.0.3.7 (issue #112) have since shown the bridge\ninstalling, listing and serving from **both** %PROGRAMDATA% and %APPDATA% on\nWindows 11, so those paths are now confirmed rather than assumed. Linux is\nconfirmed as well: a report on free 20.3.2.9 (issue #129, Fedora 43) shows the\nbridge installing to ~/.local/share/DaVinciResolve/Fusion/Scripts/Utility,\nlisting against the system Python — Linux has none of this discovery problem —\nand serving end-to-end. No platform now rests on an assumption: macOS was\nvalidated directly, Windows and Linux on user reports.\n\nNote that the bridge holds its port for as long as it serves. Before v2.70.3 a\nWindows bridge could outlive Resolve and block the next session\'s listener; if\nyou are on an older build and a bridge stops answering, check for a stale\nfuscript.exe still holding the port.\n\nThis is the documented in-app path, not a licence circumvention, but Blackmagic\ncould close it — treat it as a supported-until-it-is-not tier. Loopback only,\nHMAC-signed requests, one-use nonces.\n\nLocal Control Panel\n\nLaunch the single-user local control panel from the repository root:\n\n \n\nThe command starts a loopback-only server and opens the control panel in your browser at a URL that carries a per-launch access token (http://127.0.0.1:8765/#token=…) — use that exact URL; the panel refuses requests without it. To have an AI coding agent do this, ask: **"Open the Resolve MCP control panel for this repo."** Agents should use venv/bin/python -m src.control_panel unless your Python environment is already active. Persisted analysis jobs refresh the local search index automatically after successful slices; the manual Build Index action is for rebuilding from existing reports.\n\nServer Modes\n\n| Mode | Entry point | Tools | Best for |\n|------|-------------|-------|----------|\n| Compound | src/server.py | 37 | Default mode for most assistants. Related Resolve operations are grouped behind action parameters to keep context usage low. |\n| Full / granular | src/server.py --full or src/resolve_mcp_server.py | 390 | Power users who want one MCP tool per Resolve API method. |\n\nThe compound server is recommended unless you specifically need the granular one-tool-per-method surface.\n\nAdvanced server — beyond the scripting API (optional, Node)\n\nThe same package ships a second, optional MCP server: **davinci-resolve-advanced-mcp** (bin\nbin/davinci-resolve-advanced-mcp.mjs). Where the Python server drives a *live* Resolve over the\nsanctioned scripting API, the advanced server does what the API **can\'t** — it reads and edits Resolve\n**files** (.drp / .drt / .drx) and applies DB/XML-level changes **with no Resolve running**, so it\nruns cloud *or* local. 18 tools: drp, drt, drx (per-clip grade codec **plus a deterministic,\noffline grading/QC catalog** — within-camera + cross-camera skin (v2 skin-line metric) + b-roll +\nneutral-patch WB matching, match-to-reference, saturation/black-balance, contrast-normalize, ASC CDL\nimport, lossless grade-transfer + season-look authoring, named-LUT attach, scope reads + intent tags,\nverify-grade, display-referred frame extraction, broadcast-legal QC), offline_ref,\nconform (frame-oracle conform/relink QC + lineage), color_trace (carry grades across a re-conform),\nfusion, audio_plan, fairlight (bus routing), audio, project_read, project_db, pipeline\n(a **DB-as-truth pipeline**: compile YAML', 'readme_raw_len': 29881, 'one_line_zh': '🐍 - DaVinci Resolve 集成：视频剪辑、调色、媒体管理与项目控制', 'one_line_plain': 'Integrate DaVinci Resolve for video editing, color grading, media management and project control'}, {'id': 8059, 'key': 'skillsmp-com-creators-dmtrkovalenko-fframes-skills-fframes-video', 'kind': 'skill', 'name': 'fframes-video', 'one_line': 'Create, animate, review and render videos in code with fframes (Rust, SVG scenes, ffmpeg encoding, Skia GPU…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/dmtrkovalenko/fframes/skills-fframes-video', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/dmtrkovalenko/fframes/skills-fframes-video', 'downloads': None, 'stars': 2774, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/dmtrKovalenko.png?size=64', 'official_desc': 'Create, animate, review and render videos in code with fframes (Rust, SVG scenes, ffmpeg encoding, Skia GPU rendering). Use whenever the user wants a video, animation, motion graphic, explainer, promo, social clip, title card, podcast visual, lyric/caption video or any rendered .mp4/.webm made progr', 'stars_own': None, 'stars_creator': 2870, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'dmtrkovalenko/fframes', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '用 fframes 以代码创建、制作动画、评审并渲染视频（Rust、SVG 场景、ffmpeg 编码与 Skia GPU…）', 'one_line_plain': 'Create, animate and render videos in code using fframes (Rust, SVG scenes, ffmpeg encoding, Skia GPU…)'}, {'id': 5707, 'key': 'skillsmp-com-creators-elvisun-newsjack-skills-coverage-reel', 'kind': 'skill', 'name': 'coverage-reel', 'one_line': 'Turn a user-owned coverage CSV and reviewed press-clip captures into an honest, source-linked earned-media…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/elvisun/newsjack/skills-coverage-reel', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/elvisun/newsjack/skills-coverage-reel', 'downloads': None, 'stars': 1542, 'installs': None, 'mentions': 5, 'score': 503.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/elvisun.png?size=64', 'official_desc': "Turn a user-owned coverage CSV and reviewed press-clip captures into an honest, source-linked earned-media dashboard and a motion-designed highlight reel (MP4) that scrolls each real article to the client's sentence and highlights it. Use for coverage reports, earned-media dashboards, campaign recap", 'stars_own': None, 'stars_creator': 1623, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'elvisun/newsjack', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '把覆盖数据与已审阅的媒体剪报整理成可溯源的真实报道合集。', 'one_line_plain': 'Turn a user-owned coverage CSV and reviewed press-clip captures into an honest, source-linked earned-media…'}, {'id': 528, 'key': 'kimtaeyoon83-mcp-server-youtube-transcript', 'kind': 'mcp', 'name': 'kimtaeyoon83/mcp-server-youtube-transcript', 'one_line': '📇 ☁️ - Fetch YouTube subtitles and transcripts for AI analysis', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'This is an MCP server that allows you to directly download transcripts of YouTube videos.', 'url': 'https://github.com/kimtaeyoon83/mcp-server-youtube-transcript', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/kimtaeyoon83/mcp-server-youtube-transcript', 'downloads': None, 'stars': 603, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/kimtaeyoon83.png?size=64', 'official_desc': '📇 ☁️ - Fetch YouTube subtitles and transcripts for AI analysis.', 'stars_own': 603, 'stars_creator': None, 'stars_own_repo': 'kimtaeyoon83/mcp-server-youtube-transcript', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'YouTube Transcript Server\n \n\n \n\nA Model Context Protocol server that enables retrieval of transcripts from YouTube videos. This server provides direct access to video captions and subtitles through a simple interface.\n\n \n\nInstalling via Smithery\n\nTo install YouTube Transcript Server for Claude Desktop automatically via Smithery:\n\n \n\nComponents\n\nTools\n\n- **get_transcript**\n - Extract transcripts from YouTube videos\n - Inputs:\n - url (string, required): YouTube video URL, Shorts URL, or video ID\n - lang (string, optional, default: "en"): Language code for transcript (e.g., \'ko\', \'en\'). Automatically falls back to available languages if requested language is not found.\n - include_timestamps (boolean, optional, default: false): Include timestamps in output (e.g., \'[0:05] text\')\n - strip_ads (boolean, optional, default: true): Filter out sponsorships, ads, and promotional content from transcript based on chapter markers\n\n- **analyze_video** (optional, requires a TwelveLabs API key)\n - Analyze a video with TwelveLabs Pegasus, a video-understanding model. Unlike get_transcript, this reasons over what is *shown* on screen, so it produces useful summaries and answers even for videos with little or no speech (demos, gameplay, b-roll, music videos).\n - Inputs:\n - url (string, required): A publicly reachable **direct video URL** (e.g. an .mp4/.mov/.webm link or a pre-signed URL). TwelveLabs fetches the file server-side, so a YouTube watch page URL will not work — it serves HTML, not a raw video stream.\n - prompt (string, optional): Instruction or question for the model (e.g. "Summarize this video in 3 sentences" or "What products are shown?"). Defaults to a general summary.\n - model (string, optional, default: "pegasus1.2"): Pegasus model to use ("pegasus1.2" or "pegasus1.5").\n - max_tokens (number, optional, default: 2048): Maximum response length in tokens.\n - Requires the TWELVELABS_API_KEY environment variable. The transcript tool works without it; analysis is fully opt-in. Grab a free key at twelvelabs.io — there\'s a generous free tier.\n\nKey Features\n\n- Support for multiple video URL formats (including YouTube Shorts)\n- Language-specific transcript retrieval with automatic fallback\n- Optional timestamps for referencing specific moments\n- Built-in ad/sponsorship filtering (enabled by default)\n- Zero external dependencies for transcript fetching\n- Detailed metadata in responses\n\nConfiguration\n\nTo use with Claude Desktop, add this server configuration:\n\n \n\nTo enable the optional analyze_video tool, add your TwelveLabs API key:\n\n \n\nInstall via tool\n\nmcp-get A command-line tool for installing and managing Model Context Protocol (MCP) servers.\n\n \n\nAwesome-mcp-servers \nawesome-mcp-servers A curated list of awesome Model Context Protocol (MCP) servers.\n\nDevelopment\n\nPrerequisites\n\n- Node.js 18 or higher\n- npm or yarn\n\nSetup\n\nInstall dependencies:\n \n\nBuild the server:\n \n\nFor development with auto-rebuild:\n \n\nTesting\n\n \n\nDebugging\n\nSince MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector for development:\n\n \n\nRunning evals\n\nThe evals package loads an mcp client that then runs the index.ts file, so there is no need to rebuild between tests. You can load environment variables by prefixing the npx command. Full documentation can be found here.\n\n \nError Handling\n\nThe server implements robust error handling for common scenarios:\n- Invalid video URLs or IDs\n- Unavailable transcripts\n- Language availability issues\n- Network errors\n\nUsage Examples\n\n1. Get transcript by video URL:\n \n\n2. Get transcript by video ID:\n \n\n3. Get transcript from YouTube Shorts:\n \n\n4. Get transcript with timestamps:\n \n\n5. Get raw transcript without ad filtering:\n \n\n6. How to Extract YouTube Subtitles in Claude Desktop App\n \n\nSecurity Considerations\n\nThe server:\n- Validates all input parameters\n- Handles YouTube API errors gracefully\n- Implements timeouts for transcript retrieval\n- Provides detailed error messages for troubleshooting\n\nLicense\n\nThis MCP server is licensed under the MIT License. See the LICENSE file for details.', 'readme_raw_len': 4102, 'one_line_zh': '📇 ☁️ - 获取 YouTube 字幕与文字记录，供 AI 分析', 'one_line_plain': 'Fetch YouTube subtitles and transcripts for AI analysis'}, {'id': 4173, 'key': 'anaisbetts-mcp-youtube', 'kind': 'mcp', 'name': 'anaisbetts/mcp-youtube', 'one_line': '📇 ☁️ - Fetch YouTube subtitles', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model-Context Protocol Server for YouTube', 'url': 'https://github.com/anaisbetts/mcp-youtube', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/anaisbetts/mcp-youtube', 'downloads': None, 'stars': 551, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/anaisbetts.png?size=64', 'official_desc': '— A Model-Context Protocol Server for YouTube ☆`550`', 'stars_own': 551, 'stars_creator': None, 'stars_own_repo': 'anaisbetts/mcp-youtube', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'YouTube MCP Server\n\nUses yt-dlp to download subtitles from YouTube and connects it to claude.ai via Model Context Protocol. Try it by asking Claude, "Summarize the YouTube video >". Requires yt-dlp to be installed locally e.g. via Homebrew.\n\nHow do I get this working?\n\n1. Install yt-dlp (Homebrew and WinGet both work great here)\n1. Now, install this via mcp-installer, use the name @anaisbetts/mcp-youtube', 'readme_raw_len': 407, 'one_line_zh': '📇 ☁️ - 获取 YouTube 字幕', 'one_line_plain': 'Fetch YouTube subtitles'}, {'id': 5589, 'key': 'abubakrchan-product-launch-motion', 'kind': 'repo', 'name': 'AbubakrChan/product-launch-motion', 'one_line': 'Claude Code skill for Dribbble-level product launch videos from code', 'one_line_ok': 1, 'why_bad': None, 'fields': '["Agent 技能"]', 'desc_raw': 'Claude Code skill for Dribbble-level product launch videos from code. One shape that never cuts, springs, a cursor that drives every change, beat-synced SFX, 60 fps motion blur. v2: use with Claude Opus 5.5.', 'url': 'https://github.com/AbubakrChan/product-launch-motion', 'source': 'github', 'source_url': 'https://github.com/AbubakrChan/product-launch-motion', 'downloads': None, 'stars': 323, 'installs': None, 'mentions': 2, 'score': 203.38, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/AbubakrChan.png?size=64', 'official_desc': 'Claude Code skill for Dribbble-level product launch videos from code. One shape that never cuts, springs, a cursor that drives every change, beat-synced SFX, 60 fps motion blur. v2: use with Claude Opus 5.5.', 'stars_own': 323, 'stars_creator': None, 'stars_own_repo': 'AbubakrChan/product-launch-motion', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx skills', 'install_src': 'HEAD/README.md', 'install_conf': 'low', 'desc_src': 'github-api', 'readme_body': "Product Launch Motion — a Claude Code Skill\n\nPaste your website URL. Get a product launch video. That's it.\n\n \n\nAlready installed it? Get the latest version with npx skills update.\n\n \n\n**No Claude Code? Get the film made for you: a polished launch video in 30 minutes, from $20 → launchvideos.app**\n\nThen, in Claude Code:\n\n> Make a launch video for https://your-website.com\n\nClaude reads your site, writes the story, directs every scene and renders a 60 fps MP4 with\nmusic, synced sound effects and real motion blur. No editing, no After Effects, no templates.\nThe film above started the same way: a URL, then a few rounds of notes.\n\n \n v2 is here. Use it with Claude Opus 5.5 to get results like these films. \n\nThis skill has been rebuilt on a newer, simpler stack: plain HTML and closed-form springs\ninstead of GSAP and HyperFrames, music-first timing instead of voiceover, and Playwright\nsubframes with ffmpeg motion blur. **Use it with Claude Opus 5.5 to get results like these\nfilms.** The original v1 (190 ⭐, thank you) is kept in legacy/.\n\n \n\n> 🔊 **Download it and turn the sound on.** The preview above is silent. The real film is cut to the beat\n> of its music, and every click, whoosh and number roll lands on a drum hit.\n> **Download the film (1080p, 60 fps, with sound)**\n> to watch it as intended.\n\n30 s, made for cheerful.ai. No After Effects, no stock templates,\nno AI video model: every frame is HTML computed from time. Bonus: the same film with a\nWebGL glass pass\n(refractive 3D shapes floating through every scene).\n\n \n \n \n\n**▶ One shape, never cut**\n(14 s seamless loop, 1440×1440, with sound)\n\nThe idea the whole skill is built on. One element morphs through eleven UI states:\nbutton → loader → check → dynamic island → video reel → slider → toggle → tabs → chart →\n⌘K palette → toast → button. Its content swaps with a short blur, the cursor does every\nclick and drag, and the last frame is the first frame.\n\n \n\n---\n\nWhat makes it look expensive\n\nThese are the rules the skill enforces. Each one came from a review note on a real film\n(references/prompts.md has the prompts and notes, verbatim).\n\n- **One shape, never cut.** Every state is the same element morphing size, radius and\n colour. Transitions are morphs, camera pushes, or zooms into an element that *becomes*\n the next scene.\n- **Blur content swaps.** Text inside a morphing container exits short and early, then the\n new text springs in. Nothing overlaps.\n- **Springs, a tiny overshoot at most.** Closed-form damped springs, never bouncy easing.\n Toggle knobs and tab indicators stretch because their two edges ride different springs.\n- **A cursor drives everything.** Real clicks with press-scale and ripple, real drags that\n spring back on release. The product's own agent can take over as a second cursor.\n- **The camera frames every state.** An empty frame is a bug. Pull-backs reveal scale: one\n email becomes a wall of hundreds.\n- **Music first.** The beat grid is measured with numpy, the drop lands on the big moment,\n and every synthesized UI sound is placed by its own measured peak, so it hits the beat.\n- **Real data, original scenes.** Real numbers on odometers, customer logos slammed one per\n beat, and nothing copied from the product's website.\n- **You get a director.** It makes the creative calls itself and logs each one as a\n DECISION: line you can overturn in one reply.\n\nWhat's in the box\n\n| Path | What it is |\n|---|---|\n| SKILL.md | The creative direction and the process Claude follows |\n| references/prompts.md | The original prompts and every review note, verbatim. Swap the product and reuse them |\n| references/technique.md | Springs, camera, cursor, odometer, audio and render code patterns |\n| legacy/ | The v1 skill: voiceover-led films with GSAP + HyperFrames, 34 traps |\n\nRequirements\n\n| Need | For |\n|---|---|\n| Claude Code with **Opus 5.5** | Directing and building the film. Smaller models follow the rules but miss the detail |\n| Node ≥ 20 + Playwright | Rendering |\n| FFmpeg | Motion blur, encoding, loudness checks |\n| Python 3 + numpy | Beat grid and synthesized SFX |\n| A ~120 BPM royalty-free song | Optional. Check each track's licence; some free libraries mix in restricted items |\n\nEverything runs locally and nothing costs money.\n\nHow it works\n\n \n\nBecause every frame is a pure function of time, renders are deterministic: seek to any\nmoment and you get the same pixels, which is what makes per-beat QA and parallel rendering\npossible.\n\nv1 → v2\n\n| | v1 (legacy) | v2 |\n|---|---|---|\n| Timing | Locked to voiceover words | Locked to the music's measured beat grid |\n| Motion | GSAP timelines | Closed-form springs, pure seek(t) |\n| Renderer | HyperFrames | Playwright subframes + ffmpeg tmix motion blur |\n| Structure | Shots and transitions | One shape that never cuts |\n| Sound | Library SFX, levelled | Synthesized SFX placed by measured peak |\n| Depth | CSS camera rig | Camera springs + optional WebGL glass |\n\nv1 is still the better choice for a narrated explainer. It lives on in legacy/.\n\nUpdating\n\n- **Installed v1 (before 13 October 2026)?** Run npx skills update. Your product-launch-motion skill is\n replaced by v2.\n- **Installed v2 on 13 or 14 October 2026?** It was briefly named motion-launch-video, so update can't\n find it. Reinstall once:\n \n- **Want to keep v1 for narrated explainers?** Install it alongside v2:\n npx skills add AbubakrChan/product-launch-motion/legacy\n\nMade by\n\nMuhammad Abubakr, who builds Cheerful. Made a film with this skill?\nTag me on X and I'll share it.\n\n \n \n\nLicense\n\nMIT for the skill and references — see LICENSE.\n\n**The sample films do not travel.** They were made for Cheerful and are theirs; see\nNOTICE for that and for third-party credits (music, footage, fonts).", 'readme_raw_len': 5744, 'one_line_zh': '从代码生成 Dribbble 级产品发布视频的 Claude Code 技能', 'one_line_plain': 'Claude Code skill that lets you create Dribbble-level product launch videos from code'}, {'id': 1908, 'key': 'cyberchitta-llm-context-py', 'kind': 'mcp', 'name': 'cyberchitta/llm-context.py', 'one_line': '🐍 🏠 - Share code context with LLMs via MCP or the clipboard', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Share code with LLMs via Model Context Protocol or clipboard. Rule-based customization enables easy switching between different tasks (like code review and documentation). Includes smart code outlining.', 'url': 'https://github.com/cyberchitta/llm-context.py', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/cyberchitta/llm-context.py', 'downloads': None, 'stars': 307, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/cyberchitta.png?size=64', 'official_desc': '🐍 🏠 - Share code context with LLMs via MCP or the clipboard.', 'stars_own': 307, 'stars_creator': None, 'stars_own_repo': 'cyberchitta/llm-context.py', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'LLM Context\n\n \n \n \n\n**Context curation your coding agent does for itself.** lc-init installs a skill that teaches the agent to work out which files a task actually needs, write that down as a composable rule, check the rule against the codebase, and pack the result — for its own context, for a chat you paste into, or for a sub-agent it dispatches.\n\nGetting the right context into an LLM is friction-heavy: finding and copying files by hand wastes time, too much context hits token limits, too little misses what matters, and follow-up file requests mean more manual fetching. The usual answers are to send everything, or to have a person curate by hand. A rule describes the selection once, and it is a thing an agent can author, verify and reuse — the tooling handles packing, follow-up fetches, and change tracking.\n\nDocumentation lives in the skill\n\nThe full documentation is the **lc-curate-context skill**, installed into your project by lc-init. It is written to be read by an agent, and it is the only copy — this README is a landing page, not a manual.\n\n| File | Contents |\n| --- | --- |\n| SKILL.md | Writing a rule; the workflow; packing for a sub-agent |\n| COMMANDS.md | CLI and MCP reference, including output routing |\n| SYNTAX.md | Rule file schema and every field |\n| PATTERNS.md | Reusable rule shapes |\n| EXAMPLES.md | Worked examples |\n| TROUBLESHOOTING.md | Failure cases |\n\nFind them at .claude/skills/lc-curate-context/ after lc-init. In Claude Code the skill loads automatically; elsewhere, read the files directly.\n\nInstallation\n\n \n\nUpgrading: uv tool upgrade llm-context, then any lc-* command refreshes the skill, rules and templates in place.\n\nThree ways to use it\n\n**Your coding agent, curating for itself** — this is the primary path. After lc-init the agent has the lc-curate-context skill, so "get me focused context for the auth refactor" becomes a rule it writes and verifies without you naming files.\n\n \n\nlc-preview reports the exact file lists, the size, and — via the code graph — files defining symbols the selection uses but does not include. That last section is how the agent catches the module it forgot, before spending a turn on a wrong answer.\n\n**A sub-agent, via a pipe** — a dispatcher writes the prompt, llm-context supplies the files.\n\n \n\n-r routes output to stdout; without it lc-context copies to the clipboard, so a pipe or redirect gets nothing but log lines. These commands take no bare positional rule name — always -r. -a renders the pack\'s fetch instructions as shell commands the child runs itself, so from the repo root it can call lc-missing against the pack\'s timestamp for anything left out. See SKILL.md "Packing for a Sub-Agent".\n\n**A chat, via MCP or the clipboard** — for models that aren\'t driving a terminal.\n\n \n\nWith MCP the model pulls files it wasn\'t given and notices ones that changed underneath it, through lc_missing, lc_changed, lc_outlines and lc_preview. Without it, lc-select then lc-context puts the pack on your clipboard to paste anywhere.\n\nRules in one minute\n\nA rule is YAML frontmatter plus optional markdown:\n\n \n\nYou rarely write one by hand — the skill does, and lc-preview is how it checks its work. Rules compose, and are named by category: prm- produces a context, flt- controls file inclusion, ins- supplies guidelines, sty- enforces coding standards, exc- configures excerpting. Files you expect to edit go in full-files; supporting code goes in excerpted-files, where it is reduced to signatures and definitions. See SYNTAX.md and PATTERNS.md.\n\nWhat a generated context contains\n\nComplete contents for full files, structural excerpts for the rest, a filtered file listing marking what is and isn\'t included, and a timestamp that lc-missing and lc-changed resolve against.\n\nA pack is always partial — the listing is filtered by your .gitignore files and by the rule before anything is marked excluded, so files can exist that it never mentions. The header reports how many files are full, outlined and excerpted, so a consumer can check what it actually received rather than trusting the rule.\n\nLearn More\n\n- Design Philosophy — why llm-context exists\n- Real-world Examples — using full context effectively\n\nLicense\n\nApache License, Version 2.0. See LICENSE for details.\n\n---\n\nDeveloped in collaboration with several Claude models and Groks, using LLM Context itself to share code during development. All code is heavily human-curated by @restlessronin.', 'readme_raw_len': 4444, 'one_line_zh': '通过 MCP 或剪贴板与 LLM 共享代码上下文', 'one_line_plain': 'Share code context with LLMs via MCP or the clipboard'}, {'id': 323, 'key': 'burningion-video-editing-mcp', 'kind': 'mcp', 'name': 'burningion/video-editing-mcp', 'one_line': '🐍 - Add, analyze, search and generate video edits from your Video Jungle collection', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP Interface for Video Jungle', 'url': 'https://github.com/burningion/video-editing-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/burningion/video-editing-mcp', 'downloads': None, 'stars': 294, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/burningion.png?size=64', 'official_desc': '🐍 - Add, analyze, search and generate video edits from your Video Jungle collection.', 'stars_own': 294, 'stars_creator': None, 'stars_own_repo': 'burningion/video-editing-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Video Editor MCP server\n\n \n\nSee a demo here: https://www.youtube.com/watch?v=KG6TMLD8GmA\n\nUpload, edit, search, and generate videos from everyone\'s favorite LLM and Video Jungle.\n\nYou\'ll need to sign up for an account at Video Jungle in order to use this tool, and add your API key.\n\n \n\nComponents\n\nResources\n\nThe server implements an interface to upload, generate, and edit videos with:\n- Custom vj:// URI scheme for accessing individual videos and projects\n- Each project resource has a name, description\n- Search results are returned with metadata about what is in the video, and when, allowing for edit generation directly\n\nPrompts\n\nComing soon.\n\nTools\n\nThe server implements a few tools:\n- add-video\n - Add a Video File for analysis from a URL. Returns an vj:// URI to reference the Video file\n- create-videojungle-project\n - Creates a Video Jungle project to contain generative scripts, analyzed videos, and images for video edit generation\n- edit-locally\n - Creates an OpenTimelineIO project and downloads it to your machine to open in a Davinci Resolve Studio instance (Resolve Studio _must_ already be running before calling this tool.) \n- generate-edit-from-videos\n - Generates a rendered video edit from a set of video files\n- generate-edit-from-single-video\n - Generate an edit from a single input video file\n- get-project-assets\n - Get assets within a project for video edit generation.\n- search-videos\n - Returns video matches based upon embeddings and keywords\n- update-video-edit\n - Live update a video edit\'s information. If Video Jungle is open, edit will be updated in real time.\n\nUsing Tools in Practice\n\nIn order to use the tools, you\'ll need to sign up for Video Jungle and add your API key.\n\n**add-video**\n\nHere\'s an example prompt to invoke the add-video tool:\n\n \n\nThis will download a video from a URL, add it to your library, and analyze it for retrieval later. Analysis is multi-modal, so both audio and visual components can be queried against.\n\n**search-videos**\n\nOnce you\'ve got a video downloaded and analyzed, you can then do queries on it using the search-videos tool:\n\n \n\nSearch results contain relevant metadata for generating a video edit according to details discovered in the initial analysis.\n\n**search-local-videos**\n\nYou must set the environment variable LOAD_PHOTOS_DB=1 in order to use this tool, as it will make Claude prompt to access your files on your local machine.\n\nOnce that\'s done, you can search through your Photos app for videos that exist on your phone, using Apple\'s tags.\n\nIn my case, when I search for "Skateboard", I get 1903 video files.\n\n \n\n**generate-edit-from-videos**\n\nFinally, you can use these search results to generate an edit:\n\n \n\n(Currently), the video edits tool relies on the context within the current chat. \n\n**generate-edit-from-single-video**\n\nFinally, you can cut down an edit from a single, existing video:\n\n \n\nConfiguration\n\nYou must login to Video Jungle settings, and get your API key. Then, use this to start Video Jungle MCP:\n\n \n\nTo allow this MCP server to search your Photos app on MacOS:\n\n \nQuickstart\n\nInstall\n\nInstalling via Smithery\n\nTo install Video Editor for Claude Desktop automatically via Smithery:\n\n \n\nClaude Desktop\n\nYou\'ll need to adjust your claude_desktop_config.json manually:\n\nOn MacOS: ~/Library/Application\\ Support/Claude/claude_desktop_config.json\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n \n Published Server Configuration \n \n \n \n Development/Unpublished Servers Configuration \n \n \n\n With local Photos app access enabled (search your Photos app):\n\n \n\n \n\nBe sure to replace the directories with the directories you\'ve placed the repository in on **your** computer.\n\nDevelopment\n\nBuilding and Publishing\n\nTo prepare the package for distribution:\n\n1. Sync dependencies and update lockfile:\n \n\n2. Build package distributions:\n \n\nThis will create source and wheel distributions in the dist/ directory.\n\n3. Publish to PyPI:\n \n\nNote: You\'ll need to set PyPI credentials via environment variables or command flags:\n- Token: --token or UV_PUBLISH_TOKEN\n- Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD\n\nMCP Server Registry\n\n \n\nDebugging\n\nSince MCP servers run over stdio, debugging can be challenging. For the best debugging\nexperience, we strongly recommend using the MCP Inspector.\n\nYou can launch the MCP Inspector via npm with this command:\n\n(Be sure to replace YOURDIRECTORY and YOURAPIKEY with the directory this repo is in, and your Video Jungle API key, found in the settings page.)\n\n \n\nUpon launching, the Inspector will display a URL that you can access in your browser to begin debugging.\n\nAdditionally, I\'ve added logging to app.log in the project directory. You can add logging to diagnose API calls via a:\n\n \n\nA reasonable way to follow along as you\'re workin on the project is to open a terminal session and do a:', 'readme_raw_len': 4884, 'one_line_zh': '🐍 - 对 Video Jungle 收藏中的视频进行添加、分析、搜索和剪辑生成', 'one_line_plain': 'Add, analyze, search and generate video edits from your Video Jungle collection'}, {'id': 7809, 'key': 'faviovazquez-showtime', 'kind': 'repo', 'name': 'FavioVazquez/showtime', 'one_line': 'A local video studio for your coding agent', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '["Agent 技能"]', 'desc_raw': '', 'url': 'https://github.com/FavioVazquez/showtime', 'source': 'github', 'source_url': 'https://github.com/FavioVazquez/showtime', 'downloads': None, 'stars': 265, 'installs': None, 'mentions': 5, 'score': 503.52, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-16', 'last_seen': '2026-10-19', 'times_seen': 4, 'icon': 'https://github.com/FavioVazquez.png?size=64', 'official_desc': 'A local video studio for your coding agent. Describe a video in one sentence: your agent directs, your machine renders. Claude Code, Codex, Cursor, Devin and more. No API keys, no uploads.', 'stars_own': 265, 'stars_creator': None, 'stars_own_repo': 'FavioVazquez/showtime', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx skills', 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '面向编码 Agent 的本地视频工作室。', 'one_line_plain': 'Edit and run code locally with a video studio built for coding agents'}, {'id': 4299, 'key': 'seym0n-tiktok-mcp', 'kind': 'mcp', 'name': 'Seym0n/tiktok-mcp', 'one_line': '📇 ☁️ - Interact with TikTok videos', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol (MCP) with TikTok integration', 'url': 'https://github.com/Seym0n/tiktok-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Seym0n/tiktok-mcp', 'downloads': None, 'stars': 203, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Seym0n.png?size=64', 'official_desc': '— Model Context Protocol (MCP) with TikTok integration ☆`201`', 'stars_own': 203, 'stars_creator': None, 'stars_own_repo': 'Seym0n/tiktok-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "TikTok MCP\n\n \n\nThe TikTok MCP integrates TikTok access into Claude AI and other apps via TikNeuron. This TikTok MCP allows you to\n- analyze TikTok videos to determine virality factors\n- get content from TikTok videos\n- chat with TikTok videos\n\nAvailable Tools\n\ntiktok_get_subtitle\n\n**Description:** \nGet the subtitle (content) for a TikTok video url. This is used for getting the subtitle, content or context for a TikTok video. If no language code is provided, the tool will return the subtitle of automatic speech recognition.\n\n**Input Parameters:**\n- tiktok_url (required): TikTok video URL, e.g., https://www.tiktok.com/@username/video/1234567890 or https://vm.tiktok.com/1234567890\n- language_code (optional): Language code for the subtitle, e.g., en for English, es for Spanish, fr for French, etc.\n\ntiktok_get_post_details\n\n**Description:** \nGet the details of a TikTok post. Returns the details of the video like:\n- Description\n- Video ID\n- Creator username\n- Hashtags\n- Number of likes, shares, comments, views and bookmarks\n- Date of creation\n- Duration of the video\n- Available subtitles with language and source information\n\n**Input Parameters:**\n- tiktok_url (required): TikTok video URL, e.g., https://www.tiktok.com/@username/video/1234567890 or https://vm.tiktok.com/1234567890, or just the video ID like 7409731702890827041\n\ntiktok_search\n\n**Description:** \nSearch for TikTok videos based on a query. Returns a list of videos matching the search criteria with their details including description, video ID, creator, hashtags, engagement metrics, date of creation, duration and available subtitles, plus pagination metadata for continuing the search.\n\n**Input Parameters:**\n- query (required): Search query for TikTok videos, e.g., 'funny cats', 'dance', 'cooking tutorial'\n- cursor (optional): Pagination cursor for getting more results\n- search_uid (optional): Search session identifier for pagination\n\nInstall via MCPB (Easiest)\n\nThe easiest way to install the TikTok MCP in Claude Desktop is via the MCPB bundle — no cloning or building required.\n\n1. Download the latest tiktok-mcp.mcpb from the Releases page\n2. Open the .mcpb file — Claude Desktop will show an installation dialog\n3. Enter your TikNeuron API Key when prompted\n\nThat's it. Claude Desktop handles the rest automatically.\n\nRequirements\n\nFor this TikTok MCP, you need\n- NodeJS v18 or higher (https://nodejs.org/)\n- Git (https://git-scm.com/)\n- TikNeuron Account and MCP API Key (https://tikneuron.com/tools/tiktok-mcp)\n\nSetup\n\n1. Clone the repository\n \n\n2. Install dependencies\n \n\n3. Build project\n \n\nThis creates the file build\\index.js\n\nUsing in Claude AI\n\nAdd the following entry to mcpServers:\n\n \n\nand replace path with the path to TikTok MCP and XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX with TIkNeuron API Key\n\nso that mcpServers will look like this:", 'readme_raw_len': 2832, 'one_line_zh': '与 TikTok 视频进行交互', 'one_line_plain': 'Interact with TikTok videos'}, {'id': 3427, 'key': 'lfnovo-content-core', 'kind': 'mcp', 'name': 'lfnovo/content-core', 'one_line': '🐍 🏠 - Extract content from web pages, PDFs, Word documents, YouTube transcripts, audio and video with…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Extract what matters from any media source', 'url': 'https://github.com/lfnovo/content-core', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/lfnovo/content-core', 'downloads': None, 'stars': 175, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/lfnovo.png?size=64', 'official_desc': '🐍 🏠 - Extract content from web pages, PDFs, Word documents, YouTube transcripts, audio and video with automatic engine selection and structured JSON output.', 'stars_own': 175, 'stars_creator': None, 'stars_own_repo': 'lfnovo/content-core', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Content Core\n\n \n \n \n \n \n \n \n \n\nExtract, process, and summarize content from URLs, files, and text through a unified async Python API, CLI, or MCP server.\n\nSupported Formats\n\n| Category | Formats |\n|----------|---------|\n| Web | URLs, HTML pages, YouTube videos, Reddit posts |\n| Documents | PDF, DOCX, PPTX, XLSX, ODT, ODS, ODP, EPUB, HTML, Markdown, plain text |\n| Media | MP3, WAV, M4A, FLAC, OGG (audio); MP4, AVI, MOV, MKV (video) |\n\nQuick Start\n\n \n\n \n\nOr with zero install:\n\n \n\nCLI Usage\n\nContent Core provides a unified content-core command with subcommands for extraction, summarization, and MCP server.\n\nExtract\n\n \n\nSummarize\n\n \n\nMCP Server\n\n \n\nConfiguration\n\n \n\nConfig is stored in ~/.content-core/config.toml. Priority: command flags > env vars > config file > defaults.\n\nZero-Install with uvx\n\nAll commands work without installation using uvx:\n\n \n\nPython API\n\nExtraction\n\n \n\nSummarization\n\n \n\nConfiguration\n\n \n\nMCP Integration\n\nContent Core includes a Model Context Protocol (MCP) server for use with Claude Desktop and other MCP-compatible applications.\n\n \n \n \n\nAdd to your claude_desktop_config.json:\n\n \n\nThe MCP server exposes two tools: extract_content and summarize_content. Both return plain text.\n\nFor detailed setup, see the MCP documentation.\n\nAgent Skill (Claude Code & Codex)\n\nContent Core ships an Agent Skill that teaches AI agents how to use it for extracting content from external sources. This repository is also a plugin marketplace, so the skill installs natively in both harnesses.\n\n**Claude Code** — add the marketplace and install the plugin:\n\n \n\n**Codex** — the repository carries a Codex plugin manifest (.codex-plugin/plugin.json) and marketplace catalog (.agents/plugins/marketplace.json) pointing at the same skill.\n\n**Manual fallback** — copy the skill file directly into your project:\n\n \n\nOnce installed, the agent can use content-core to extract content from URLs, documents, and media files — either via CLI (uvx content-core) or MCP if configured.\n\nAI Providers\n\nContent Core uses Esperanto to support multiple LLM and STT providers. Switch providers by changing the config — no code changes needed:\n\n \n\nSupported providers include OpenAI, Anthropic, Google, Groq, DeepSeek, Ollama, and more. See the Esperanto documentation for the full list.\n\nConfiguration\n\nContent Core uses ContentCoreConfig powered by pydantic-settings. Settings are resolved in priority order: constructor args > env vars (CCORE_*) > config file (~/.content-core/config.toml) > defaults.\n\nEnvironment Variables\n\n| Variable | Description | Default |\n|----------|-------------|---------|\n| CCORE_URL_ENGINE | URL extraction engine (auto, simple, firecrawl, jina, crawl4ai) | auto |\n| CCORE_DOCUMENT_ENGINE | Document extraction engine (auto, simple, docling) — docling raises ConfigurationError if the extra is not installed; auto falls back silently | auto |\n| CCORE_AUDIO_CONCURRENCY | Concurrent audio transcriptions (1-10) | 3 |\n| CCORE_AUDIO_SEGMENT_MINUTES | Length of the segments long audio is split into; 0 sends the file whole | 10 |\n| CRAWL4AI_API_URL | Crawl4AI Docker API URL (omit for local browser mode) | - |\n| CRAWL4AI_API_TOKEN | Bearer token for the Crawl4AI Docker API (required by Crawl4AI >= 0.9.0) | - |\n| FIRECRAWL_API_URL | Custom Firecrawl API URL for self-hosted instances or Firecrawl-compatible backends (e.g. fastCRW) | - |\n| CCORE_FIRECRAWL_PROXY | Firecrawl proxy mode (auto, basic, stealth) | auto |\n| CCORE_FIRECRAWL_WAIT_FOR | Wait time in ms before extraction | 3000 |\n| CCORE_LLM_PROVIDER | LLM provider for summarization | - |\n| CCORE_LLM_MODEL | LLM model for summarization | - |\n| CCORE_STT_PROVIDER | Speech-to-text provider | - |\n| CCORE_STT_MODEL | Speech-to-text model | - |\n| CCORE_STT_TIMEOUT | Speech-to-text timeout in seconds | - |\n| CCORE_YOUTUBE_LANGUAGES | Preferred YouTube transcript languages | - |\n| CCORE_YOUTUBE_COOKIES_FILE | Path to a Netscape cookies.txt for YouTube transcripts (missing file raises ConfigurationError) | - |\n| CCORE_YOUTUBE_PROXY | Proxy URL for YouTube transcripts, e.g. http://user:pass@host:port | - |\n\nAPI keys for external services are set via their standard environment variables (e.g., OPENAI_API_KEY, FIRECRAWL_API_KEY, JINA_API_KEY).\n\nProxy Configuration\n\nContent Core reads standard HTTP_PROXY / HTTPS_PROXY / NO_PROXY environment variables automatically. No additional configuration is needed.\n\nYouTube on Blocked Networks\n\nIf YouTube extraction fails with IpBlocked/RequestBlocked on a flagged IP (cloud hosts, VPNs), either point CCORE_YOUTUBE_COOKIES_FILE at a cookies.txt exported from a logged-in browser (re-export it when the cookies expire), or set CCORE_YOUTUBE_PROXY to a **residential** proxy — datacenter proxies get blocked or CAPTCHA'd. See docs/usage.md.\n\nOptional Dependencies\n\n \n\nUsing with LangChain\n\nWhen installed with the langchain extra, Content Core provides LangChain-compatible tool wrappers:\n\n \n\nDocumentation\n\n- Usage Guide -- Python API details, configuration, and examples\n- Processors -- How content extraction works for each format\n- MCP Server -- Claude Desktop and MCP integration\n\nDevelopment\n\n \n\nLicense\n\nThis project is licensed under the MIT License.\n\nContributing\n\nContributions are welcome! Please see our Contributing Guide for details.", 'readme_raw_len': 5279, 'one_line_zh': '从网页、PDF、Word 文档、YouTube 字幕、音频和视频中提取内容', 'one_line_plain': 'Content extraction tool lets users pull text from web pages, PDFs, Word documents, YouTube transcripts, and audio or video files'}, {'id': 3165, 'key': 'video-creator-ffmpeg-mcp-git', 'kind': 'mcp', 'name': 'video-creator/ffmpeg-mcp', 'one_line': 'Local video search, trimming, stitching and playback through conversation, using the ffmpeg command line', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/video-creator/ffmpeg-mcp.git', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/video-creator/ffmpeg-mcp.git', 'downloads': None, 'stars': 153, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/video-creator.png?size=64', 'official_desc': 'he following tools: find_video_path The parameters are directory and file name, file name can be complete, or is not suffixed, recursive search in the directory, return the full path get_video_info The parameters are video path, return the video info, linkes duration/fps/codec/width/height.', 'stars_own': 153, 'stars_creator': None, 'stars_own_repo': 'video-creator/ffmpeg-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'FFmpeg-MCP\nUsing ffmpeg command line to achieve an mcp server, can be very convenient, through the dialogue to achieve the local video search, tailoring, stitching, playback and other functions\n\n \n \n \n\nSupport Tools\nThe server implements the following tools: \n- find_video_path\n The parameters are directory and file name, file name can be complete, or is not suffixed, recursive search in the directory, return the full path\n- get_video_info\n The parameters are video path, return the video info, linkes duration/fps/codec/width/height.\n- clip_video\n The parameter is the file path, start time, end time or duration, and returns the trimmed file path\n- concat_videos\n The parameters are the list of files, the output path, and if the video elements in the list of files, such as width, height, frame rate, etc., are consistent, quick mode synthesis is automatically used\n- play_video\n Play video/audio with ffplay, support many format, like mov/mp4/avi/mkv/3gp, video_path: video path speed: play rate loop: play count\n- overlay_video\n Two video overlay. \n background_video: backgroud video path \n overlay_video: front video path \n output_path: output video path \n position: relative location \n dx: x offset \n dy: y offset \n- scale_video\n Video scale. \n video_path: in video path \n width: out video width, -2 keep aspect \n height: out video height, -2 keep aspect \n output_path: output video path \n- extract_frames_from_video\n Extract images from a video. \n Parameters: \n video_path (str): The path to the video. \n fps (int): Extract one frame every specified number of seconds. If set to 0, extract all frames; if set to 1, extract one frame per second. \n output_folder (str): The directory where the images will be saved. \n format (int): The format of the extracted images; 0: PNG, 1: JPG, 2: WEBP. \n total_frames (int): The maximum number of frames to extract. If set to 0, there is no limit \n \nMore features are coming\n\nInstallation procedure\n1. Download project\n \n\n2. Configuration in Cline\n \nNote: the value:/Users/XXX/Downloads/ffmpeg in args need to replace the actual download ffmpeg-mcp directory\n\nSupported platforms\nCurrently, only macos platforms are supported, including ARM64 or x86_64', 'readme_raw_len': 2201, 'one_line_zh': '通过对话完成本地视频的检索、裁剪、拼接与播放，底层使用 ffmpeg 命令行。', 'one_line_plain': 'Local video search, trimming, stitching and playback through conversation lets you edit and play videos by typing commands'}, {'id': 3448, 'key': 'mikechao-brave-search-mcp', 'kind': 'mcp', 'name': 'mikechao/brave-search-mcp', 'one_line': "📇 ☁️ - Web, image, news, video and local point-of-interest search using Brave's Search API", 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'An MCP Server implementation that integrates the Brave Search API, providing, Web Search, Local Points of Interest Search, Image Search, Video Search, News Search and LLM Context Search capabilities', 'url': 'https://github.com/mikechao/brave-search-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/mikechao/brave-search-mcp', 'downloads': None, 'stars': 130, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/mikechao.png?size=64', 'official_desc': "📇 ☁️ - Web, image, news, video and local point-of-interest search using Brave's Search API.", 'stars_own': 130, 'stars_creator': None, 'stars_own_repo': 'mikechao/brave-search-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Brave Search MCP Server\n\nAn MCP Server implementation that integrates the Brave Search API, providing, Web Search, Local Points of Interest Search, Video Search, Image Search, News Search and LLM Context Search capabilities\n\n \n \n \n\nFeatures\n\n- **Web Search**: Perform a regular search on the web\n- **Image Search**: Search the web for images.\n- **News Search**: Search the web for news\n- **Video Search**: Search the web for videos\n- **Local Points of Interest Search**: Search for local physical locations, businesses, restaurants, services, etc\n- **LLM Context Search**: Fetch and extract full web page content optimized for reading and synthesizing sources\n\nTools\n\n- **brave_web_search**\n - Execute web searches using Brave's API\n - Inputs:\n - query (string): The term to search the internet for\n - count (number, optional): The number of results to return (max 20, default 10)\n - offset (number, optional, default 0): The offset for pagination\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_image_search**\n - Get images from the web relevant to the query\n - Inputs:\n - query (string): The term to search the internet for images of\n - count (number, optional): The number of images to return (max 50, default 10)\n\n- **brave_news_search**\n - Searches the web for news\n - Inputs:\n - query (string): The term to search the internet for news articles, trending topics, or recent events\n - count (number, optional): The number of results to return (max 20, default 10)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_local_search**\n - Search for local businesses, services and points of interest\n - Falls back to brave_web_search on the initial page if no location results are found\n - Inputs:\n - query (string): Local search term\n - count (number, optional): The number of results to return (max 20, default 5)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n\n- **brave_video_search**\n - Search the web for videos\n - Inputs:\n - query: (string): The term to search for videos\n - count: (number, optional): The number of videos to return (max 20, default 10)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_llm_context_search**\n - Pre-extracted web content optimized for AI agents, LLM grounding, and RAG pipelines.\n - Uses Brave's balanced context threshold mode in compact mode and disables Brave relevance filtering in full mode.\n - Inputs:\n - query (string): The search query. Maximum 400 characters and 50 words.\n - url (string, optional): Optional URL to target. When provided, query and URL are combined for retrieval and only snippets from this exact URL are returned.\n - count (number, optional, default 8): The maximum number of search results considered. Minimum 1, maximum 50.\n - maximumNumberOfUrls (number, optional, default 8): The maximum number of URLs to include in the response. Minimum 1, maximum 50.\n - maximumNumberOfTokens (number, optional, default 2048): The approximate maximum number of tokens in the returned context. Minimum 1024, maximum 32768.\n - maximumNumberOfSnippets (number, optional, default 16): The maximum number of snippets across all URLs. Minimum 1, maximum 100.\n - maximumNumberOfTokensPerUrl (number, optional, default 512): The maximum number of tokens per URL. Minimum 512, maximum 8192.\n - maximumNumberOfSnippetsPerUrl (number, optional, default 2): The maximum number of snippets per URL. Minimum 1, maximum 100.\n - responseMode (enum, optional, default compact): compact applies Brave's balanced relevance filtering plus local snippet filtering/truncation. full disables Brave's relevance filtering and returns raw snippets without local filtering or truncation.\n - maxSnippetChars (number, optional, default 400): Maximum characters per snippet in compact mode. Minimum 80, maximum 4000.\n - maxOutputChars (number, optional, default 8000): Approximate maximum serialized response size in compact mode. Minimum 1000, maximum 100000.\n\nOpenAI Apps & MCP Apps Support\n\n \n \n \n \n \n ▶️ Click to watch the demo video \n \n\nThere is now support for OpenAI Apps and MCP Apps in this MCP Server. When UI mode is enabled for each tool there is a corresponding UI widget that let's you control what gets added to the model's context. See the directions in usage with ChatGPT section.\n\nConfiguration\n\nGetting an API Key\n\n1. Sign up for a Brave Search API account\n2. Choose a plan\n3. Generate your API key from the developer dashboard\n\nRuntime modes\n\nBy default the MCP server runs in stdio mode.\n\n \n\nTo enable Streamable HTTP mode:\n\n \n\nBy default the server listens on port 3001.\nThe URL is:\n\n \n\nThere are two configuration modes:\n\n- Env mode: BRAVE_MCP_CONFIG is unset. Feature toggles come from environment variables exactly as in previous releases.\n- File mode: BRAVE_MCP_CONFIG=/path/to/config.toml is set. The TOML file becomes the single source of truth for feature settings, and overlapping feature env vars are ignored with warnings.\n\nEnvironment-only settings\n\nThese settings are always read from the process environment, regardless of mode:\n\n- BRAVE_API_KEY (required): Brave Search API key.\n- PORT (optional): HTTP port (default: 3001).\n- HOST (optional): Interface to bind to (default: 0.0.0.0).\n- BRAVE_MCP_CONFIG (optional): Absolute or relative path to a TOML config file for feature settings.\n\nEnv mode feature settings\n\nWhen BRAVE_MCP_CONFIG is not set, these feature env vars are supported:\n\n- ALLOWED_HOSTS (HTTP mode only): Comma-separated list of allowed hostnames for Host header validation.\n - Example: ALLOWED_HOSTS=localhost,127.0.0.1,my-app.ngrok-free.app\n - Use hostnames only (no scheme/path), e.g. my-app.ngrok-free.app not https://my-app.ngrok-free.app/mcp\n- BRAVE_MCP_POLICY_FILE: JSON policy file path.\n- BRAVE_MCP_POLICY_REDACT: true to redact matched text instead of blocking it.\n- BRAVE_MCP_REQUEST_LIMIT: Positive integer request cap.\n- BRAVE_MCP_WINDOW_SECONDS: Non-negative integer rolling window size.\n- BRAVE_MCP_COOLDOWN_SECONDS: Non-negative integer cooldown after the limit is exceeded.\n- BRAVE_MCP_AUDIT_LOG: true to emit audit logs.\n- BRAVE_MCP_AUDIT_LOG_RAW: true to include raw query text in audit logs.\n- BRAVE_MCP_REQUIRE_JUSTIFICATION: true to reject tool calls without a justification string.\n\nExamples:\n\n \n\n \n\nFile mode (BRAVE_MCP_CONFIG)\n\nWhen BRAVE_MCP_CONFIG is set, the file controls feature configuration, including the HTTP host allowlist.\n\n \n\nNotes:\n\n- BRAVE_API_KEY, PORT, and HOST remain environment-only even in file mode.\n- If you set overlapping feature env vars such as BRAVE_MCP_REQUEST_LIMIT or ALLOWED_HOSTS alongside BRAVE_MCP_CONFIG, startup warns that they are being ignored.\n- Unknown TOML keys also emit warnings so typos like [guardrails] are visible before you debug runtime behavior.\n\nTo validate a config file without starting the server, use the packaged entrypoint after building t", 'readme_raw_len': 10161, 'one_line_zh': '通过 Brave 搜索 API 进行网页、图片、新闻、视频及本地兴趣点搜索', 'one_line_plain': 'Brave Search API lets users search the web, images, news, videos, and local points of interest'}, {'id': 3147, 'key': 'guimatheus92-mcp-video-analyzer', 'kind': 'mcp', 'name': 'guimatheus92/mcp-video-analyzer', 'one_line': '', 'one_line_ok': 0, 'why_bad': 'ok', 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server that turns any video — YouTube, Instagram, TikTok, Loom, X, Vimeo, direct URLs, local files — into transcripts, key frames, OCR text, and metadata for AI agents.', 'url': 'https://github.com/guimatheus92/mcp-video-analyzer', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/guimatheus92/mcp-video-analyzer', 'downloads': None, 'stars': 95, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/guimatheus92.png?size=64', 'official_desc': '[](https://glama.ai/mcp/servers/guimatheus92/mcp-video-analyzer) 📇 🏠 🍎 🪟 🐧 - Video analys', 'stars_own': 95, 'stars_creator': None, 'stars_own_repo': 'guimatheus92/mcp-video-analyzer', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'mcp-video-analyzer \n\n Turn any video — YouTube, Instagram, TikTok, Loom, X, Vimeo, direct links, local files — into transcripts, key frames, OCR text, and metadata for AI agents. \n\n \n \n \n \n \n \n\n \n \n \n\nNo existing video MCP combines **transcripts + visual frames + metadata** in one tool. This one does — across Loom, the major yt-dlp platforms (YouTube/Vimeo/TikTok/Instagram/X/Twitch/Dailymotion/Facebook), direct video URLs, and local files.\n\n> **Want a full pipeline, not just a tool?** social-knowledge-base is built on top of this server — it downloads whole Instagram creator accounts (reels, stories, highlights), transcribes them, and turns the result into a searchable, RAG-queryable knowledge base with AI-generated notes. Use this MCP when you want per-video analysis inside an agent; use social-knowledge-base when you want to archive and query an entire account.\n\nInstallation\n\nPrerequisites\n\n- **Node.js 22.12+** — required to run the server via npx\n- **yt-dlp** — **required** for YouTube/Vimeo/TikTok/Instagram/X/Twitch/Dailymotion/Facebook URLs; optional for everything else (improves Loom download quality). Install with pip install yt-dlp\n- **Chrome/Chromium** (optional) — fallback for frame extraction if yt-dlp is unavailable\n\n> Without yt-dlp or Chrome, direct URLs and local files still get frames — the bundled ffmpeg-static does the extraction, and Loom falls back to its own CDN download. Platform URLs (YouTube etc.) degrade to a clear "install yt-dlp" warning. Transcripts, metadata, and comments never require either.\n\nThere are three ways in: the **/video plugin** (Claude Code — slash command + MCP server auto-configured), a plain **MCP server** config (any MCP client), or the **portable skill + CLI** (Codex, Cursor, Copilot, and any agent with a shell — no MCP required).\n\nClaude Code — /video plugin (recommended)\n\n \n\nThis adds the /video slash command **and** auto-registers the MCP server — no claude mcp add needed:\n\n \n\nOther agents — Codex, Cursor, Copilot, Gemini CLI, …\n\n \n\nInstalls the video skill (Agent Skills format) into every agent detected on your machine. Agents without the MCP server configured fall back to the bundled CLI automatically — zero configuration.\n\nClaude Code (MCP only)\n\n \n\nThen restart Claude Code or start a new conversation.\n\nVS Code / Cursor\n\nAdd to your MCP settings file:\n\n- **VS Code**: File → Preferences → Settings → search "MCP" or edit ~/.vscode/mcp.json / %APPDATA%\\Code\\User\\mcp.json (Windows)\n- **Cursor**: Settings → MCP Servers → Add\n\n \n\nThen reload the window (Ctrl+Shift+P → "Developer: Reload Window").\n\nClaude Desktop\n\nAdd to your Claude Desktop config file:\n\n- **macOS**: ~/Library/Application Support/Claude/claude_desktop_config.json\n- **Windows**: %APPDATA%\\Claude\\claude_desktop_config.json\n\n \n\nThen restart Claude Desktop.\n\nCLI (one-shot, no MCP client)\n\nThe same engine is exposed as a one-shot command — this is what the video skill uses on agents without MCP, and it works standalone in any terminal:\n\n \n\nstdout is a single JSON document — metadata, transcript, ocrResults, timeline, warnings, frameCount, and frames as { time, filePath, mimeType } entries pointing at JPEG key frames copied to --out (default: the per-user cache dir — %LOCALAPPDATA% on Windows, ~/Library/Caches on macOS, $XDG_CACHE_HOME or ~/.cache on Linux — under mcp-video-analyzer/ /; set MCP_CACHE_DIR to an absolute path to relocate it). Unlike the temp dir this used to live in, nothing reaps that location, so frames persist until you delete them — the directories are created 0700. Progress streams on stderr, so stdout can be piped straight into a JSON parser. Partial failures land in warnings with exit code 0; only hard failures exit 1.\n\n| Flag | Description |\n|------|-------------|\n| --detail | brief (metadata + transcript, no frames), standard (default), detailed |\n| --max-frames | Max key frames, 1–60 (default adapts to duration) |\n| --max-width | Width cap for emitted frames (default 800, or MCP_FRAME_MAX_WIDTH); 0 keeps the source resolution — see Frame size |\n| --fields | Output filter — comma-separated subset: metadata,transcript,frames,comments,chapters,ocrResults,timeline,aiSummary. Filters the emitted JSON only; use --detail brief to actually skip download/frame extraction |\n| --force-refresh | Bypass the cache and re-analyze |\n| --ocr-language | Tesseract languages (default eng+por) |\n| --model / --language | Whisper overrides for the transcription fallback |\n| --out | Where frame images are copied |\n\nRun with no arguments (npx mcp-video-analyzer@latest) to start the MCP stdio server — the CLI is purely additive.\n\nVerify it works\n\nOnce installed, ask your AI assistant:\n\n \n\n(also works with an Instagram/TikTok/Loom link, a direct .mp4 URL, or a local file path). If the server is connected, it will automatically call the analyze_video tool.\n\nTools\n\nEight tools — the AI picks the cheapest one for the job and calls it automatically. Click any tool to expand its parameters and examples.\n\n| Tool | What it does |\n|------|--------------|\n| **analyze_video** | Full analysis: transcript + key frames + OCR + timeline + metadata |\n| **analyze_videos** | Batch version, one structured result per source (resumable) |\n| **get_transcript** | Transcript only (native captions or Whisper fallback) |\n| **get_metadata** | Metadata + comments + chapters, no download |\n| **get_frames** | Key frames only (scene-change or dense 1 fps) |\n| **analyze_moment** | Deep-dive on a time range (burst frames + transcript + OCR) |\n| **get_frame_at** | Single frame at a timestamp |\n| **get_frame_burst** | N frames across a narrow window (motion/animation) |\n\n \n analyze_video — full video analysis \n\n \n\nExtracts everything from a video URL in one call:\n\n \n\nReturns:\n- **Transcript** with timestamps and speakers\n- **Key frames** extracted via scene-change detection (automatically deduplicated). For static clips with no scene cuts — e.g. talking-head Reels/Stories where only an on-screen text overlay changes — it automatically falls back to uniform temporal sampling so you still get frames (and OCR) instead of an empty result.\n- **OCR text** extracted from frames (code, error messages, UI text, prices/dates/CTAs visible on screen)\n- **Annotated timeline** merging transcript + frames + OCR into a unified "what happened when" view\n- **Metadata** (title, duration, platform)\n- **Comments** from viewers\n- **Chapters** and **AI summary** (when available)\n\nThe AI will **automatically** call this tool when it sees a video URL — no need to ask.\n\nOptions:\n- detail — analysis depth: "brief" (metadata + truncated transcript, no frames), "standard" (default), "detailed" (dense sampling, more frames)\n- fields — array of specific fields to return, e.g. ["metadata", "transcript"]. Available: metadata, transcript, frames, comments, chapters, ocrResults, timeline, aiSummary\n- maxFrames (1-60) — cap on extracted frames. Default scales with video duration at standard detail (~12 for ≤30s up to 60 for >10min); fixed 60 at detailed, 0 at brief. An explicit value always wins\n- threshold (0.0-1.0, default 0.1) — scene-change sensitivity\n- forceRefresh — bypass cache and re-analyze\n- skipFrames — skip frame extraction for transcript-only analysis\n- model / language / initialPrompt — per-call Whisper overrides for the transcription fallback (override WHISPER_MODEL / WHISPER_LANGUAGE / WHISPER_PROMPT for this call only — pick a heavier model or a domain glossary for one hard clip without restarting the server)\n\n \n\n \n analyze_videos — batch analysis \n\n \n\n \n\nRuns analyze_video over a list of sources with a concurrency limit (default 2), returning one **structured result per source** — counts + warnings on success, or a per-item error on failure (one bad file never aborts the batch). Frame images are not inlined and full transcript/OCR/timeline are returned only when fields is set; otherwise you get counts. Pair with MCP_WRITE_SIDECARS=1 (below) so each video\'s result persists to disk and a re-run r', 'readme_raw_len': 29014, 'one_line_zh': '把任意视频转成文稿、关键帧、文字识别结果与元数据，供 AI Agent 使用。', 'one_line_plain': None}, {'id': 4307, 'key': 'tan-yong-sheng-ai-vision-mcp', 'kind': 'mcp', 'name': 'tan-yong-sheng/ai-vision-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Image, video and object-detection analysis with Google Gemini and Vertex AI for UI/UX evaluation,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol (MCP) server that provides vision capabilities to analyze image and video', 'url': 'https://github.com/tan-yong-sheng/ai-vision-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/tan-yong-sheng/ai-vision-mcp', 'downloads': None, 'stars': 80, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/tan-yong-sheng.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Image, video and object-detection analysis with Google Gemini and Vertex AI for UI/UX evaluation, visual regression testing and interface understanding.', 'stars_own': 80, 'stars_creator': None, 'stars_own_repo': 'tan-yong-sheng/ai-vision-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "AI Vision MCP Server\n\nA powerful Model Context Protocol (MCP) server that provides AI-powered image and video analysis using Google Gemini and Vertex AI models.\n\nFeatures\n\n- **Dual Provider Support**: Choose between Google Gemini API and Vertex AI\n- **Multimodal Analysis**: Support for both image and video content analysis\n- **Flexible File Handling**: Upload via multiple methods (URLs, local files, base64)\n- **Storage Integration**: Built-in Google Cloud Storage support\n- **Comprehensive Validation**: Zod-based data validation throughout\n- **Error Handling**: Robust error handling with retry logic and circuit breakers\n- **TypeScript**: Full TypeScript support with strict type checking\n\nQuick Start\n\nPre-requisites\n\nYou could choose either to use google provider or vertex_ai provider. For simplicity, google provider is recommended.\n\nBelow are the environment variables you need to set based on your selected provider. (Note: It’s recommended to set the timeout configuration to more than 5 minutes for your MCP client).\n\n(i) **Using Google AI Studio Provider**\n\n \n\nGet your Google AI Studio's api key here\n\n(ii) **Using Vertex AI Provider**\n\n \n\nRefer to the guideline here on how to set this up.\n\nInstallation\n\nBelow are the installation guide for this MCP on different MCP clients, such as Claude Desktop, Claude Code, Cursor, Cline, etc.\n\n \n Claude Desktop \n\nAdd to your Claude Desktop configuration:\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n\n \n\n \n Claude Code \n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n\nNote: Increase the MCP startup timeout to 1 minutes and MCP tool execution timeout to about 5 minutes by updating ~\\.claude\\settings.json as follows:\n\n \n\n \n\n \n Cursor \n\nGo to: Settings -> Cursor Settings -> MCP -> Add new global MCP server\n\nPasting the following configuration into your Cursor ~/.cursor/mcp.json file is the recommended approach. You may also install in a specific project by creating .cursor/mcp.json in your project folder. See Cursor MCP docs for more info.\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n \n\n \n Cline \n\nCline uses a JSON configuration file to manage MCP servers. To integrate the provided MCP server configuration:\n\n1. Open Cline and click on the MCP Servers icon in the top navigation bar.\n2. Select the Installed tab, then click Advanced MCP Settings.\n3. In the cline_mcp_settings.json file, add the following configuration:\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n \n\n \n\n Other MCP clients \n\nThe server uses stdio transport and follows the standard MCP protocol. It can be integrated with any MCP-compatible client by running:\n\n \n \n\nMCP Tools\n\nThe server provides four main MCP tools:\n\n1) analyze_image\n\nAnalyzes an image using AI and returns a detailed description.\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the image\n- prompt (string): Question or instruction for the AI\n- mode (string, optional): Analysis mode - one of:\n - general (default) - General image analysis\n - palette - Extract design tokens (colors, spacing, typography)\n - hierarchy - Analyze visual hierarchy and eye flow\n - components - Catalog UI components and design system maturity\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Examples:**\n\n1. **General image analysis:**\n \n\n2. **Extract design tokens:**\n \n\n3. **Analyze visual hierarchy:**\n \n\n4. **Component inventory:**\n \n\n2) compare_images\n\nCompares multiple images using AI and returns a detailed comparison analysis.\n\n**Parameters:**\n- imageSources (array): Array of image sources (URLs, base64 data, or file paths) - minimum 2, maximum 4 images\n- prompt (string): Question or instruction for comparing the images\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Examples:**\n\n1. **Compare images from URLs:**\n \n\n2. **Compare mixed sources:**\n \n\n3) detect_objects_in_image\n\nDetects objects in an image using AI vision models and generates annotated images with bounding boxes. Returns detected objects with coordinates and either saves the annotated image to a file or temporary directory.\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the image\n- prompt (string): Custom detection prompt describing what to detect or recognize in the image\n- outputFilePath (string, optional): Explicit output path for the annotated image\n\n**Configuration:**\nThis function uses optimized default parameters for object detection and does not accept runtime options parameter. To customize the AI parameters (temperature, topP, topK, maxTokens), use environment variables:\n\n \n\n**File Handling Logic:**\n1. **Explicit outputFilePath provided** → Saves to the exact path specified\n2. **If not explicit outputFilePath** → Automatically saves to temporary directory\n\n**Response Types:**\n- Returns file object when explicit outputFilePath is provided\n- Returns tempFile object when explicit outputFilePath is not provided so the image file output is auto-saved to temporary folder\n- Always includes detections array with detected objects and coordinates\n- Includes summary with percentage-based coordinates for browser automation\n\n**Examples:**\n\n1. **Basic object detection:**\n \n\n2. **Save annotated image to specific path:**\n \n\n3. **Custom detection prompt:**\n \n\n4) audit_design\n\nAudits UI/UX design compliance with pixel-level analysis and AI critique.\n\nThis tool provides automated design compliance auditing using pure TypeScript/JavaScript pixel analysis combined with Gemini Vision API critique. It extracts dominant colors, detects visual complexity, validates WCAG contrast ratios, and generates actionable design recommendations.\n\n**Inspired by:** Automating UX/UI Design Analysis with Python, Machine Learning, and LLMs by Jade Graham\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the design image\n- prompt (string, optional): Custom audit context or focus areas\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Features:**\n- **Dominant Colors**: K-means clustering to extract 5 primary colors\n- **Edge Complexity**: Sobel operator for visual structure analysis\n- **WCAG Contrast**: W3C relative luminance formula validation (AA/AAA)\n- **Luminance Stats**: Mean brightness and standard deviation calculations\n- **Design Issues**: Automated detection of contrast, complexity, and brightness problems\n- **AI Critique**: Gemini-powered recommendations for design improvements\n\n**Examples:**\n\n1. **Basic design audit:**\n \n\n2. **Audit local design file:**\n \n\n5) analyze_video\n\nAnalyzes a video using AI and returns a detailed description.\n\n**Parameters:**\n- videoSource (string): YouTube URL, GCS URI, or local file path to the video\n- prompt (string): Question or instruction for the AI\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Supported video sources:**\n- YouTube URLs (e.g., https://www.youtube.com/watch?v=...)\n- Local file paths (e.g., C:\\Users\\username\\Downloads\\video.mp4)\n\n**Examples:**\n\n1. **Analyze video from YouTube URL:**\n \n\n2. **Analyze local video file:**\n \n\n**Note:** Only YouTube URLs are supported for public video URLs. Other public video URLs are not currently supported.\n\nEnvironment Configuration\n\nFor basic setup, you only need to configure the provider selection and required credentials:\n\nGoogle AI Studio Provider (Recommended)\n \n\nVertex AI Provider (Production)\n \n\n📖 **Detailed Configuration Guide**\n\nFor comprehensive environment variable documentation, including:\n- Complete configuration reference (60+ environment variables)\n- Function-specific optimization examples\n- Advanced configuration patterns\n- Troubleshooting guidance\n\n👉 **See Environment Variable Guide**\n\nConfiguration Priority Overview\n\nThe server uses a hierarchical configuration system where more specific settings override general ones:\n\n1. **LLM-assigned values** (ru", 'readme_raw_len': 10342, 'one_line_zh': '使用 Google Gemini 和 Vertex AI 进行图像、视频和物体检测分析，用于 UI/UX 评估', 'one_line_plain': 'Analyse images, video and objects with Google Gemini and Vertex AI for UI/UX evaluation'}, {'id': 371, 'key': 'raveenb-fal-mcp-server', 'kind': 'mcp', 'name': 'raveenb/fal-mcp-server', 'one_line': '🐍 ☁️ - Generate images, videos and music with Fal.ai models such as FLUX, Stable Diffusion and MusicGen', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/luminarylane/fal-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/raveenb/fal-mcp-server', 'downloads': None, 'stars': 61, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/raveenb.png?size=64', 'official_desc': 'Fal.ai MCP Server A Model Context Protocol (MCP) server that enables Claude Desktop (and other MCP clients) to generate images, videos, music, and audio using Fal.ai models.', 'stars_own': 61, 'stars_creator': None, 'stars_own_repo': 'luminarylane/fal-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': '🎨 Fal.ai MCP Server\n\n \n \n \n \n \n \n \n \n\nA Model Context Protocol (MCP) server that enables Claude Desktop (and other MCP clients) to generate images, videos, music, and audio using Fal.ai models.\n\n \n \n \n\n✨ Features\n\n🚀 Performance\n- **Native Async API** - Uses fal_client.run_async() for optimal performance\n- **Queue Support** - Long-running tasks (video/music) use queue API with progress updates\n- **Non-blocking** - All operations are truly asynchronous\n\n🌐 Transport Modes (New!)\n- **STDIO** - Traditional Model Context Protocol communication\n- **HTTP/SSE** - Web-based access via Server-Sent Events\n- **Dual Mode** - Run both transports simultaneously\n\n🎨 Media Generation (18 Tools)\n\n**Image Generation:**\n- 🖼️ **generate_image** - Create images from text prompts (Flux, SDXL, etc.)\n- 🎯 **generate_image_structured** - Fine-grained control over composition, lighting, subjects\n- 🔄 **generate_image_from_image** - Transform existing images with style transfer\n\n**Image Editing:**\n- ✂️ **remove_background** - Remove backgrounds from images (transparent PNG)\n- 🔍 **upscale_image** - Upscale images 2x or 4x while preserving quality\n- ✏️ **edit_image** - Edit images using natural language instructions\n- 🎭 **inpaint_image** - Edit specific regions using masks\n- 📐 **resize_image** - Smart resize for social media (Instagram, YouTube, TikTok, etc.)\n- 🏷️ **compose_images** - Overlay images (watermarks, logos) with precise positioning\n\n**Video Tools:**\n- 🎬 **generate_video** - Text-to-video and image-to-video generation\n- 📹 **generate_video_from_image** - Animate images into videos\n- 🔀 **generate_video_from_video** - Video restyling and motion transfer\n\n**Audio Tools:**\n- 🎵 **generate_music** - Create instrumental music or songs with vocals\n\n**Utility Tools:**\n- 🔍 **list_models** - Discover 600+ available models with smart filtering\n- 💡 **recommend_model** - AI-powered model recommendations for your task\n- 💰 **get_pricing** - Check costs before generating content\n- 📊 **get_usage** - View spending history and usage stats\n- ⬆️ **upload_file** - Upload local files for use with generation tools\n\n🔍 Dynamic Model Discovery (New!)\n- **600+ Models** - Access all models available on Fal.ai platform\n- **Auto-Discovery** - Models are fetched dynamically from the Fal.ai API\n- **Smart Caching** - TTL-based cache for optimal performance\n- **Flexible Input** - Use full model IDs or friendly aliases\n\n🚀 Quick Start\n\nPrerequisites\n\n- Python 3.10 or higher\n- Fal.ai API key (free tier available)\n- Claude Desktop (or any MCP-compatible client)\n\nInstallation\n\nOption 0: Claude Code Plugin (Simplest for Claude Code Users) 🔌\n\nIf you\'re using Claude Code, install directly via the plugin system:\n\n \n\nOr install directly without adding the marketplace:\n \n\n> **Note:** You\'ll need to set FAL_KEY in your environment before using the plugin.\n\nOption 1: uvx (Recommended - Zero Install) ⚡\n\nRun directly without installation using uv:\n\n \n\n**Claude Desktop Configuration for uvx:**\n \n\n> **Note:** Install uv first: curl -LsSf https://astral.sh/uv/install.sh | sh\n\nOption 2: Docker (Recommended for Production) 🐳\n\nOfficial Docker image available on GitHub Container Registry.\n\n**Step 1: Start the Docker container**\n\n \n\n**Step 2: Configure Claude Desktop to connect**\n\nAdd to your Claude Desktop config file:\n- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n- Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n\n \n\n> **Note:** This uses mcp-remote to connect to the HTTP/SSE endpoint. Alternatively, if you have curl available: "command": "curl", "args": ["-N", "http://localhost:8080/sse"]\n\n**Step 3: Restart Claude Desktop**\n\nThe fal-ai tools should now be available.\n\n**Docker Environment Variables:**\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| FAL_KEY | (required) | Your Fal.ai API key |\n| FAL_MCP_TRANSPORT | http | Transport mode: http, stdio, or dual |\n| FAL_MCP_HOST | 0.0.0.0 | Host to bind the server to |\n| FAL_MCP_PORT | 8080 | Port for the HTTP server |\n\n**Using Docker Compose:**\n\n \n\n**⚠️ File Upload with Docker:**\n\nThe upload_file tool requires volume mounts to access host files:\n\n \n\nThen use container paths like /downloads/image.png instead of host paths.\n\n| Feature | stdio (uvx) | Docker (HTTP/SSE) |\n|---------|:-----------:|:-----------------:|\n| upload_file | ✅ Full filesystem | ⚠️ Needs volume mounts |\n| Security | Runs as user | Sandboxed container |\n\nOption 3: Install from PyPI\n\n \n\nOr with uv:\n \n\nOption 4: Install from source\n\n \n\nConfiguration\n\n1. Get your Fal.ai API key from fal.ai\n\n2. Configure Claude Desktop by adding to:\n - macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n - Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n\nFor PyPI/pip Installation:\n \n\n> **Note:** For Docker configuration, see Option 2: Docker above.\n\nFor Source Installation:\n \n\n3. Restart Claude Desktop\n\n💬 Usage\n\nWith Claude Desktop\n\nOnce configured, ask Claude to:\n\n- "Generate an image of a sunset"\n- "Create a video from this image"\n- "Generate 30 seconds of ambient music"\n- "Convert this text to speech"\n- "Transcribe this audio file"\n\nDiscovering Available Models\n\nUse the list_models tool to discover available models:\n\n- "What image models are available?"\n- "List video generation models"\n- "Search for flux models"\n\nUsing Any Fal.ai Model\n\nYou can use any model from the Fal.ai platform:\n\n \n\nHTTP/SSE Transport (New!)\n\nRun the server with HTTP transport for web-based access:\n\n \n\nConnect from web clients via Server-Sent Events:\n- SSE endpoint: http://localhost:8080/sse (Docker) or http://localhost:8000/sse (pip)\n- Message endpoint: POST http://localhost:8080/messages/\n\nSee Docker Documentation and HTTP Transport Documentation for details.\n\n📦 Supported Models\n\nThis server supports **600+ models** from the Fal.ai platform through dynamic discovery. Use the list_models tool to explore available models, or use any model ID directly.\n\nPopular Aliases (Quick Reference)\n\nThese friendly aliases are always available for commonly used models:\n\n| Alias | Model ID | Type |\n|-------|----------|------|\n| flux_schnell | fal-ai/flux/schnell | Image |\n| flux_dev | fal-ai/flux/dev | Image |\n| flux_pro | fal-ai/flux-pro | Image |\n| sdxl | fal-ai/fast-sdxl | Image |\n| stable_diffusion | fal-ai/stable-diffusion-v3-medium | Image |\n| svd | fal-ai/stable-video-diffusion | Video |\n| animatediff | fal-ai/fast-animatediff | Video |\n| kling | fal-ai/kling-video | Video |\n| musicgen | fal-ai/musicgen-medium | Audio |\n| musicgen_large | fal-ai/musicgen-large | Audio |\n| bark | fal-ai/bark | Audio |\n| whisper | fal-ai/whisper | Audio |\n\nUsing Full Model IDs\n\nYou can also use any model directly by its full ID:\n\n \n\nUse list_models with category filters to discover more:\n- list_models(category="image") - All image generation models\n- list_models(category="video") - All video generation models\n- list_models(category="audio") - All audio models\n- list_models(search="flux") - Search for specific models\n\n📚 Documentation\n\n| Guide | Description |\n|-------|-------------|\n| Installation Guide | Detailed setup instructions for all platforms |\n| API Reference | Complete tool documentation with parameters |\n| Examples | Usage examples for image, video, and audio generation |\n| Docker Guide | Container deployment and configuration |\n| HTTP Transport | Web-based SSE transport setup |\n| Local Testing | Running CI locally with act |\n\n📖 **Full documentation site**: raveenb.github.io/fal-mcp-server\n\n🔌 Claude Code Plugin Marketplace\n\nThis project is part of the **Luminary Lane Tools** marketplace for Claude Code plugins.\n\n**Add the marketplace:**\n \n\n**Available plugins:**\n| Plugin | Description |\n|--------|-------------|\n| fal-ai | Generate images, videos, and music using 600+ Fal.ai models |\n\n*More plugins coming soon!*\n\n🔧 Troubleshooting\n\nCommon Errors\n\nFAL_KEY not set\n \n**Solution:** Set your Fal.ai API key:\n \n\nModel not found\n \n**Solution:** Use list_models to discover available models, or check the ', 'readme_raw_len': 9308, 'one_line_zh': '使用 FLUX、Stable Diffusion、MusicGen 等 Fal.ai 模型生成图像、视频和音乐', 'one_line_plain': 'Fal.ai models such as FLUX, Stable Diffusion and MusicGen let users generate images, videos and music'}, {'id': 3795, 'key': 'anwerj-youtube-uploader-mcp', 'kind': 'mcp', 'name': 'anwerj/youtube-uploader-mcp', 'one_line': '🏎️ ☁️ - Upload videos to YouTube directly from an AI assistant', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Upload, Update or Schedule videos on Youtube with the help of AI', 'url': 'https://github.com/anwerj/youtube-uploader-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/anwerj/youtube-uploader-mcp', 'downloads': None, 'stars': 59, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/anwerj.png?size=64', 'official_desc': '🏎️ ☁️ - Upload videos to YouTube directly from an AI assistant.', 'stars_own': 59, 'stars_creator': None, 'stars_own_repo': 'anwerj/youtube-uploader-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'YouTube Uploader MCP\n \n \n\nAI‑powered YouTube uploader—no CLI, no YouTube Studio, and no secrets ever shared with LLMs or third‑party apps and all free of cost! It includes OAuth2 authentication, token management, and video upload functionality.\n\nFeatures\n\n* **Direct Uploads**: Upload videos to YouTube from Claude, Cursor, VS Code, or any other MCP client.\n* **AI-Assisted Metadata**: Automatically generate titles, descriptions, and tags via your MCP client.\n* **OAuth2 Authentication**: Secure local login, multi-channel support, and auto-refreshing.\n* **Metadata & Settings**: Category tags, optional language settings, and explicit metadata control.\n* **Privacy & Scheduling**: Support for public, private, or unlisted statuses, and scheduled publish times.\n* **Post-Upload Configs (update_video)**: Add videos to playlists, upload custom thumbnails ( \n\n \n\nManual Installation\nPlease check Single Command Installation, proceed if you prefer manual installation.\n\nVisit the Releases page and download the appropriate binary for your operating system:\n\n- youtube-uploader-mcp-linux-amd64\n- youtube-uploader-mcp-darwin-arm64\n- youtube-uploader-mcp-windows-amd64.exe\n- etc.\n\n> You can use the latest versioned tag, e.g., v1.0.0.\n\n---\n\n2. Make it Executable (Linux/macOS)\n\n \n\n3. Configure MCP (e.g., in Claude Desktop or Cursor)\n \n4. Set Up Google OAuth 2.0\nTo upload to YouTube, you must configure OAuth and get a client_secret.json file from the Google Developer Console.\n\n➡️ Follow the guide in youtube_oauth2_setup.md for a step-by-step walkthrough.\n\n> [!WARNING]\n> **Important: Sensitive OAuth Scope & Re-Authentication**:\n> Subtitle/caption upload relies on the https://www.googleapis.com/auth/youtube.force-ssl scope, which is required by the YouTube Data API to perform caption write operations. \n> * **Existing Users**: If you authenticated with an older version of this MCP, your saved token will lack this scope. Any calls attempting to upload subtitles will fail with a 403 Forbidden API error. \n> * **How to Update**: You must delete the channel cache file (usually .youtube_uploader_channels_cache in your working directory) and rerun the authenticate tool to grant the new permissions.\n> * **Google Console Warning**: Adding this sensitive scope will trigger a warning screen in Google Cloud Console. Since this is for your personal use, you can safely proceed past the "unverified app" warnings.\n\nUsage & Tool Orchestration\n\nThe MCP server registers the following tools:\n1. authenticate: Generates the OAuth2 URL for authentication.\n2. accesstoken: Exchanges the code for user credentials and channel info.\n3. channels: Retrieves authenticated channels.\n4. refreshtoken: Force-refreshes tokens.\n5. upload_video: Uploads the video file and configures main details (title, description, tags, category, optional language, status, kids\' flags, scheduled publish).\n6. update_video: Decoupled tool that manages post-upload configurations: adds the video to a playlist, uploads a custom thumbnail (must be <2MB), attaches subtitle/caption tracks, schedules an existing private video to publish at an RFC3339 time, and updates the made-for-kids audience setting.\n7. list_videos: Lists videos uploaded to a channel (public, unlisted, and private), with optional title/description search, privacy filter, and date ordering.\n\nContributing\n\nSee CONTRIBUTING.md to contribute.', 'readme_raw_len': 3379, 'one_line_zh': '🏎️ ☁️ - 通过 AI 助手直接上传视频到 YouTube', 'one_line_plain': 'YouTube lets you upload videos directly from an AI assistant'}, {'id': 5248, 'key': 'utensils-mold', 'kind': 'repo', 'name': 'utensils/mold', 'one_line': 'CLI-native local AI image and video generation for people, scripts, and agents — CUDA on Linux, Metal on macOS, plus desktop, web, iPhone, REST/SSE, and MCP.', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'CLI-native local AI image and video generation for people, scripts, and agents — CUDA on Linux, Metal on macOS, plus desktop, web, iPhone, REST/SSE, and MCP.', 'url': 'https://github.com/utensils/mold', 'source': 'github', 'source_url': 'https://github.com/utensils/mold', 'downloads': None, 'stars': 54, 'installs': None, 'mentions': 1, 'score': 103.1, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-10', 'times_seen': 2, 'icon': 'https://github.com/utensils.png?size=64', 'official_desc': 'CLI-native local AI image and video generation for people, scripts, and agents — CUDA on Linux, Metal on macOS, plus desktop, web, iPhone, REST/SSE, and MCP.', 'stars_own': 54, 'stars_creator': None, 'stars_own_repo': 'utensils/mold', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "mold\n\n \n \n \n \n \n \n \n\nLocal AI image and video generation on your own GPU. Mold supports NVIDIA CUDA\nand Apple Silicon Metal, with a CLI, native desktop app, web studio, mobile\ncompanions, Discord bot, and REST/SSE API built on the same engine.\n\n**Documentation** ·\n**Models** ·\n**Desktop guide** ·\n**API**\n\n \n\nThe generation composer offers **Edit prompt** for spacious long-text editing,\nrecent-prompt search and Clear with Undo. Native iOS and macOS Library Settings\nalso let you hide date separators for a continuous grid.\n\nInstall\n\nStable release:\n\n \n\nNightly CLI from the latest published main build:\n\n \n\nAutomatic cloud provisioning uses a verified container digest for the client’s exact\nsource revision. Nightly/source builds wait for their matching container readiness\nmanifest; an unpublished image fails before creating a paid host.\n\nThe installer selects a compatible build and verifies its checksum. Linux x86_64\nclients without a visible NVIDIA GPU receive a GPU-free CLI. To use only remote\nGPU hosts, including from a machine with NVIDIA hardware:\n\n \n\nThe CPU archive needs no NVIDIA driver or CUDA libraries. Local GPU generation\nrequires a CUDA build (or Metal on macOS). See the\ninstallation guide for Nix,\nArch, Windows, Android, and source builds.\nGH200, GB200, and GB300 require future linux/arm64 artifacts and are unsupported.\n\nMold no longer publishes new versions to crates.io. Existing registry versions\nare historical; use GitHub releases, Nix, Docker, AUR, or a source build\nfor current versions.\n\nQuick start\n\n \n\nFor API-only servers, set web_ui_enabled = false in config.toml (or\nMOLD_WEB_UI_ENABLED=false) and restart. The interface defaults on; disabling\nit leaves every API route available. NixOS: services.mold.webUi.enable = false;.\n\nModels download automatically on first use. Generated media is saved locally\nwith prompt, model, seed, and generation metadata.\nLoRA reuse tracks adapter file revisions as well as strengths; replacing an\nadapter invalidates cached weights. FLUX and SD3 retain at most 256 MiB of\nderived LoRA deltas per engine and release that cache when unloaded.\nWeb, desktop and mobile reject empty video/audio attachments and files above\n64 MiB before encoding, with inline errors that preserve the prior attachment.\nMobile also enforces a 45 MiB combined media budget.\nFramewise video upscale also needs the host codec bridge: Nix packages and\nCUDA containers include it, while raw binary installs must provide ffmpeg\nand ffprobe on PATH before the server advertises that feature.\n\nWhat it supports\n\n- **Models:** FLUX.1, Flux.2, Stable Diffusion, Z-Image, Qwen-Image,\n Qwen Image 2.1, Wuerstchen, LTX Video, Wan, MiniMax H3, and Hunyuan3D. See the\n model catalog for variants and hardware\n requirements.\n Wan's 1.3B BF16 and 5B Q8/FP16 paths are performance-qualified on Apple\n Metal as well as CUDA; fp8-scaled Wan checkpoints remain CUDA-only.\n MiniMax H3's compact runtime is supported on SM89 CUDA and Apple Metal;\n Metal admission checks each request against live unified-memory headroom.\n FL2VA accepts text-only (no frame), first, last, or both boundary frames,\n with roughly 1,000 prompt tokens with paired frames and charging their\n additional memory. Distilled FL2VA tiers include Turbo 4/8-step, DMAD\n 4-step, PDMD 2/4-step, and FastVideo's FastH3 V2 8-step (CUDA-qualified).\n Queued FL2VA jobs preserve first/last endpoint roles through durable storage\n and replay, including all supported Turbo tiers.\n Forced-local H3 execution accepts one FL2VA request; batches, sequences, and\n Ref2VA reference uploads require the server route.\n Remote Ref2VA automatically selects authenticated uploads or bounded inline\n references on hosts with authentication disabled; no client\n API key is needed for the latter. Server license acceptance remains server-owned.\n Qwen Image 2.1 (non-commercial Qwen Research License) edits from up to ten\n ordered references, renders transparent PNG/WebP cut-outs, runs native 2K\n presets, and ships BF16, INT8, FP8, GGUF and 6-step turbo tiers; its CUDA\n fast path is 2.5x faster at 1024² and 6x at 2K than v0.32.\n- **Images:** text-to-image, image editing, inpainting, ControlNet, LoRA,\n identity photos, reference-image prompting, transparent backgrounds, prompt\n expansion, and upscaling, as PNG, JPEG or still WebP.\n- **Video and audio:** text/image-to-video, sequences, clip continuation,\n lip dub, text-to-audio, and MP4 output with generated audio.\n- **3D:** single-image and named multiview-to-mesh with Hunyuan3D 2.0 and 2.1,\n automatic background removal, optional highlight and lighting removal, and\n Hunyuan3D Paint PBR materials in CUDA builds. Shape transformers can be\n locally derived as qualified FP8/GGUF tiers with mold quantize, which\n registers the derived tier on the host that made it. Results are published to the Library as\n binary glTF with a rendered poster tile, exportable as OBJ, an OBJ+PBR ZIP, STL, or PLY, or\n shared as a turntable GIF, APNG, or WebP. Durable text-to-3D, supplied-mesh texturing, and mesh-rebuild workflows remain\n available through the API/CLI. Apps generate 3-D objects directly from supplied\n images in the main generation screen.\n Painted prints expose their base-color, metallic-roughness, and normal maps as\n independent, digest-checked Library downloads on web, desktop, and mobile.\n- **Multiple machines:** connect local, LAN, Tailscale, and RunPod hosts, then\n route work and browse one combined Library.\n- **Organization:** title, favorite, tag, collect, restore, and manage prints\n across the desktop and web apps.\n\nCurated models use the same short manifest title throughout the apps and\nmold list, such as **FLUX.1 Dev Q4**. The runnable ID (flux-dev:q4)\nremains available for commands, saved settings, and API requests. Model files\nkeep their original names; third-party models keep their provider titles.\n\nModel weights keep their own licenses. See each model page for terms and\ncurrent platform support.\n\nMold Studio\n\nUse **View** (native apps) or **View original** (web, desktop and mobile) beside an attached input to inspect it without replacing or removing it. References, source/target pictures, boundary frames, masks, control and identity pictures, and attached video/audio have separate viewing actions. Image previews fit the whole picture. Use **Actual size** on desktop and web, or pinch to zoom in the native iOS app, for close inspection. **Done** or Escape returns to the attachment or picker. Picker previews do not select or attach the picture. Queue viewing actions show the full submitted media, which may already have been fitted to the generation canvas; older machines without the original-input endpoint report it unavailable.\n\nEvery download, with requirements, is on the\nDownload page.\n\n| Platform | Download |\n| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| macOS | Mold Studio for Mac — built natively, Apple Silicon, macOS 26+ |\n| macOS | Mold Desktop for Mac — same app as Windows and Linux, macOS 12+ |\n| Windows | Mold Desktop for Windows — self-signed, see below |\n| Linux | Mold Desktop for Linux — GPU-free, for remote GPU machines |\n| iPhone and iPad | Mold Studio Companion (iOS 26) and Mold for iPhone (iOS 17) — TestFlight |\n| Android | Mold for Android — signed APK, Android 7.0+ |\n\n**Two desktop apps on the Mac.** Mold Studio is built natively for macOS: signed,\nnotarized, self-updating through Sparkle, with mold's own Metal engine built\nin, and it talks to any mold serve on your network too. Generate, a merged\nmulti-machine Library, Queue, Models and Machines, all in one native window.\nLibrary’s native machine and media filter menus keep their current selections visible.\nBoth native apps keep neutral queue placeholders for jobs without source images;\nMac restores fully local source attachments without stale archive warnings.\nQueue jobs open ", 'readme_raw_len': 32967, 'one_line_zh': '面向人、脚本与 Agent 的 CLI 原生本地 AI 图像与视频生成，Linux 用 CUDA、macOS 用 Metal，并提供桌面与 MCP', 'one_line_plain': 'Generate images and videos locally using CUDA on Linux, Metal on macOS, and MCP for desktop, web, and iPhone'}, {'id': 1590, 'key': 'pzalutski-pixel-javalens-mcp', 'kind': 'mcp', 'name': 'pzalutski-pixel/javalens-mcp', 'one_line': '☕ 🏠 - Semantic Java analysis via Eclipse JDT: compiler-accurate navigation, refactoring, find references,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server providing semantic Java code analysis for AI agents. Built on Eclipse JDT with tools for navigation, refactoring, search, and metrics.', 'url': 'https://github.com/pzalutski-pixel/javalens-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/pzalutski-pixel/javalens-mcp', 'downloads': None, 'stars': 44, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/pzalutski-pixel.png?size=64', 'official_desc': '☕ 🏠 - Semantic Java analysis via Eclipse JDT: compiler-accurate navigation, refactoring, find references, call hierarchy and code metrics.', 'stars_own': 44, 'stars_creator': None, 'stars_own_repo': 'pzalutski-pixel/javalens-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'JavaLens: AI-First Code Analysis for Java\n\n \n \n \n\nAn MCP server providing 75 semantic analysis tools for Java, built directly on Eclipse JDT for compiler-accurate code understanding.\n\nBuilt for AI Agents\n\nJavaLens exists because **AI systems need compiler-accurate insights that reading source files cannot provide**. When an AI uses grep or Read to find usages of a method, it cannot distinguish:\n\n- A method call from a method with the same name in an unrelated class\n- A field read from a field write\n- An interface implementation from an unrelated class\n- A cast to a type from other references to that type\n\nThis leads to incorrect refactorings, missed usages, and incomplete understanding of code behavior.\n\nCompiler-Accurate Analysis\n\nJavaLens provides **compiler-accurate code analysis** through Eclipse JDT—the same engine that powers Eclipse IDE. Unlike text search, JDT understands:\n\n- Type resolution across inheritance hierarchies\n- Method overloading and overriding\n- Generic type arguments\n- Import resolution and classpath dependencies\n- Java source from version 1.1 through **Java 25** (markdown Javadoc, module imports, compact source files, flexible constructor bodies)\n- **Lombok**-generated members — a bundled agent makes @Data accessors and the like resolve, so code using them is not flagged as undefined\n\n**Example:** Finding all places where UserService.save() is called:\n\n| Approach | Result |\n|----------|--------|\n| grep "save(" | Returns 47 matches including orderService.save(), saveButton, comments |\n| find_references | Returns exactly 12 calls to UserService.save() |\n\nAI Training Bias Warning\n\n> ⚠️ **Important for AI developers and users**\n\nAI models may exhibit **trained bias toward native tools** (Grep, Read, LSP) over MCP server tools, even when semantic analysis provides better results. This happens because:\n\n1. Training data contains extensive grep/text-search patterns\n2. Native tools are "always available" in the model\'s experience\n3. The model may not recognize when semantic analysis is superior\n\n**To get the best results:**\n\nAdd guidance to your project instructions or system prompt (e.g., CLAUDE.md for Claude Code):\n\n \n\nWhat is JavaLens?\n\nJavaLens is an MCP server that gives AI assistants deep understanding of Java codebases. It provides semantic analysis, navigation, refactoring, and code intelligence tools that go beyond simple text search.\n\nWhy Not LSP?\n\nLanguage Server Protocol was designed for IDE autocomplete and basic navigation—not for AI agent workflows that require deep semantic analysis.\n\n| Capability | Native LSP | JavaLens |\n|------------|------------|----------|\n| Find all @Annotation usages | ❌ | ✅ |\n| Find all new Type() instantiations | ❌ | ✅ |\n| Find all casts to a type | ❌ | ✅ |\n| Distinguish field reads from writes | ❌ | ✅ |\n| Detect circular package dependencies | ❌ | ✅ |\n| Calculate cyclomatic complexity | ❌ | ✅ |\n| Find unused private methods | ❌ | ✅ |\n| Detect possible null pointer bugs | ❌ | ✅ |\n| Project-wide dead-code reachability from entry points | ❌ | ✅ |\n| Find the tests that exercise a symbol, transitively | ❌ | ✅ |\n\nJavaLens wraps **Eclipse JDT Core** directly via OSGi, providing:\n\n- **Fine-grained reference types**: Find specifically casts, annotations, throws clauses, catch blocks, instanceof checks, method references, type arguments\n- **Read vs write access distinction**: Track where fields are mutated vs just read\n- **Indexed search**: JDT pre-builds an index at load time, so symbol/reference queries do not walk source files\n- **Full AST access**: Direct manipulation for complex refactorings\n\nInstallation\n\nPrerequisites\n\n- **Java 21** or later (must be on PATH or set JAVA_HOME) — required for both install paths.\n- **Node.js 18+** — required *only* if you use the npm/npx install path below. Skip if you use the direct-download path.\n\nJavaLens is an analytical server, not a compiler. It uses Eclipse JDT 2025-12 to parse and understand Java source code from **version 1.1 through 25**. Java 21 is required only as the server runtime.\n\nInstall from GitHub Releases (recommended — Java only)\n\nThis is the simplest path if you already have Java 21 and don\'t have Node.js. Download from Releases:\n\n| Platform | File |\n|----------|------|\n| All platforms | javalens.zip or javalens.tar.gz |\n\nExtract to a location of your choice (e.g., /opt/javalens or C:\\javalens). Then point your MCP client at the bundled jar — see Configure MCP Client below.\n\nInstall via npm (requires Node.js 18+)\n\nIf you already have Node.js, npx will download and cache the JavaLens distribution (~23 MB) on first run:\n\n \n\nConfigure MCP Client\n\nAdd to your MCP configuration (e.g., .mcp.json for Claude Code):\n\n \n\nThe -data argument specifies where JavaLens stores its workspace metadata. See How Workspaces Work below.\n\nAuto-Load a Project\n\nSet JAVA_PROJECT_PATH to auto-load a project when the server starts:\n\n \n\n> **Note:** Project loading happens asynchronously in the background. The MCP server responds immediately while the project loads. Use health_check to monitor loading status—it will show "project.status": "loading" until complete, then "loaded" when ready.\n\nHow Workspaces Work\n\nUnlike in-memory code models, Eclipse JDT requires a **workspace directory** to store:\n\n- Search indexes for fast symbol lookup\n- Compilation state and caches\n- Project metadata\n\nWorkspaces Are Outside Your Source\n\nJavaLens creates its workspace **outside your source project** to keep your codebase clean:\n\n \n1. load_project(projectPath="/path/to/java/project")\n2. search_symbols(query="*Service", kind="Class")\n3. find_references(filePath="...", line=10, column=15)\n4. analyze_type(typeName="com.example.UserService")\n \n1. Use JavaLens tools to analyze\n2. Write changes to files\n3. Use JavaLens tools to verify — answers already reflect the changes\n bash\ngit clone https://github.com/pzalutski-pixel/javalens-mcp.git\ncd javalens-mcp\n./mvnw clean verify\n bash\nFull suite, gentle (missing tools skip)\n./mvnw verify\n\nFull suite, strict (missing tools fail; what CI does)\nJAVALENS_TESTS_REQUIRE_TOOLS=true ./mvnw verify\n mermaid\nflowchart TD\n Client[" MCP Client "]\n MCP[" org.javalens.mcp McpProtocolHandler → ToolRegistry → 75 Tools"]\n Core[" org.javalens.core JdtServiceImpl → WorkspaceManager, SearchService"]\n JDT[" Eclipse JDT Core (via OSGi / Equinox) IWorkspace, IJavaProject, SearchEngine, ASTParser"]\n\n Client -->|"JSON-RPC over stdio"| MCP\n MCP --> Core\n Core --> JDT\n`\n\nLicense\n\nMIT License - see LICENSE for details.', 'readme_raw_len': 6513, 'one_line_zh': '通过 Eclipse JDT 进行 Java 语义分析：编译器级精确导航、重构、查找引用等', 'one_line_plain': 'Navigate and refactor Java code with compiler-accurate accuracy via Eclipse JDT'}, {'id': 7542, 'key': 'houtini-ai-gemini-mcp', 'kind': 'repo', 'name': 'houtini-ai/gemini-mcp', 'one_line': "Google Gemini MCP - Use Google's visual, video and chat capabilities in your AI Assistant", 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '["开源项目"]', 'desc_raw': "Google Gemini MCP - Use Google's visual, video and chat capabilities in your AI Assistant. Uses MCP Apps to preview created images, landing pages, SVGs and video", 'url': 'https://github.com/houtini-ai/gemini-mcp', 'source': 'github', 'source_url': 'https://github.com/houtini-ai/gemini-mcp', 'downloads': None, 'stars': 34, 'installs': None, 'mentions': 1, 'score': 103.07, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-14', 'last_seen': '2026-10-19', 'times_seen': 6, 'icon': 'https://github.com/houtini-ai.png?size=64', 'official_desc': "Google Gemini MCP - Use Google's visual, video and chat capabilities in your AI Assistant. Uses MCP Apps to preview created images, landing pages, SVGs and video", 'stars_own': 34, 'stars_creator': None, 'stars_own_repo': 'houtini-ai/gemini-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'claude mcp add -e GEMINI_API_KEY=your-api-key-here -s user gemini -- npx -y @houtini/gemini-mcp', 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Gemini MCP - Google Gemini image generation, video and search grounding inside Claude\n\n \n \n \n \n\nI\'ve had this Gemini MCP server running in my Claude Desktop setup for the best part of a year now. It\'s one of the few I leave switched on permanently. Not because Gemini replaces Claude (it doesn\'t), but because grounded search, image generation, SVG diagrams and video are things Gemini happens to do well, and having them as tools inside Claude beats flipping between browser tabs.\n\nFourteen tools, built around the models people actually come looking for: **Nano Banana Pro** (gemini-3-pro-image) and **Nano Banana 2** for images, **Veo 3.1** for video with synchronised audio, **Gemini 4 Argon** for chat and **Gemini 3.1 Pro** for deep research, both with Google Search grounding. Everything previews inline in Claude Desktop through MCP Apps rather than landing as a file path you have to go and open.\n\nOne npx command. That\'s it.\n\nNew here? The full user guide walks through every tool, the free-tier gotcha, and what to do when something breaks.\n\n \n \n \n \n \n\n---\n\n> **Quick Navigation**\n>\n> What it makes | Get started | What it does | Image output | Configuration | Tools | Models | Requirements\n\n---\n\nWhat it makes\n\nEverything below came out of the tools in this repo, unretouched, on the afternoon I wrote this. Prompts are in the captions so you can judge for yourself.\n\n**Image generation with search grounding.** One generate_image call with use_search=true. Gemini looked up the actual Met Office forecast for the week, then drew it. The dates and temperatures are real (well, as real as a forecast gets).\n\n \n\n*Prompt: "A polished editorial infographic poster: London weather, this week. Use real forecast data from search for the next 5 days..." - gemini-3-pro-image, 16:9, 2K.*\n\n**Text that\'s actually spelled correctly.** This is the thing Nano Banana Pro does that the previous generation of image models couldn\'t. Every label here is straight out of the model.\n\n \n\n*Prompt: "A clean, magazine-quality infographic titled How an MCP tool call works, with four numbered steps..." - gemini-3-pro-image.*\n\n**Generate, then edit.** Left is generate_image. Right is edit_image on that file with one sentence of instructions: change the track to Monza in daylight, swap the rim lighting for window light, make the pedals red. Same cockpit, same camera angle.\n\n| generate_image | edit_image |\n|:---:|:---:|\n| | |\n\n**Image to video with Veo 3.1.** The night-time cockpit above, passed to generate_video as firstFrameImage. Eight seconds at 1080p with generated audio (engine note, tyre hiss) - the GIF below is silent and squashed for GitHub, the real file is a proper MP4.\n\n \n\n**SVG that you can actually use.** Not a picture of a diagram - real vector markup you can drop into a page, edit by hand or commit to a repo. Both of these are the raw .svg files the tool wrote to disk.\n\n| generate_svg style=technical | generate_svg style=data-viz |\n|:---:|:---:|\n| | |\n\n**A landing page from a paragraph.** generate_landing_page with a brief, a company name and a brand colour. Self-contained HTML, inline CSS, the little chart in the hero is animated SVG. Screenshot of the file opened in Chrome, nothing else touched.\n\n \n\n**And the fast one.** Nano Banana 2 (gemini-3.1-flash-image) for when you want volume rather than 4K. This took about ten seconds.\n\n \n\n---\n\nGet started in two minutes\n\n**Step 1: Get a Gemini API key**\n\nGo to Google AI Studio and create one.\n\nA word on the free tier, because the defaults lean on a paid model. Google\'s pricing page gives Gemini 3 Flash Preview a free tier, but the flagship models are paid-only - and gemini-4-argon is the default for gemini_chat and generate_landing_page, with gemini-3.1-pro-preview the default for gemini_deep_research (synthesis) and analyze_image. On a free key those calls will fail unless you point them at a free model: set GEMINI_DEFAULT_MODEL=gemini-3-flash-preview (covers chat and landing pages), GEMINI_DEEP_RESEARCH_MODEL and GEMINI_IMAGE_ANALYSIS_MODEL likewise, or pass model per call. generate_svg already defaults to gemini-3-flash-preview. Check the pricing page for the other models before relying on them - Google changes these tiers.\n\n**Step 2: Add to your Claude Desktop config**\n\nConfig file locations:\n- Windows: C:\\Users\\{username}\\AppData\\Roaming\\Claude\\claude_desktop_config.json\n- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n\n \n\n**Step 3: Restart Claude Desktop**\n\nThat\'s it. Tools show up automatically. npx pulls the package on first run, so there\'s no separate install.\n\nLocal build instead\n\nFor development, or if you\'d rather not rely on npx:\n\n \n\nThen point your config at the local build:\n\n \n\nClaude Code (CLI)\n\nClaude Code doesn\'t read claude_desktop_config.json. Use claude mcp add instead:\n\n \n\nWith an output directory for images and video:\n\n \n\nCheck with claude mcp get gemini - you want to see Status: Connected.\n\n---\n\nWhat it does\n\nChat with Google Search grounding\n\n \n\nGrounding is on by default. Gemini searches Google before it answers, so you get this month\'s information rather than a training-cutoff guess, and the sources come back as markdown links. For questions where you want pure reasoning ("explain this code", that sort of thing) set grounding: false.\n\nRuns on gemini-4-argon (Gemini 4 Argon) unless you say otherwise. Pass model: "gemini-3.8-flash" if you\'d rather have speed than depth, or model: "gemini-3.1-pro-preview" for the previous flagship. thinking_level works on Gemini 3.x and 4 models: high for the hard stuff, low to keep it snappy.\n\nDeep research\n\n \n\nRuns grounded search passes and then writes them up as one report. The passes run on gemini-3.8-flash with low thinking (they\'re gathering facts, not reasoning about them), and the synthesis at the end runs on gemini-3.1-pro-preview with high thinking. Two passes plus a synthesis is the default and lands in two to three minutes.\n\nThat split matters more than it sounds. Earlier versions ran everything on Pro with full thinking, and one pass on a broad question could run past Claude Desktop\'s four-minute timeout on its own. Keep max_iterations at 2 or 3 in Claude Desktop; in an IDE or an agent framework, 5 to 7 produces noticeably better synthesis. focus_areas takes an array if you want to steer each pass.\n\nImage generation with search grounding\n\n \n\nDefault model is gemini-3-pro-image, which is Nano Banana Pro now that it\'s out of preview. It renders legible text, does 4K, and it\'s the only one that supports conversational editing. gemini-3.1-flash-image (Nano Banana 2) is the fast option - not far off in quality, a fraction of the time and the cost. There\'s a Lite variant too if you\'re doing hundreds.\n\nWith use_search=true, Gemini looks things up before it draws. Weather, prices, sports results, that kind of data-driven image works reliably. The full-resolution file always goes to disk; the inline preview is resized to fit the MCP transport cap but the original is untouched.\n\nVideo generation with Veo 3.1\n\n \n\nGoogle\'s Veo 3.1. Four to eight second clips at up to 4K with native, synchronised audio. It\'s asynchronous on Google\'s side and takes two to five minutes; the tool polls until it\'s ready so you don\'t have to.\n\nOptions worth knowing about:\n- aspectRatio - 16:9 landscape or 9:16 for vertical\n- generateAudio - on by default; dialogue and effects that match the prompt\n- firstFrameImage - animate from a still (that\'s how the cockpit clip above was made)\n- referenceImages - up to three, for character or style consistency\n- sampleCount - up to four variations in one call\n- seed - deterministic output across runs\n- generateThumbnail - pulls a frame out with ffmpeg, if you\'ve got it in PATH\n- model - veo-3.1-lite-generate-preview if you want cheaper and faster\n\nSVG generation\n\nThis is the one people underestimate. The output isn\'t a picture of a diagram, it\'s the SVG markup itself - drop it into a codebase, a slide, a web page, and it scales without a', 'readme_raw_len': 17845, 'one_line_zh': 'Google Gemini MCP：在你的 AI 助手中使用 Google 的视觉、视频与聊天能力', 'one_line_plain': "Use Google Gemini's visual, video and chat capabilities in your AI Assistant"}, {'id': 3382, 'key': 'format37-youtube-mcp', 'kind': 'mcp', 'name': 'format37/youtube_mcp', 'one_line': '🐍 ☁️ - Transcribe YouTube videos to text using yt-dlp for audio and OpenAI Whisper for transcription, with…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'youtube transcriber mcp server', 'url': 'https://github.com/format37/youtube_mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/format37/youtube_mcp', 'downloads': None, 'stars': 32, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/format37.png?size=64', 'official_desc': '🐍 ☁️ - Transcribe YouTube videos to text using yt-dlp for audio and OpenAI Whisper for transcription, with chunked output for long videos.', 'stars_own': 32, 'stars_creator': None, 'stars_own_repo': 'format37/youtube_mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'youtube_mcp\nYoutube transcribation MCP server\n\nDemo Video\n\n \n\n*Click the image above to watch the demo video*\n\nRequirements:\n* OpenAI API key\n* Cookies\n\nServer installation\n* Clone the repo:\n \n* Extract your cookies. See cookies.md \nPlace cookies.txt in the ./mcp/ folder.\n* Generate MCP_KEY:\n \n* Define .env:\n \n* Provide run access\n \n* Run\n \n* Check that port is opened for incoming connections.\n\nClient configuration\n3. Add Bybit server to the Claude desktop config:\nExample:\n \nClient side\n`\n4. Restart Claude desktop\n5. Check that tws tools are listed in the tools list. Ask Claude to check ibkr account', 'readme_raw_len': 606, 'one_line_zh': '借助 yt-dlp 抓取音频、OpenAI Whisper 转写文字，将 YouTube 视频转为文本', 'one_line_plain': 'Transcribe YouTube videos to text with yt-dlp and OpenAI Whisper'}, {'id': 3166, 'key': 'aditya2755-video-edit-mcp', 'kind': 'mcp', 'name': 'video-edit-mcp', 'one_line': '🐍 🏠 🍎 🪟 - Video and audio editing: trim, merge, effects, overlays, format conversion, audio processing and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/Aditya2755/video-edit-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Aditya2755/video-edit-mcp', 'downloads': None, 'stars': 28, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Aditya2755.png?size=64', 'official_desc': '🐍 🏠 🍎 🪟 - Video and audio editing: trim, merge, effects, overlays, format conversion, audio processing and YouTube downloads, chaining steps without intermediate files.', 'stars_own': 28, 'stars_creator': None, 'stars_own_repo': 'Aditya2755/video-edit-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "Video Edit MCP Server 🎬\n\nA powerful **Model Context Protocol (MCP)** server designed for advanced video and audio editing operations. This server enables MCP clients—such as Claude Desktop, Cursor, and others—to perform comprehensive multimedia editing tasks through a standardized and unified interface.\n\n \n \n \n\nhttps://github.com/user-attachments/assets/134b8b82-80b1-4678-8930-ab53121b121f\n\n✨ Key Features\n\n🎥 Video Operations\n- **Basic Editing**: Trim, merge, resize, crop, rotate videos\n- **Effects**: Speed control, fade in/out, grayscale, mirror\n- **Overlays**: Add text, images, or video overlays with transparency\n- **Format Conversion**: Convert between formats with codec control\n- **Frame Operations**: Extract frames, create videos from images\n\n🎵 Audio Operations \n- **Audio Processing**: Extract, trim, loop, concatenate audio\n- **Volume Control**: Adjust levels, fade in/out effects\n- **Audio Mixing**: Mix multiple tracks together\n- **Integration**: Add audio to videos, replace soundtracks\n\n📥 Download & Utilities\n- **Video Download**: Download from YouTube and other platforms\n- **File Management**: Directory operations, file listing\n- **Path Suggestions**: Get recommended download locations\n\n🧹 Memory & Cleanup\n- **Smart Memory**: Chain operations without saving intermediate files\n- **Resource Management**: Clear memory, check stored objects\n- **Efficient Processing**: Keep objects in memory for complex workflows\n\n🔗 Operation Chaining\nSeamlessly chain multiple operations together without creating intermediate files. Process your video through multiple steps (trim → add audio → apply effects → add text) while keeping everything in memory for optimal performance.\n\n📋 Requirements\n\n- **Python 3.10 or higher**\n- **moviepy==1.0.3**\n- **yt-dlp>=2023.1.6**\n- **mcp>=1.12.2**\n- **typing-extensions>=4.0.0**\n\n⚙️ Installation & Setup\n\nFor Claude Desktop / Cursor MCP Integration\n\n**Ensure that uv is installed.** \nIf not, install it using the following PowerShell command:\n\n \n\nAdd this configuration to your MCP configuration file:\n\n \n\n**Configuration file locations:**\n- **Claude Desktop (Windows)**: %APPDATA%/Claude/claude_desktop_config.json\n- **Claude Desktop (macOS)**: ~/Library/Application Support/Claude/claude_desktop_config.json\n- **Cursor**: .cursor/mcp.json in your project root\n\nManual Installation\n\n \n\n🏗️ Project Structure\n\n \n\n🎯 Example Usage\n\n \n\n🚀 Future Enhancements & Contributions\n\nWe welcome contributions in these exciting areas:\n\n🤖 AI-Powered Features\n- **Speech-to-Text (STT)**: Automatic subtitle generation and transcription\n- **Text-to-Speech (TTS)**: AI voice synthesis for narration\n- **Audio Enhancement**: AI-based noise reduction and audio quality improvement\n- **Smart Timestamps**: Automatic scene detection and chapter generation\n- **Face Tracking**: Advanced face detection and tracking for automatic editing\n- **Object Recognition**: Track and edit based on detected objects\n- **Content Analysis**: AI-powered content categorization and tagging\n\n🤝 Contributing\n\n1. Fork the repository\n2. Create a feature branch: git checkout -b feature/amazing-feature\n3. Commit your changes: git commit -m 'Add amazing feature'\n4. Push to the branch: git push origin feature/amazing-feature\n5. Open a Pull Request\n\n📄 License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n---\n\n \n\n**Made with ❤️ for the AI and multimedia editing community**\n\n⭐ Star this project | 🤝 Contribute | 📖 Documentation", 'readme_raw_len': 3472, 'one_line_zh': '音视频编辑：裁剪、合并、特效、叠加、格式转换与音频处理等。', 'one_line_plain': 'Video and audio editing lets you trim, merge, add effects and overlays, convert formats, and process audio'}]/2026-10-20 https://anclum.com/en/[{'id': 8056, 'key': 'skillsmp-com-creators-aldegad-sprite-gen-skill', 'kind': 'skill', 'name': 'sprite-gen', 'one_line': 'Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults,…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/aldegad/sprite-gen/skill', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/aldegad/sprite-gen/skill', 'downloads': None, 'stars': 2836, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/aldegad.png?size=64', 'official_desc': 'Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults, automatic cleanup and optional curation. Handles sprite requests, ordinary image generation/editing, typography logos (로고, 타이포그래피, 타이틀 글자, 타이틀 로고, wordmark, logo, title lettering: a title scr', 'stars_own': None, 'stars_creator': 2918, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'aldegad/sprite-gen', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '通过 GPT 或 Grok 生成图像与游戏精灵，带引导式供应商选择与分开保存的默认值…', 'one_line_plain': 'Generate images and game sprites using GPT or Grok, with guided provider choices and saved defaults'}, {'id': 5749, 'key': 'skillsmp-com-creators-daymade-claude-code-skills-frontend-visual-qa', 'kind': 'skill', 'name': 'frontend-visual-qa', 'one_line': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'downloads': None, 'stars': 1452, 'installs': None, 'mentions': 5, 'score': 503.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/daymade.png?size=64', 'official_desc': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard, design-system, or Electron/native app — via real-browser/native-app journeys and a Playwright sweep. Use after UI implementation to find typography, overflow, responsive, routing, data-viz, browser-output', 'stars_own': None, 'stars_creator': 1477, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'daymade/claude-code-skills', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '审计已渲染的界面，以及 Markdown 与 Obsidian 中的图片排版。', 'one_line_plain': 'The tool audits already-rendered UI and Markdown/Obsidian image layout for web apps, decks, dashboards, and more'}, {'id': 5746, 'key': 'skillsmp-com-creators-youmind-openlab-ai-image-prompts-skill-skill', 'kind': 'skill', 'name': 'ai-image-prompts-skill', 'one_line': 'Recommend curated prompts from a 10,000+ real-world image generation prompt library. Works with ANY AI image…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/youmind-openlab/ai-image-prompts-skill/skill', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/youmind-openlab/ai-image-prompts-skill/skill', 'downloads': None, 'stars': 1248, 'installs': None, 'mentions': 8, 'score': 803.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/YouMind-OpenLab.png?size=64', 'official_desc': 'Recommend curated prompts from a 10,000+ real-world image generation prompt library.\nWorks with ANY AI image model — Nano Banana Pro, Nano Banana 2, Seedream 5.0, GPT Image 1.5,\nMidjourney, DALL-E 3, Flux, Stable Diffusion, and more.\n\nUse this skill when users want to:\n- Find proven image generation', 'stars_own': None, 'stars_creator': 1309, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'youmind-openlab/ai-image-prompts-skill', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '从一万多条真实图像生成 Prompt 中推荐精选提示词，兼容任意 AI 绘图工具。', 'one_line_plain': 'Get curated prompts from a 10,000+ real-world image generation prompt library that works with any AI image tool'}, {'id': 3109, 'key': 'aidc-ai-pixelle-mcp', 'kind': 'mcp', 'name': 'AIDC-AI/Pixelle-MCP', 'one_line': '🐍 📇 🏠 - Omnimodal AIGC framework that turns ComfyUI workflows into MCP tools without code, for text, image,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/ATH-MaaS/Pixelle-MCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/AIDC-AI/Pixelle-MCP', 'downloads': None, 'stars': 1131, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/AIDC-AI.png?size=64', 'official_desc': 'or quick experience and testing** Temporary Run **View uvx CLI Reference →** Persistent Installation **View pip CLI Reference →** After startup, it will automatically enter the **configuration wizard** to guide you through execution engine selection (ComfyUI/RunningHub) and LLM configuration.', 'stars_own': 1131, 'stars_creator': None, 'stars_own_repo': 'ATH-MaaS/Pixelle-MCP', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "🎨 Pixelle MCP - Omnimodal Agent Framework \n\n English | 中文 \n\n ✨ An AIGC solution based on the MCP protocol, supporting both local ComfyUI and cloud ComfyUI (RunningHub) modes, seamlessly converting workflows into MCP tools with zero code. \n\n \n\nhttps://github.com/user-attachments/assets/65422cef-96f9-44fe-a82b-6a124674c417\n\n📋 Recent Updates\n\n- ✅ **2025-09-29**: Added RunningHub cloud ComfyUI support, enabling workflow execution without local GPU and ComfyUI environment\n- ✅ **2025-09-03**: Architecture refactoring from three services to unified application; added CLI tool support; published to PyPI\n- ✅ **2025-08-12**: Integrated the LiteLLM framework, adding multi-model support for Gemini, DeepSeek, Claude, Qwen, and more\n\n🚀 Features\n\n- ✅ 🔄 **Full-modal Support**: Supports TISV (Text, Image, Sound/Speech, Video) full-modal conversion and generation\n- ✅ 🚀 **Dual Execution Modes**: Local ComfyUI self-hosted environment + RunningHub cloud ComfyUI service, users can flexibly choose based on their needs\n- ✅ 🧩 **ComfyUI Ecosystem**: Built on ComfyUI, inheriting all capabilities from the open ComfyUI ecosystem\n- ✅ 🔧 **Zero-code Development**: Defines and implements the Workflow-as-MCP Tool solution, enabling zero-code development and dynamic addition of new MCP Tools\n- ✅ 🗄️ **MCP Server**: Based on the MCP protocol, supporting integration with any MCP client (including but not limited to Cursor, Claude Desktop, etc.)\n- ✅ 🌐 **Web Interface**: Developed based on the Chainlit framework, inheriting Chainlit's UI controls and supporting integration with more MCP Servers\n- ✅ 📦 **One-click Deployment**: Supports PyPI installation, CLI commands, Docker and other deployment methods, ready to use out of the box\n- ✅ ⚙️ **Simplified Configuration**: Uses environment variable configuration scheme, simple and intuitive configuration\n- ✅ 🤖 **Multi-LLM Support**: Supports multiple mainstream LLMs, including OpenAI, Ollama, Gemini, DeepSeek, Claude, Qwen, and more\n\n📁 Project Architecture\n\nPixelle MCP adopts a **unified architecture design**, integrating MCP server, web interface, and file services into one application, providing:\n\n- 🌐 **Web Interface**: Chainlit-based chat interface supporting multimodal interaction\n- 🔌 **MCP Endpoint**: For external MCP clients (such as Cursor, Claude Desktop) to connect\n- 📁 **File Service**: Handles file upload, download, and storage\n- 🛠️ **Workflow Engine**: Supports both local ComfyUI and cloud ComfyUI (RunningHub) workflows, automatically converts workflows into MCP tools\n\n \n\n \n\n🏃\u200d♂️ Quick Start\n\nChoose the deployment method that best suits your needs, from simple to complex:\n\n🎯 Method 1: One-click Experience\n\n> 💡 **Zero configuration startup, perfect for quick experience and testing**\n\n🚀 Temporary Run\n\n \n\n📚 **View uvx CLI Reference →**\n\n📦 Persistent Installation\n\n \n\n📚 **View pip CLI Reference →**\n\nAfter startup, it will automatically enter the **configuration wizard** to guide you through execution engine selection (ComfyUI/RunningHub) and LLM configuration.\n\n🛠️ Method 2: Local Development Deployment\n\n> 💡 **Supports custom workflows and secondary development**\n\n📥 1. Get Source Code\n\n \n\n🚀 2. Start Service\n\n \n\n📚 **View Complete CLI Reference →**\n\n🔧 3. Add Custom Workflows (Optional)\n\n \n\n**⚠️ Important**: Make sure to test workflows in ComfyUI first to ensure they run properly, otherwise execution will fail.\n\n🐳 Method 3: Docker Deployment\n\n> 💡 **Suitable for production environments and containerized deployment**\n\n📋 1. Prepare Configuration\n\n \n\n🚀 2. Start Container\n\n \n\n🌐 Access Services\n\nRegardless of which method you use, after startup you can access via:\n\n- **🌐 Web Interface**: http://localhost:9004 \n *Default username and password are both dev, can be modified after startup*\n- **🔌 MCP Endpoint**: http://localhost:9004/pixelle/mcp \n *For MCP clients like Cursor, Claude Desktop to connect*\n\n**💡 Port Configuration**: Default port is 9004, can be customized via environment variable PORT=your_port.\n\n⚙️ Initial Configuration\n\nOn first startup, the system will automatically detect configuration status:\n\n1. **🚀 Execution Engine Selection**: Choose between local ComfyUI or RunningHub cloud service\n2. **🤖 LLM Configuration**: Configure at least one LLM provider (OpenAI, Ollama, etc.)\n3. **📁 Workflow Directory**: System will automatically create necessary directory structure\n\n🌐 RunningHub Cloud Mode Advantages\n- ✅ **Zero Hardware Requirements**: No need for local GPU or high-performance hardware\n- ✅ **No Environment Setup**: No need to install and configure ComfyUI locally\n- ✅ **Ready to Use**: Register and get API key to start immediately\n- ✅ **Stable Performance**: Professional cloud infrastructure ensures stable execution\n- ✅ **Auto Scaling**: Automatically handles concurrent requests and resource allocation\n\n🏠 Local ComfyUI Mode Advantages\n- ✅ **Full Control**: Complete control over execution environment and model versions\n- ✅ **Privacy Protection**: All data processing happens locally, ensuring data privacy\n- ✅ **Custom Models**: Support for custom models and nodes not available in cloud\n- ✅ **No Network Dependency**: Can work offline without internet connection\n- ✅ **Cost Control**: No cloud service fees for high-frequency usage\n\n**🆘 Need Help?** Join community groups for support (see Community section below)\n\n🛠️ Add Your Own MCP Tool\n\n⚡ One workflow = One MCP Tool, supports two addition methods:\n\n📋 **Method 1: Local ComfyUI Workflow** - Export API format workflow files\n📋 **Method 2: RunningHub Workflow ID** - Use cloud workflow IDs directly\n\n \n\n🎯 1. Add the Simplest MCP Tool\n\n* 📝 Build a workflow in ComfyUI for image Gaussian blur (Get it here), then set the LoadImage node's title to $image.image! as shown below:\n \n\n* 📤 Export it as an API format file and rename it to i_blur.json. You can export it yourself or use our pre-exported version (Get it here)\n\n* 📋 Copy the exported API workflow file (must be API format), input it on the web page, and let the LLM add this Tool\n\n \n\n* ✨ After sending, the LLM will automatically convert this workflow into an MCP Tool\n\n \n\n* 🎨 Now, refresh the page and send any image to perform Gaussian blur processing via LLM\n\n \n\n🔌 2. Add a Complex MCP Tool\n\nThe steps are the same as above, only the workflow part differs (Download workflow: UI format and API format)\n\n> **Note:** When using RunningHub, you only need to input the corresponding workflow ID, no need to download and upload workflow files.\n\n \n\n🔧 ComfyUI Workflow Custom Specification\n\n🎨 Workflow Format\nThe system supports ComfyUI workflows. Just design your workflow in the canvas and export it as API format. Use special syntax in node titles to define parameters and outputs.\n\n📝 Parameter Definition Specification\n\nIn the ComfyUI canvas, double-click the node title to edit, and use the following DSL syntax to define parameters:\n\n \n\n🔍 Syntax Explanation:\n- param_name: The parameter name for the generated MCP tool function\n- ~: Optional, indicates URL parameter upload processing, returns relative path\n- field_name: The corresponding input field in the node\n- !: Indicates this parameter is required\n- description: Description of the parameter\n\n💡 Example:\n\n**Required parameter example:**\n\n- Set LoadImage node title to: $image.image!:Input image URL\n- Meaning: Creates a required parameter named image, mapped to the node's image field\n\n**URL upload processing example:**\n\n- Set any node title to: $image.~image!:Input image URL\n- Meaning: Creates a required parameter named image, system will automatically download URL and upload to ComfyUI, returns relative path\n\n> 📝 Note: LoadImage, VHS_LoadAudioUpload, VHS_LoadVideo and other nodes have built-in functionality, no need to add ~ marker\n\n🎯 Type Inference Rules\n\nThe system automatically infers parameter types based on the current value of the node field:\n- 🔢 int: Integer values (e.g. 512, 1024)\n- 📊 float: Floating-point values (e.g. 1.5, 3.14)\n- ✅ bool: Boolean values (e.g. true, false)\n- 📝 str: String values (default typ", 'readme_raw_len': 11185, 'one_line_zh': '全模态 AIGC 框架，无需写代码即可把 ComfyUI 工作流转成 MCP 工具，覆盖文本与图像等。', 'one_line_plain': 'Omnimodal AIGC framework converts ComfyUI workflows into MCP tools for text, image, and other media'}, {'id': 1387, 'key': 'ckreiling-mcp-server-docker', 'kind': 'mcp', 'name': 'ckreiling/mcp-server-docker', 'one_line': '🐍 🏠 - Manage Docker containers, images, volumes and networks', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for Docker', 'url': 'https://github.com/ckreiling/mcp-server-docker', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ckreiling/mcp-server-docker', 'downloads': None, 'stars': 748, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ckreiling.png?size=64', 'official_desc': '🐍 🏠 - Manage Docker containers, images, volumes and networks.', 'stars_own': 748, 'stars_creator': None, 'stars_own_repo': 'ckreiling/mcp-server-docker', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '🐋 Docker MCP server\n\nAn MCP server for managing Docker with natural language!\n\n🪩 What can it do?\n\n- 🚀 Compose containers with natural language\n- 🔍 Introspect & debug running containers\n- 📀 Manage persistent data with Docker volumes\n\n❓ Who is this for?\n\n- Server administrators: connect to remote Docker engines for e.g. managing a\n public-facing website.\n- Tinkerers: run containers locally and experiment with open-source apps\n supporting Docker.\n- AI enthusiasts: push the limits of that an LLM is capable of!\n\nDemo\n\nA quick demo showing a WordPress deployment using natural language:\n\nhttps://github.com/user-attachments/assets/65e35e67-bce0-4449-af7e-9f4dd773b4b3\n\n🏎️ Quickstart\n\nInstall\n\nClaude Desktop\n\nOn MacOS: ~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n Install from PyPi with uv \n\nIf you don\'t have uv installed, follow the installation instructions for your\nsystem:\nlink\n\nThen add the following to your MCP servers file:\n\n \n\n \n\n \n Install with Docker \n\nPurely for convenience, the server can run in a Docker container.\n\nAfter cloning this repository, build the Docker image:\n\n \n\nAnd then add the following to your MCP servers file:\n\n \n\nNote that we mount the Docker socket as a volume; this ensures the MCP server\ncan connect to and control the local Docker daemon.\n\n \n\n📝 Prompts\n\n🎻 docker_compose\n\nUse natural language to compose containers. See above for a demo.\n\nProvide a Project Name, and a description of desired containers, and let the LLM\ndo the rest.\n\nThis prompt instructs the LLM to enter a plan+apply loop. Your interaction\nwith the LLM will involve the following steps:\n\n1. You give the LLM instructions for which containers to bring up\n2. The LLM calculates a concise natural language plan and presents it to you\n3. You either:\n - Apply the plan\n - Provide the LLM feedback, and the LLM recalculates the plan\n\nExamples\n\n- name: nginx, containers: "deploy an nginx container exposing it on port\n 9000"\n- name: wordpress, containers: "deploy a WordPress container and a supporting\n MySQL container, exposing Wordpress on port 9000"\n\nResuming a Project\n\nWhen starting a new chat with this prompt, the LLM will receive the status of\nany containers, volumes, and networks created with the given project name.\n\nThis is mainly useful for cleaning up, in-case you lose a chat that was\nresponsible for many containers.\n\n📔 Resources\n\nThe server exposes resource templates rather than enumerating currently-running containers:\n\n- docker://containers/{container_id}/logs (text/plain)\n- docker://containers/{container_id}/stats (application/json)\n\nRead either URI with a Docker container ID or name.\n\n🔨 Tools\n\nContainers\n\n- list_containers\n- create_container\n- run_container\n- recreate_container\n- start_container\n- fetch_container_logs\n- stop_container\n- remove_container\n\nImages\n\n- list_images\n- pull_image\n- push_image\n- build_image\n- remove_image\n\nNetworks\n\n- list_networks\n- create_network\n- remove_network\n\nVolumes\n\n- list_volumes\n- create_volume\n- remove_volume\n\n🚧 Disclaimers\n\nSensitive Data\n\n**DO NOT CONFIGURE CONTAINERS WITH SENSITIVE DATA.** This includes API keys,\ndatabase passwords, etc.\n\nAny sensitive data exchanged with the LLM is inherently compromised, unless the\nLLM is running on your local machine.\n\nIf you are interested in securely passing secrets to containers, file an issue\non this repository with your use-case.\n\nReviewing Created Containers\n\nBe careful to review the containers that the LLM creates. Docker is not a secure\nsandbox, and therefore the MCP server can potentially impact the host machine\nthrough Docker.\n\nFor safety reasons, this MCP server doesn\'t support sensitive Docker options\nlike --privileged or --cap-add/--cap-drop. If these features are of interest\nto you, file an issue on this repository with your use-case.\n\n🛠️ Configuration\n\nThis server uses the Python Docker SDK\'s from_env method. For configuration\ndetails, see\nthe documentation.\n\nConnect to Docker over SSH\n\nThis MCP server can connect to a remote Docker daemon over SSH.\n\nSimply set a ssh:// host URL in the MCP server definition:\n\n \n\n💻 Development\n\nPrefer using Devbox to configure your development environment. The server uses\nMCP Python SDK v2\'s high-level MCPServer API and can be inspected directly:\n\n \n\nRun the hermetic test and lint suite without a Docker daemon:\n\n \n\nSee the devbox.json for helpful development commands.\n\nAfter setting up devbox you can configure your Claude MCP config to use it:', 'readme_raw_len': 4511, 'one_line_zh': '管理 Docker 容器、镜像、数据卷和网络', 'one_line_plain': 'Manage Docker containers, images, volumes and networks'}, {'id': 4206, 'key': 'evalstate-mcp-hfspace', 'kind': 'mcp', 'name': 'evalstate/mcp-hfspace', 'one_line': '📇 ☁️ - Use Hugging Face Spaces for open-source image generation, chat, vision and more, with image, audio and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP Server to Use HuggingFace spaces, easy configuration and Claude Desktop mode.', 'url': 'https://github.com/evalstate/mcp-hfspace', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/evalstate/mcp-hfspace', 'downloads': None, 'stars': 389, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/evalstate.png?size=64', 'official_desc': '📇 ☁️ - Use Hugging Face Spaces for open-source image generation, chat, vision and more, with image, audio and text uploads and downloads.', 'stars_own': 389, 'stars_creator': None, 'stars_own_repo': 'evalstate/mcp-hfspace', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'mcp-hfspace MCP Server 🤗\n\n> [!TIP]\n>\n> You can access and configure Hugging Face MCP services directly at https://hf.co/mcp, including Gradio spaces.\n>\n> This project has been superceded by the official Hugging Face MCP Server and Gradio MCP Endpoints.\n> \n> Alternatively you can run hf-mcp-server locally as a STDIO Server, or with robust support for SSE, Streaming HTTP and Streaming HTTP JSON Mode. This also runs a local UI for selecting tools and endpoints and supports ToolListChangedNotifications too.\n\nhf.co/mcp\n\n \n\nmcp-hfspace\n\nRead the introduction here llmindset.co.uk/resources/mcp-hfspace/\n\nConnect to Hugging Face Spaces with minimal setup needed - simply add your spaces and go!\n\nBy default, it connects to black-forest-labs/FLUX.1-schnell providing Image Generation capabilities to Claude Desktop.\n\n \n\nGradio MCP Support\n\n> [!TIP]\n> Gradio 5.28 now has integrated MCP Support via SSE: https://huggingface.co/blog/gradio-mcp. Check out whether your target Space is MCP Enabled!\n\nInstallation\n\nNPM Package is @llmindset/mcp-hfspace.\n\nInstall a recent version of NodeJS for your platform, then add the following to the mcpServers section of your claude_desktop_config.json file:\n\n \n\nPlease make sure you are using Claude Desktop 0.78 or greater.\n\nThis will get you started with an Image Generator.\n\nBasic setup\n\nSupply a list of HuggingFace spaces in the arguments. mcp-hfspace will find the most appropriate endpoint and automatically configure it for usage. An example claude_desktop_config.json is supplied below.\n\nBy default the current working directory is used for file upload/download. On Windows this is a read/write folder at \\users\\ \\AppData\\Roaming\\Claude\\ Text Output: david bowie\n \n\n_If you are uploading something to Claude\'s context use the Paperclip Attachment button, otherwise specify the filename for the Server to send directly._\n\nWe can also supply a URL. For example : use paligemma to detect humans in https://e3.365dm.com/24/12/1600x900/skynews-taylor-swift-eras-tour_6771083.jpg?20241209000914 -> One person is detected in the image - Taylor Swift on stage.\n\nExample 3 - Text-to-Speech (Download Audio)\n\nIn _Claude Desktop Mode_, the audio file is saved in the WORK_DIR, and Claude is notified of the creation. If not in desktop mode, the file is returned as a base64 encoded resource to the Client (useful if it supports embedded Audio attachments).\n\n \n\nExample 4 - Speech-to-Text (Upload Audio)\n\nHere, we use hf-audio/whisper-large-v3-turbo to transcribe some audio, and make it available to Claude.\n\n \n\nExample 5 - Image-to-Image\n\nIn this example, we specify the filename for microsoft/OmniParser to use, and get returned an annotated Image and 2 separate pieces of text: descriptions and coordinates. The prompt used was use omniparser to analyse ./screenshot.png and use the analysis to produce an artifact that reproduces that screen. DawnC/Pawmatch is also good at this.\n\n \n\nExample 6 - Chat\n\nIn this example, Claude sets a number of reasoning puzzles for Qwen, and asks follow-up questions for clarification.\n\n \n\nSpecifying API Endpoint\n\nIf you need, you can specify a specific API Endpoint by adding it to the spacename. So rather than passing in Qwen/Qwen2.5-72B-Instruct you would use Qwen/Qwen2.5-72B-Instruct/model_chat.\n\nClaude Desktop Mode\n\nThis can be disabled with the option --desktop-mode=false or the environment variable CLAUDE_DESKTOP_MODE=false. In this case, content as returned as an embedded Base64 encoded Resource.\n\nRecommended Spaces\n\nSome recommended spaces to try:\n\nImage Generation\n\n- shuttleai/shuttle-3.1-aesthetic\n- black-forest-labs/FLUX.1-schnell\n- yanze/PuLID-FLUX\n- gokaygokay/Inspyrenet-Rembg (Background Removal)\n- diyism/Datou1111-shou_xin - Beautiful Pencil Drawings\n\nChat\n\n- Qwen/Qwen2.5-72B-Instruct\n- prithivMLmods/Mistral-7B-Instruct-v0.3\n\nText-to-speech / Audio Generation\n\n- fantaxy/Sound-AI-SFX\n- parler-tts/parler_tts\n\nSpeech-to-text\n\n- hf-audio/whisper-large-v3-turbo\n- (the openai models use unnamed parameters so will not work)\n\nText-to-music\n\n- haoheliu/audioldm2-text2audio-text2music\n\nVision Tasks\n\n- microsoft/OmniParser\n- merve/paligemma2-vqav2\n- merve/paligemma-doc\n- DawnC/PawMatchAI\n- DawnC/PawMatchAI/on_find_match_click - for interactive dog recommendations\n\nOther Features\n\nPrompts\n\nPrompts for each Space are generated, and provide an opportunity to input. Bear in mind that often Spaces aren\'t configured with particularly helpful labels etc. Claude is actually very good at figuring this out, and the Tool description is quite rich (but not visible in Claude Desktop).\n\nResources\n\nA list of files in the WORK_DIR is returned, and as a convenience returns the name as "Use the file..." text. If you want to add something to Claude\'s context, use the paperclip - otherwise specify the filename for the MCP Server. Claude does not support transmitting resources from within Context.\n\nPrivate Spaces\n\nPrivate Spaces are supported with a HuggingFace token. The Token is used to download and save generated content.\n\nUsing Claude Desktop\n\nTo use with Claude Desktop, add the server config:\n\nOn MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n\nKnown Issues and Limitations\n\nmcp-hfspace\n\n- Endpoints with unnamed parameters are unsupported for the moment.\n- Full translation from some complex Python types to suitable MCP formats.\n\nClaude Desktop\n\n- Claude Desktop 0.75 doesn\'t seem to respond to errors from the MCP Server, timing out instead. For persistent issues, use the MCP Inspector to get a better look at diagnosing what\'s going wrong. If something suddenly stops working, it\'s probably due to exhausting your HuggingFace ZeroGPU quota - try again after a short period, or set up your own Space for hosting.\n- Claude Desktop seems to use a hard timeout value of 60s, and doesn\'t appear to use Progress Notifications to manage UX or keep-alive. If you are using ZeroGPU spaces, large/heavy jobs may timeout. Check the WORK_DIR for results though; the MCP Server will still capture and save the result if it was produced.\n- Claude Desktops reporting of Server Status, logging etc. isn\'t great - use @modelcontextprotocol/inspector to help diagnose issues.\n\nHuggingFace Spaces\n\n- If ZeroGPU quotas or queues are too long, try duplicating the space. If your job takes less than sixty seconds, you can usually change the function decorator @spaces.GPU(duration=20) in app.py` to request less quota when running the job.\n- Passing HF_TOKEN will make ZeroGPU quotas apply to your (Pro) HF account\n- If you have a private space, and dedicated hardware your HF_TOKEN will give you direct access to that - no quota\'s apply. I recommend this if you are using for any kind of Production task.\n\nThird Party MCP Services', 'readme_raw_len': 6795, 'one_line_zh': '借助 Hugging Face Spaces 实现开源图像生成、对话、视觉等多项能力', 'one_line_plain': 'Create and share AI applications using Hugging Face Spaces for image, text, and audio tasks'}, {'id': 3157, 'key': 'sunriseapps-imagesorcery-mcp', 'kind': 'mcp', 'name': 'sunriseapps/imagesorcery-mcp', 'one_line': '🐍 🏠 🐧 🍎 🪟 - Computer vision-based image recognition and editing tools', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'An MCP server providing tools for image processing operations', 'url': 'https://github.com/sunriseapps/imagesorcery-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/sunriseapps/imagesorcery-mcp', 'downloads': None, 'stars': 333, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/sunriseapps.png?size=64', 'official_desc': '🐍 🏠 🐧 🍎 🪟 - Computer vision-based image recognition and editing tools.', 'stars_own': 333, 'stars_creator': None, 'stars_own_repo': 'sunriseapps/imagesorcery-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '🪄 ImageSorcery MCP\n**ComputerVision-based 🪄 sorcery of local image recognition and editing tools for AI assistants**\n\nOfficial website: imagesorcery.net\n\n \n \n \n\n \n \n \n\n✅ With ImageSorcery MCP\n\n🪄 ImageSorcery empowers AI assistants with powerful image processing capabilities:\n\n- ✅ Crop, resize, and rotate images with precision\n- ✅ Remove background\n- ✅ Draw text and shapes on images\n- ✅ Add logos and watermarks\n- ✅ Detect objects using state-of-the-art models\n- ✅ Extract text from images with OCR\n- ✅ Use a wide range of pre-trained models for object detection, OCR, and more\n- ✅ Do all of this **locally**, without sending your images to any servers\n\nJust ask your AI to help with image tasks:\n\n> "copy photos with pets from folder photos to folder pets"\n \n\n> "Find a cat at the photo.jpg and crop the image in a half in height and width to make the cat be centered"\n \n😉 _**Hint:** Use full path to your files"._\n\n> "Enumerate form fields on this form.jpg with foduucom/web-form-ui-field-detection model and fill the form.md with a list of described fields"\n \n😉 _**Hint:** Specify the model and the confidence"._\n\n😉 _**Hint:** Add "use imagesorcery" to make sure it will use the proper tool"._\n\nYour tool will combine multiple tools listed below to achieve your goal.\n\n🛠️ Available Tools\n\n| Tool | Description | Example Prompt |\n|------|-------------|----------------|\n| blur | Blurs specified rectangular or polygonal areas of an image using OpenCV. Can also invert the provided areas e.g. to blur background. | "Blur the area from (150, 100) to (250, 200) with a blur strength of 21 in my image \'test_image.png\' and save it as \'output.png\'" |\n| change_color | Changes the color palette of an image | "Convert my image \'test_image.png\' to sepia and save it as \'output.png\'" |\n| config | View and update ImageSorcery MCP configuration settings | "Show me the current configuration" or "Set the default detection confidence to 0.8" |\n| crop | Crops an image using OpenCV\'s NumPy slicing approach | "Crop my image \'input.png\' from coordinates (10,10) to (200,200) and save it as \'cropped.png\'" |\n| detect | Detects objects in an image using models from Ultralytics. Can return segmentation masks (as PNG files) or polygons. | "Detect objects in my image \'photo.jpg\' with a confidence threshold of 0.4" |\n| draw_arrows | Draws arrows on an image using OpenCV | "Draw a red arrow from (50,50) to (150,100) on my image \'photo.jpg\'" |\n| draw_circles | Draws circles on an image using OpenCV | "Draw a red circle with center (100,100) and radius 50 on my image \'photo.jpg\'" |\n| draw_lines | Draws lines on an image using OpenCV | "Draw a red line from (50,50) to (150,100) on my image \'photo.jpg\'" |\n| draw_rectangles | Draws rectangles on an image using OpenCV | "Draw a red rectangle from (50,50) to (150,100) and a filled blue rectangle from (200,150) to (300,250) on my image \'photo.jpg\'" |\n| draw_texts | Draws text on an image using OpenCV | "Add text \'Hello World\' at position (50,50) and \'Copyright 2023\' at the bottom right corner of my image \'photo.jpg\'" |\n| fill | Fills specified rectangular, polygonal, or mask-based areas of an image with a color and opacity, or makes them transparent. Can also invert the provided areas e.g. to remove background. | "Fill the area from (150, 100) to (250, 200) with semi-transparent red in my image \'test_image.png\'" |\n| find | Finds objects in an image based on a text description. Can return segmentation masks (as PNG files) or polygons. | "Find all dogs in my image \'photo.jpg\' with a confidence threshold of 0.4" |\n| get_metainfo | Gets metadata information about an image file | "Get metadata information about my image \'photo.jpg\'" |\n| ocr | Performs Optical Character Recognition (OCR) on an image using EasyOCR | "Extract text from my image \'document.jpg\' using OCR with English language" |\n| overlay | Overlays one image on top of another, handling transparency | "Overlay \'logo.png\' on top of \'background.jpg\' at position (10, 10)" |\n| resize | Resizes an image using OpenCV | "Resize my image \'photo.jpg\' to 800x600 pixels and save it as \'resized_photo.jpg\'" |\n| rotate | Rotates an image using imutils.rotate_bound function | "Rotate my image \'photo.jpg\' by 45 degrees and save it as \'rotated_photo.jpg\'" |\n\n😉 _**Hint:** detailed information and usage instructions for each tool can be found in the tool\'s /src/imagesorcery_mcp/tools/README.md._\n\n📚 Available Resources\n\n| Resource URI | Description | Example Prompt |\n|--------------|-------------|----------------|\n| models://list | Lists all available models in the models directory | "Which models are available in ImageSorcery?" |\n\n😉 _**Hint:** detailed information and usage instructions for each resource can be found in the resource\'s /src/imagesorcery_mcp/resources/README.md._\n\n💬 Available Prompts\n\n| Prompt Name | Description | Example Usage |\n|-------------|-------------|---------------|\n| remove-background | Guides the AI through a comprehensive background removal workflow using object detection and masking tools | "Use the remove-background prompt to remove the background from my photo \'portrait.jpg\', keeping only the person" |\n\n😉 _**Hint:** detailed information and usage instructions for each prompt can be found in the prompt\'s /src/imagesorcery_mcp/prompts/README.md._\n\n🚀 Getting Started\n\nRequirements\n\n- Python 3.10 or higher\n- pipx (recommended) - for easy installation and virtual environment management\n- ffmpeg, libsm6, libxext6, libgl1-mesa-glx - system libraries required by OpenCV\n- Claude.app, Cline, or another MCP client\n\n \n Virtual environments (e.g. Docker) \n\nThese dependencies are typically included with OpenCV installation and don\'t require separate installation. But they might be missing in some virtual environments like Docker.\n\n**For Ubuntu/Debian systems:**\n \n\n**For Docker containers:**\nAdd this line to your Dockerfile:\n \n \n\nInstallation\n\n \n \n For Cline just copy-paste LLM-INSTALL.md \n\n \n \n\nMethod 1: Using pipx (Recommended)\n\npipx is the recommended way to install ImageSorcery MCP as it automatically handles virtual environment creation and management, making the installation process much simpler.\n\n \n 0. Install pipx (if not already installed): \n\n0. **Install pipx (if not already installed):**\n \n \n\n1. **Install ImageSorcery MCP with pipx:**\n \n\n2. **Run the post-installation script:**\n This step is crucial. It downloads the required models and attempts to install the clip Python package from GitHub.\n \n\nMethod 2: Manual Virtual Environment (Plan B)\n\n \n If pipx doesn\'t work for your system, you can manually create a virtual environment \n\nFor reliable installation of all components, especially the clip package (installed via the post-install script), it is **strongly recommended to use Python\'s built-in venv module instead of uv venv**.\n\n1. **Create and activate a virtual environment:**\n \n\n2. **Install the package into the activated virtual environment:**\n You can use pip or uv pip.\n \n\n3. **Run the post-installation script:**\n This step is crucial. It downloads the required models and attempts to install the clip Python package from GitHub into the active virtual environment.\n \n\n**Note:** When using this method, you\'ll need to provide the full path to the executable in your MCP client configuration (e.g., /full/path/to/venv/bin/imagesorcery-mcp).\n \n\nAdditional Notes\n \n What does the post-installation script do? \nThe imagesorcery-mcp --post-install script performs the following actions:\n\n- **Creates a config.toml configuration file** in the current directory, allowing users to customize default tool parameters.\n- Creates a models directory (usually within the site-packages directory of your virtual environment, or a user-specific location if installed globally) to store pre-trained models.\n- Generates an initial models/model_descriptions.json file there.\n- Downloads default YOLO models (yoloe-11l-seg-pf.pt, yoloe-11s-seg-pf.pt, yoloe-11l-seg.pt, yoloe-11s-seg.pt) required by the detect tool into this model', 'readme_raw_len': 16425, 'one_line_zh': '基于计算机视觉的图像识别与编辑工具', 'one_line_plain': 'Use computer vision tools for image recognition and editing'}, {'id': 5499, 'key': 'openimagedebugger-openimagedebugger', 'kind': 'repo', 'name': 'OpenImageDebugger/OpenImageDebugger', 'one_line': 'An advanced in-memory image visualization plugin for GDB and LLDB on Linux, with experimental support for MacOS and Windows. Previously known as gdb-imagewatch. Also available as an extension for VSCode and forks', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'An advanced in-memory image visualization plugin for GDB and LLDB on Linux, with experimental support for MacOS and Windows. Previously known as gdb-imagewatch. Also available as an extension for VSCode and forks', 'url': 'https://github.com/OpenImageDebugger/OpenImageDebugger', 'source': 'github', 'source_url': 'https://github.com/OpenImageDebugger/OpenImageDebugger', 'downloads': None, 'stars': 250, 'installs': None, 'mentions': 1, 'score': 103.5, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-12', 'times_seen': 1, 'icon': 'https://github.com/OpenImageDebugger.png?size=64', 'official_desc': 'An advanced in-memory image visualization plugin for GDB and LLDB on Linux, with experimental support for MacOS and Windows. Previously known as gdb-imagewatch. Also available as an extension for VSCode and forks', 'stars_own': 250, 'stars_creator': None, 'stars_own_repo': 'OpenImageDebugger/OpenImageDebugger', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Open Image Debugger: Enabling visualization of in-memory buffers on GDB/LLDB\n\n \n \n \n\nOpen Image Debugger is a tool for visualizing in-memory buffers during debug\nsessions, compatible with both GDB and LLDB. It works out of the box with\ninstances of the OpenCV Mat class and Eigen matrices, but can also be\ncustomized to work with any arbitrary data structure.\n\n \n\n> **Prefer VS Code or a fork (Cursor, VSCodium, Windsurf, …)?** Skip the manual\n> build — install the extension from the\n> VS Code Marketplace\n> or Open VSX.\n> See Installation below.\n\n> **New — using CLion or Android Studio?** The Open Image Debugger plugin is\n> now on the\n> JetBrains Marketplace:\n> plot buffers straight from the native C/C++ debugger, inside the IDE. See\n> Installation below.\n\n> **New — declarative custom types.** You can now describe your own buffer types\n> in a .oid/types.json file instead of writing Python; the same file works in\n> gdb, lldb, and the VS Code and JetBrains extensions. See\n> doc/declarative-types.md.\n\nDownload (experimental) \nA bit experimental, better to compile manually\n\nFeatures\n\n* GUI interactivity:\n * Scroll to zoom, left click+drag to move the buffer around;\n * Rotate buffers 90° clockwise or counterclockwise;\n * Go-to widget that quickly takes you to any arbitrary pixel location;\n* Buffer values: Zoom in close enough to inspect the numerical contents of any pixel.\n* Auto update: Whenever a breakpoint is hit, the buffer view is automatically\n updated.\n* Auto contrast: The entire range of values present in the buffer can be\n automatically mapped to the visualization range [0, 1], where 0\n represents black and 1 represents white.\n* The contrast range can be manually adjusted, which is useful for inspecting\n buffers with extreme values (e.g. infinity, nan and other outliers).\n* Link views together, moving all watched buffers simultaneously when any\n single buffer is moved on the screen\n* Supported buffer types: uint8_t, int16_t, uint16_t, int32_t, uint32_t,\n float and double\n* Supported buffer channels: Up to four channels (Grayscale, two-channels, RGB\n and RGBA)\n* GPU accelerated\n* Supports large buffers whose dimensions exceed GL_MAX_TEXTURE_SIZE.\n* Supports data structures that map to a ROI of a larger buffer.\n* Exports buffers as png images (with auto contrast) or octave/matlab matrix\n files (unprocessed).\n* Auto-load buffers being visualized in the previous debug session\n* Designed to scale well for HighDPI displays\n* Works on Linux, macOS X and Windows (experimental)\n\nSupported OSes\n\n* OID is developed with Ubuntu as the main target. The goal is to support the two latest LTS versions at a given time.\n * Ubuntu is also used as a basis for the minimum versions of the dependencies: we try to support the default versions of the packages you get via apt install\n* There are currently no plans to support other Linux distros. OID may or may not compile on your favorite distro, your mileage may vary.\n* Support for MacOS and Windows are somewhat experimental now - the code should be able to compile (see ), but the binaries are not actively tested - in fact we currently have no automated tests at all for any OS - help is more than welcome in this regard. Also, we haven\'t come up with a simple installation/usage guides for these OSes yet.\n\nRequirements\n\n* A C++20 compliant compiler\n* GDB **15.0.50+** or LLDB **18.1.3+**\n* CMake **3.28.3+**\n* Python **3.12.3+** development packages\n* OpenGL **2.1+** support\n* Linux only: Wayland and X11 development packages (needed to build the bundled GLFW) and GTK 3 development packages (needed by the native file-open dialog); see the apt install command below. Alternatively, configure with -DNFD_PORTAL=ON to use the xdg-desktop-portal (D-Bus) dialog backend instead of GTK.\n\nAll other third-party libraries are bundled as git submodules and built from source, so they don\'t need to be installed:\n\n* Dear ImGui — viewer UI\n* GLFW — window and OpenGL context management\n* Eigen — linear algebra for the visualization layer\n* Asio (standalone) — IPC between the debugger bridge and the viewer\n* nlohmann/json — settings persistence\n* stb — image decoding for opening files, plus PNG export and text rendering (stb_image, stb_image_write, stb_truetype)\n* nanosvg — toolbar icon rasterization\n* nativefiledialog-extended — native OS dialog for File → Open (native builds only)\n* GoogleTest — unit tests\n\nNote: this list might get out-of-date by accident. For a more accurate list of requirements, please check what is used in and in the CI container images defined in .\n\nInstallation\n\nVS Code and forks\n\nThe quickest way to get started is the Open Image Debugger extension, available for\nVS Code and compatible forks (Cursor, VSCodium, Windsurf, and others):\n\n* VS Code Marketplace\n* Open VSX\n\nCLion and Android Studio\n\nThe Open Image Debugger plugin brings the same viewer to JetBrains IDEs with a\nnative C/C++ debugger — CLion, and Android Studio with the NDK:\n\n* JetBrains Marketplace\n\nIf you\'d rather build and integrate the desktop version manually, follow the steps below.\n\nUbuntu Linux dependencies\n\nOn Ubuntu, you can install most of the dependencies with the following command:\n\n \n\nBuilding the Open Image Debugger\n\nClone the source code to any folder you prefer and initialize the\nsubmodules:\n\n \n\nNow run the following commands to build it:\n\n \n\n**GDB integration:** Edit the file ~/.gdbinit (create it if it doesn\'t exist)\nand append the following line:\n\n \n\n**LLDB integration:** Edit the file ~/.lldbinit (create it if it doesn\'t\nexist) and append the following line:\n\n \n\nMacOS Installation\n\nAt the moment, the MacOS build is only known to work with python3 and lldb\ninstalled from Homebrew (the system-provided LLDB from the\nXcode Command Line Tools is not supported). Install them with:\n\n \n\nMake sure python3 resolves to the Homebrew one — run which python3 and\nconfirm it points under the Homebrew prefix (/opt/homebrew on Apple Silicon,\n/usr/local on Intel; brew --prefix prints it), rather than a pyenv, conda or\nsystem Python. The standard Homebrew install puts that prefix\'s bin on your\nPATH.\n\nThen debug your program using the Homebrew LLDB, for example:\n\n \n\nTesting your installation\n\nAfter compiling the plugin, you can test it by running the following command\n(use the same Python 3 interpreter CMake found when building):\n\n \n\nOn MacOS, invoke the test with the full path to the Homebrew python3, for\nexample:\n\n \n\nIf the installation was succesful, you should see the Open Image Debugger window\nwith the buffers sample_buffer_1 and sample_buffer_2.\n\nUsing plugin\n\nWhen the debugger hits a breakpoint, the Open Image Debugger window will be\nopened. You only need to type the name of the buffer to be watched in the\n"add symbols" input, and press .\n\nOpening image files directly\n\nYou can also open an image or NumPy array in the viewer without a debugger\nsession at all, either from the **File → Open** menu (shortcut Ctrl+O) or\nfrom the command line.\n\nFrom the command line, pass one or more files with the repeatable -o /\n--open flag:\n\n \n\nSupported formats:\n\n| Category | Extensions |\n| --- | --- |\n| Images (via stb_image) | png, jpg/jpeg, bmp, tga, gif, psd, hdr, ppm/pgm/pnm |\n| NumPy arrays | npy (little-endian uint8/uint16/int16/int32/float32/float64; 2-D grayscale or 3-D with 1–4 channels) |\n\nFiles opened this way are shown alongside any debugger buffers, but they are\nnever reported back to a debugger and are not saved as session state.\n\n> **Linux build note:** the native file dialog requires GTK 3\n> (libgtk-3-dev) at build time, or configure with -DNFD_PORTAL=ON to use\n> the xdg-desktop-portal (D-Bus) backend instead. MacOS and Windows need no\n> extra packages.\n\nOpen Image Debugger does not register itself as a system handler for these\nfile types; use the **File → Open** menu or the --open flag to load them.\n\nAuto-contrast and manual contrast\n\nThe (min) and (max) fields on top of the buffer view can be changed to control\nautocontrast settings. By default, O', 'readme_raw_len': 13409, 'one_line_zh': '面向 Linux 上 GDB 与 LLDB 的高级内存图像可视化插件，实验性支持 macOS 与 Windows', 'one_line_plain': None}, {'id': 3399, 'key': 'hellokaton-unsplash-mcp-server', 'kind': 'mcp', 'name': 'hellokaton/unsplash-mcp-server', 'one_line': '🐍 ☁️ - Unsplash image search', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A MCP server for Unsplash image search.', 'url': 'https://github.com/hellokaton/unsplash-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/hellokaton/unsplash-mcp-server', 'downloads': None, 'stars': 237, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/hellokaton.png?size=64', 'official_desc': '— A MCP server for Unsplash image search. ☆`237`', 'stars_own': 237, 'stars_creator': None, 'stars_own_repo': 'hellokaton/unsplash-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Unsplash MCP Server\n\nEnglish | 简体中文\n\n> A simple MCP server for seamless Unsplash image integration and search capabilities.\n\n \n \n\n \n \n\n📋 Overview\n\nUnsplash MCP Server is used for searching rich, high-quality images. It's ideal for developers who want to integrate Unsplash functionality into their own applications.\n\n✨ Features\n\n- **Advanced Image Search**: Search Unsplash's extensive photo library with filters for:\n - Keyword relevance\n - Color schemes\n - Orientation options\n - Custom sorting and pagination\n\n🔑 Obtaining Unsplash Access Key\n\nBefore installing this server, you'll need to obtain an Unsplash API Access Key:\n\n1. Create a developer account at Unsplash\n2. Register a new application\n3. Get your Access Key from the application details page\n4. Use this key in the configuration steps below\n\nFor more details, refer to the official Unsplash API documentation.\n\n🚀 Installation\n\nTo install Unsplash Image Integration Server for Claude Desktop automatically via Smithery:\n\nIDE Setup\n\n**Cursor IDE**\n\n \n\n**Windsurf**\n\n \n\n**Cline**\n\n \n\nManual Installation\n\n \n\n**Cursor Editor Integration**\n\nAdd the following configuration to your Cursor editor's settings.json:\n\n⚠️ **Note:** Please adjust the following configuration according to your actual installation:\n\n- If uv is not in your system PATH, use an absolute path (e.g., /path/to/uv)\n- ./server.py should be modified to the actual location of your server script (can use absolute path or path relative to workspace)\n\n \n\n \n\nUsing in Cursor\n\n \n\n🛠️ Available Tools\n\nSearch Photos\n\n \n\n🔄 Other Implementations\n\n- Golang: unsplash-mcp-server\n- Java: unsplash-mcp-server\n\n📄 License\n\nMIT License\n\n📬 Contact\n\n- Twitter/X\n- GitHub Issues", 'readme_raw_len': 1688, 'one_line_zh': '基于 Unsplash 的图片搜索服务', 'one_line_plain': 'Search and download Unsplash images'}, {'id': 329, 'key': 'constantineb6-comfy-pilot', 'kind': 'mcp', 'name': 'ConstantineB6/comfy-pilot', 'one_line': '🐍 🏠 - View, edit and run ComfyUI node-based image generation workflows, with an embedded terminal', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server + embedded terminal that lets Claude Code see and edit your ComfyUI workflows', 'url': 'https://github.com/ConstantineB6/comfy-pilot', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ConstantineB6/comfy-pilot', 'downloads': None, 'stars': 232, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ConstantineB6.png?size=64', 'official_desc': '🐍 🏠 - View, edit and run ComfyUI node-based image generation workflows, with an embedded terminal.', 'stars_own': 232, 'stars_creator': None, 'stars_own_repo': 'ConstantineB6/comfy-pilot', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Comfy Pilot\n\n \n \n \n\nTalk to your ComfyUI workflows. Comfy Pilot gives Claude Code direct access to see, edit, and run your workflows — with an embedded terminal right inside ComfyUI.\n\n \n\nWhy?\n\nBuilding ComfyUI workflows means manually searching for nodes, dragging connections, and tweaking values one at a time. With Comfy Pilot, you just describe what you want:\n\n- *"Build me an SDXL workflow with ControlNet"* — Claude creates all the nodes, connects them, and sets the parameters\n- *"Look at the output and increase the detail"* — Claude sees your generated image and adjusts the workflow\n- *"Download the FLUX schnell model and set up a workflow for it"* — Claude downloads the model and builds a workflow from scratch\n\nNo copy-pasting node names. No hunting through menus. Just say what you want.\n\nInstallation\n\n**CLI (Recommended):**\n \n\n**ComfyUI Manager:**\n1. Open ComfyUI\n2. Click **Manager** → **Install Custom Nodes**\n3. Search for "Comfy Pilot"\n4. Click **Install**\n5. Restart ComfyUI\n\n**Git Clone:**\n \n\nClaude Code CLI will be installed automatically if not found.\n\nRequirements\n\n- ComfyUI\n- Python 3.8+\n\nFeatures\n\n- **MCP Server** - Gives Claude Code direct access to view, edit, and run your ComfyUI workflows\n- **Embedded Terminal** - Full xterm.js terminal running Claude Code right inside ComfyUI\n- **Image Viewing** - Claude can see outputs from Preview Image and Save Image nodes\n- **Graph Editing** - Create, delete, move, and connect nodes programmatically\n\nDemo\n\nhttps://github.com/user-attachments/assets/325b1194-2334-48a1-94c3-86effd1fef02\n\nUsage\n\n1. Restart ComfyUI after installation\n2. The floating Claude Code terminal appears in the top-right corner\n3. The MCP server is automatically configured for Claude Code\n4. Ask Claude to help with your workflow:\n - "What nodes are in my current workflow?"\n - "Add a KSampler node connected to my checkpoint loader"\n - "Look at the preview image and tell me what you see"\n - "Run the workflow up to node 5"\n\nMCP Tools\n\nThe MCP server provides these tools to Claude Code:\n\n| Tool | Description |\n|------|-------------|\n| get_workflow | Get the current workflow from the browser |\n| summarize_workflow | Human-readable workflow summary |\n| get_node_types | Search available node types with filtering |\n| get_node_info | Get detailed info about a specific node type |\n| get_status | Queue status, system stats, and execution history |\n| run | Run workflow (optionally up to a specific node) or interrupt |\n| edit_graph | Batch create, delete, move, connect, and configure nodes |\n| view_image | View images from Preview Image / Save Image nodes |\n| search_custom_nodes | Search ComfyUI Manager registry for custom nodes |\n| install_custom_node | Install a custom node from the registry |\n| uninstall_custom_node | Uninstall a custom node |\n| update_custom_node | Update a custom node to latest version |\n| download_model | Download models from Hugging Face, CivitAI, or direct URLs |\n\nExample: Creating Nodes\n\n \n\nClaude will use edit_graph to:\n1. Create the KSampler node\n2. Connect the MODEL output from CheckpointLoader to KSampler\'s model input\n3. Position it appropriately in the graph\n\nExample: Viewing Images\n\n \n\nClaude will use view_image to fetch and analyze the image output.\n\nExample: Downloading Models\n\n \n\nClaude will use download_model to download from Hugging Face to your ComfyUI models folder. Supports:\n- Hugging Face (including gated models with token auth)\n- CivitAI\n- Direct download URLs\n\nTerminal Controls\n\n- **Drag** title bar to move\n- **Drag** bottom-right corner to resize\n- **−** Minimize\n- **×** Close\n- **↻** Reconnect session\n\nArchitecture\n\n \n\nFiles\n\n- __init__.py - Plugin backend: WebSocket terminal, REST endpoints\n- js/claude-code.js - Frontend: xterm.js terminal, workflow sync\n- mcp_server.py - MCP server for Claude Code integration\n- CLAUDE.md - Instructions for Claude when working with ComfyUI\n\nTroubleshooting\n\n"Command \'claude\' not found"\n\nInstall Claude Code CLI:\n\n**macOS / Linux / WSL:**\n \n\n**Windows (PowerShell):**\n \n\n**Windows (CMD):**\n \n\nMCP server not connecting\n\nThe plugin auto-configures MCP on startup. Check ComfyUI console for errors, or manually add to ~/.claude.json:\n\n \n\nTerminal disconnected\n\nClick the ↻ button to reconnect, or check ComfyUI console for errors.\n\nLicense\n\nMIT', 'readme_raw_len': 4305, 'one_line_zh': '🐍 🏠 - 查看、编辑并运行 ComfyUI 基于节点的图像生成工作流，内置终端', 'one_line_plain': 'Lets you view, edit and run ComfyUI node-based image generation workflows, with an embedded terminal'}, {'id': 4238, 'key': 'johannesbrandenburger-typst-mcp', 'kind': 'mcp', 'name': 'johannesbrandenburger/typst-mcp', 'one_line': '🐍 🏠 - Typst typesetting tools: convert between LaTeX and Typst, validate Typst syntax and generate images…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Typst MCP Server is an MCP (Model Context Protocol) implementation that helps AI models interact with Typst, a markup-based typesetting system. The server provides tools for converting between LaTeX and Typst, validating Typst syntax, and generating images from Typst code.', 'url': 'https://github.com/johannesbrandenburger/typst-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/johannesbrandenburger/typst-mcp', 'downloads': None, 'stars': 184, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/johannesbrandenburger.png?size=64', 'official_desc': '🐍 🏠 - Typst typesetting tools: convert between LaTeX and Typst, validate Typst syntax and generate images from Typst code.', 'stars_own': 184, 'stars_creator': None, 'stars_own_repo': 'johannesbrandenburger/typst-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Typst MCP Server\n\nTypst MCP Server is an MCP (Model Context Protocol) implementation that helps AI models interact with Typst, a markup-based typesetting system. The server provides tools for converting between LaTeX and Typst, validating Typst syntax, and generating images from Typst code.\n\nAvailable Tools\n\n>⚠️ Currently all the functionality is implemented as tools, because Cursor and VS Code are not able to handle the other primitives yet.\n\nThe server provides the following tools:\n\n1. **list_docs_chapters()**: Lists all chapters in the Typst documentation.\n - Lets the LLM get an overview of the documentation and select a chapter to read.\n - The LLM should select the relevant chapter to read based on the task at hand.\n\n2. **get_docs_chapter(route)**: Retrieves a specific chapter from the Typst documentation.\n - Based on the chapter selected by the LLM, this tool retrieves the content of the chapter.\n - Also available as get_docs_chapters(routes: list) for retrieving multiple chapters at once.\n\n3. **latex_snippet_to_typst(latex_snippet)**: Converts LaTeX code to Typst using Pandoc.\n - LLMs are better at writing LaTeX than Typst, so this tool helps convert LaTeX code to Typst.\n - Also available as latex_snippets_to_typst(latex_snippets: list) for converting multiple LaTeX snippets at once.\n\n4. **check_if_snippet_is_valid_typst_syntax(typst_snippet)**: Validates Typst code.\n - Before sending Typst code to the user, the LLM should check if the code is valid.\n - Also available as check_if_snippets_are_valid_typst_syntax(typst_snippets: list) for validating multiple Typst snippets at once.\n\n5. **typst_to_image(typst_snippet)**: Renders Typst code to a PNG image.\n - Before sending complex Typst illustrations to the user, the LLM should render the code to an image and check if it looks correct.\n - Only relevant for multi modal models.\n\nGetting Started\n\n- Clone this repository\n - git clone https://github.com/johannesbrandenburger/typst-mcp.git\n- Clone the typst repository\n - git clone https://github.com/typst/typst.git\n- Run the docs generation in the typst repository\n - cargo run --package typst-docs -- --assets-dir ../typst-mcp/typst-docs --out-file ../typst-mcp/typst-docs/main.json\n - Make sure to adjust the path to your local clone of the typst-mcp repository\n - This will generate the main.json and the assets in the typst-docs folder\n- Install required dependencies: uv sync (install uv if not already installed)\n \n- Install Typst\n\nRunning the Server\n\nLocal Installation\n\nExecute the server script:\n\n \n\nOr install it in Claude Desktop with MCP:\n\n \n\nOr use the new agent mode in VS Code:\n\nAgent mode: available to all users and supports MCP\n\nDocker\n\nFor Docker installation and usage, see DOCKER.md for detailed build information and instructions.\n\nPlatform Configuration\n\nThis MCP server can be integrated with various AI coding platforms. Below are configuration instructions for popular platforms:\n\nClaude Desktop\n\nAdd the following to your Claude Desktop configuration file (claude_desktop_config.json):\n\n**macOS**: ~/Library/Application Support/Claude/claude_desktop_config.json \n**Windows**: %APPDATA%\\Claude\\claude_desktop_config.json\n\n \n\nOr for local installation:\n\n \n\nCursor\n\nCreate .cursor/mcp.json in your project root for project-specific configuration:\n\n \n\nOr create ~/.cursor/mcp.json for global configuration. For local installation:\n\n \n\nRoo Code\n\nCreate .roo/mcp.json in your project root for project-specific configuration:\n\n \n\nOr edit global settings via Roo Code MCP settings view. For local installation:\n\n \n\nOpenCode\n\nCreate or edit the opencode.json file in your project root:\n\n \n\nFor local installation:\n\n \n\nClaude Code\n\nConfigure MCP servers in your Claude Code settings file (~/.claude/settings.json):\n\n \n\nOr for local installation:\n\n \n\nVS Code with Agent Mode\n\nVS Code's Agent Mode has native MCP support (no extension required):\n\n1. Enable Agent Mode in VS Code settings (chat.agent.enabled)\n2. Create .vscode/mcp.json in your workspace:\n\n \n\nFor global configuration, add to your user profile via **MCP: Open User Configuration** command.\n\nJSON Schema of the Typst Documentation\n\n>⚠️ The schema of the typst documentation is not stable and may change at any time. The schema is generated from the typst source code and is not guaranteed to be complete or correct. If the schema changes, this repository will need to be updated accordingly, so that the docs functionality works again.", 'readme_raw_len': 4448, 'one_line_zh': 'Typst 排版工具：在 LaTeX 与 Typst 之间转换、校验 Typst 语法并生成图片', 'one_line_plain': 'Typst tools to convert, validate syntax and generate images from LaTeX'}, {'id': 1411, 'key': 'docker-hub-mcp', 'kind': 'mcp', 'name': 'docker/hub-mcp', 'one_line': '🎖️ 📇 ☁️ 🏠 - Interact with Docker Hub: repositories, search and Docker Hardened Images', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Docker Hub MCP Server', 'url': 'https://github.com/docker/hub-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/docker/hub-mcp', 'downloads': None, 'stars': 169, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/docker.png?size=64', 'official_desc': '🎖️ 📇 ☁️ 🏠 - Interact with Docker Hub: repositories, search and Docker Hardened Images.', 'stars_own': 169, 'stars_creator': None, 'stars_own_repo': 'docker/hub-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Docker Hub MCP Server\n\n \n\nThe Docker Hub MCP Server is a Model Context Protocol (MCP) server that interfaces with Docker Hub APIs to make them accessible to LLMs, enabling intelligent content discovery and repository management.\n\nDevelopers building with containers, especially in AI and LLM-powered workflows, often face inadequate context across the vast landscape of Docker Hub images. As a result, LLMs struggle to recommend the right images, and developers lose time manually searching instead of building.\n\n \n \n \n\nUse Cases\n\n- AI-powered image recommendations - LLMs access real-time Docker Hub data for accurate container image suggestions.\n- Enhanced content discovery - AI tools help developers find the right images faster.\n- Simplified Hub workflows - Manage Docker repositories and images using natural language.\n\nPrerequisites\n\n- Docker installed\n- Node.js (version 22+)\n- [Optional] A Docker Personal Access Token (PAT) with appropriate permissions\n\nSetup\n\n1. **Build**\n\n \n\n2. **Run**\n\n \n\n- Default args:\n - transport: Choose between http or stdio (default: stdio)\n - port=3000\n - host=127.0.0.1 (HTTP transport only)\n This starts the server with default settings and can only access public Docker Hub content.\n\n> [!IMPORTANT]\n> The http transport binds to 127.0.0.1 (loopback) by default and **requires\n> authentication**. See Securing the HTTP transport\n> before exposing it to a network.\n\nRun in inspector [Optional]\n\nThe MCP Inspector provides a web interface to test your server:\n\n \n\nSecuring the HTTP transport\n\nThe stdio transport is only reachable by the local process that spawns it. The\nhttp transport, however, dispatches every tool call using the server operator's\nDocker Hub Personal Access Token (HUB_PAT_TOKEN). Anyone who can reach the HTTP\nendpoint can therefore act as that Docker Hub identity — including creating and\nmodifying repositories. To prevent this, the HTTP transport is locked down by\ndefault:\n\n- **Loopback binding.** The listener binds to 127.0.0.1 unless you pass\n --host= (for example --host=0.0.0.0 to expose it from a container).\n- **Authentication required (fail-closed).** In http mode the server refuses to\n start unless you either provide a bearer token or explicitly opt out. Set the\n token via the MCP_AUTH_TOKEN environment variable; clients must then send it as\n Authorization: Bearer on every request.\n- **DNS-rebinding / CSRF protection.** Requests are rejected when the Host header\n is not in the allow-list (loopback plus --host, extendable with\n --allowed-hosts), or when they carry a browser Origin header that is not\n listed in --allowed-origins. Non-browser MCP clients are unaffected.\n\nRun the HTTP transport with authentication:\n\n \n\nExpose it beyond loopback (e.g. inside a container), still authenticated:\n\n \n\n| Flag / env var | Purpose |\n| ------------------------- | --------------------------------------------------------------- |\n| MCP_AUTH_TOKEN | Bearer token required on every /mcp request. |\n| --host= | Address to bind (default 127.0.0.1). |\n| --allowed-hosts=a,b | Extra Host header values to accept (comma-separated). |\n| --allowed-origins=a,b | Browser Origin values to accept (comma-separated). |\n| --allow-unauthenticated | Serve /mcp with **no** authentication. Insecure; opt-in only. |\n\n> [!WARNING]\n> --allow-unauthenticated disables authentication entirely and exposes your\n> Docker Hub PAT to any client that can reach the port. Only use it on a trusted,\n> isolated network.\n\nAuthenticate with docker\n\nBy default this MCP server can only query public content on Docker Hub. In order to manage your repositories you need to provide authentication.\n\nRun with authentication\n\n \n\nRun in inspector [Optional]\n\n \n\nUsage in Docker Ask Gordon\n\nYou can configure Gordon to be a host that can interact with the Docker Hub MCP server.\n\nGordon Setup\n\nAsk Gordon is your personal AI assistant embedded in Docker Desktop and the Docker CLI. It's designed to streamline your workflow and help you make the most of the Docker ecosystem.\n\nYou can configure Gordon to be a client that can interact with the Docker Hub MCP server.\n\n1. Create the gordon-mcp.yml file file in your working directory.\n2. Replace environment variables in the gordon-mcp.yml with your Docker Hub username and a PAT token.\n\n \n\n2. Run docker build -t hub .\n3. Run docker ai\n\nUsage in other MCP Clients\n\nUsage with Claude Desktop\n\n> NOTE: Make sure you have already built the application as mentioned in Step 1.\n\n1. Add the Docker Hub MCP Server configuration to your claude_desktop_config.json:\n\n> NOTE: if you are using nvm to manage node versions, you should put the node binary path in the command property. This ensure MCP server runs with the right node version. You can find your binary path by running which node in your shell\n\nFor public repositories only:\n\n- /FULL/PATH/TO/YOUR/docker-hub-mcp-server - The complete path to where you cloned this repository\n\n \n\nFor authenticated access (recommended):\n\nReplace the following values:\n\n- YOUR_DOCKER_HUB_USERNAME - Your Docker Hub username\n- YOUR_DOCKER_HUB_PERSONAL_ACCESS_TOKEN - Your Docker Hub Personal Access Token\n- /FULL/PATH/TO/YOUR/docker-hub-mcp-server - The complete path to where you cloned this\n\n \n\n2. Save the configuration file and completely restart Claude Desktop for the changes to take effect.\n\nUsage with VS Code\n\n1. Add the Docker Hub MCP Server configuration to your User Settings (JSON) file in VS Code. You can do this by opening the Command Palette and typing Preferences: Open User Settings (JSON).\n\nFor public repositories only:\n\n- /FULL/PATH/TO/YOUR/docker-hub-mcp-server - The complete path to where you cloned this repository\n\n \n\nFor authenticated access (recommended):\n\nReplace the following values:\n\n- YOUR_DOCKER_HUB_USERNAME - Your Docker Hub username\n- YOUR_DOCKER_HUB_PERSONAL_ACCESS_TOKEN - Your Docker Hub Personal Access Token\n- /FULL/PATH/TO/YOUR/docker-hub-mcp-server - The complete path to where you cloned this\n\n \n\n2. Open the Command Palette and type MCP: List Servers.\n3. Select docker-hub and select Start Server.\n\nTask Examples\n\nFinding images\n\n \n\nRepository Management\n\n \n\nPull/Push Images\n\n \n\nTag Management\n\n \n\nDocker Hardened Images\n\n \n\nTools\n\nSearch\n\n- **search** - Search repositories and content using Search V4 API\n - query: Search query parameter (string, required)\n - architectures: Filter on architectures (string, optional)\n - badges: Filter by image content type badges (string, optional)\n - categories: Filter on categories (string, optional)\n - extension_reviewed: Filter on reviewed extensions (boolean, optional)\n - from: Number of documents to skip for pagination (number, optional)\n - images: Filter on image names (string, optional)\n - operating_systems: Filter on operating systems (string, optional)\n - order: Change the ordering of results (string, optional)\n - size: Maximum number of results to return (number, optional)\n - sort: Sort results by search field (string, optional)\n - type: Filter on repository content type (string, optional)\n\nNamespace Management\n\n- **get_namespaces** - Get list of namespaces the user is a member of\n - page: Page number for pagination (string, optional)\n - page_size: Number of items per page (string, optional)\n\nRepository Management\n\n- **list_repositories_by_namespace** - List all repositories under the provided namespace\n - namespace: Repository namespace (string, required)\n - content_types: Comma-delimited list of content types (string, optional)\n - media_types: Comma-delimited list of media types (string, optional)\n - name: Search by repository name (string, optional)\n - ordering: Sort order (string, optional)\n - page: Page number (number, optional)\n - page_size: Number of items per page (number, optional)\n\n- **get_repository_info** - Get information about a repository\n - namespace: Repository namespace (string, required)\n - repository: Repository name (string, required)\n\n- **check_repository** - Check if a repository exists\n - namespace: Repository namespace (string, requi", 'readme_raw_len': 9582, 'one_line_zh': '与 Docker Hub 交互：仓库、搜索及 Docker Hardened Images', 'one_line_plain': 'The tool lets users interact with Docker Hub: searching repositories and accessing Docker Hardened Images'}, {'id': 3448, 'key': 'mikechao-brave-search-mcp', 'kind': 'mcp', 'name': 'mikechao/brave-search-mcp', 'one_line': "📇 ☁️ - Web, image, news, video and local point-of-interest search using Brave's Search API", 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'An MCP Server implementation that integrates the Brave Search API, providing, Web Search, Local Points of Interest Search, Image Search, Video Search, News Search and LLM Context Search capabilities', 'url': 'https://github.com/mikechao/brave-search-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/mikechao/brave-search-mcp', 'downloads': None, 'stars': 130, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/mikechao.png?size=64', 'official_desc': "📇 ☁️ - Web, image, news, video and local point-of-interest search using Brave's Search API.", 'stars_own': 130, 'stars_creator': None, 'stars_own_repo': 'mikechao/brave-search-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Brave Search MCP Server\n\nAn MCP Server implementation that integrates the Brave Search API, providing, Web Search, Local Points of Interest Search, Video Search, Image Search, News Search and LLM Context Search capabilities\n\n \n \n \n\nFeatures\n\n- **Web Search**: Perform a regular search on the web\n- **Image Search**: Search the web for images.\n- **News Search**: Search the web for news\n- **Video Search**: Search the web for videos\n- **Local Points of Interest Search**: Search for local physical locations, businesses, restaurants, services, etc\n- **LLM Context Search**: Fetch and extract full web page content optimized for reading and synthesizing sources\n\nTools\n\n- **brave_web_search**\n - Execute web searches using Brave's API\n - Inputs:\n - query (string): The term to search the internet for\n - count (number, optional): The number of results to return (max 20, default 10)\n - offset (number, optional, default 0): The offset for pagination\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_image_search**\n - Get images from the web relevant to the query\n - Inputs:\n - query (string): The term to search the internet for images of\n - count (number, optional): The number of images to return (max 50, default 10)\n\n- **brave_news_search**\n - Searches the web for news\n - Inputs:\n - query (string): The term to search the internet for news articles, trending topics, or recent events\n - count (number, optional): The number of results to return (max 20, default 10)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_local_search**\n - Search for local businesses, services and points of interest\n - Falls back to brave_web_search on the initial page if no location results are found\n - Inputs:\n - query (string): Local search term\n - count (number, optional): The number of results to return (max 20, default 5)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n\n- **brave_video_search**\n - Search the web for videos\n - Inputs:\n - query: (string): The term to search for videos\n - count: (number, optional): The number of videos to return (max 20, default 10)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_llm_context_search**\n - Pre-extracted web content optimized for AI agents, LLM grounding, and RAG pipelines.\n - Uses Brave's balanced context threshold mode in compact mode and disables Brave relevance filtering in full mode.\n - Inputs:\n - query (string): The search query. Maximum 400 characters and 50 words.\n - url (string, optional): Optional URL to target. When provided, query and URL are combined for retrieval and only snippets from this exact URL are returned.\n - count (number, optional, default 8): The maximum number of search results considered. Minimum 1, maximum 50.\n - maximumNumberOfUrls (number, optional, default 8): The maximum number of URLs to include in the response. Minimum 1, maximum 50.\n - maximumNumberOfTokens (number, optional, default 2048): The approximate maximum number of tokens in the returned context. Minimum 1024, maximum 32768.\n - maximumNumberOfSnippets (number, optional, default 16): The maximum number of snippets across all URLs. Minimum 1, maximum 100.\n - maximumNumberOfTokensPerUrl (number, optional, default 512): The maximum number of tokens per URL. Minimum 512, maximum 8192.\n - maximumNumberOfSnippetsPerUrl (number, optional, default 2): The maximum number of snippets per URL. Minimum 1, maximum 100.\n - responseMode (enum, optional, default compact): compact applies Brave's balanced relevance filtering plus local snippet filtering/truncation. full disables Brave's relevance filtering and returns raw snippets without local filtering or truncation.\n - maxSnippetChars (number, optional, default 400): Maximum characters per snippet in compact mode. Minimum 80, maximum 4000.\n - maxOutputChars (number, optional, default 8000): Approximate maximum serialized response size in compact mode. Minimum 1000, maximum 100000.\n\nOpenAI Apps & MCP Apps Support\n\n \n \n \n \n \n ▶️ Click to watch the demo video \n \n\nThere is now support for OpenAI Apps and MCP Apps in this MCP Server. When UI mode is enabled for each tool there is a corresponding UI widget that let's you control what gets added to the model's context. See the directions in usage with ChatGPT section.\n\nConfiguration\n\nGetting an API Key\n\n1. Sign up for a Brave Search API account\n2. Choose a plan\n3. Generate your API key from the developer dashboard\n\nRuntime modes\n\nBy default the MCP server runs in stdio mode.\n\n \n\nTo enable Streamable HTTP mode:\n\n \n\nBy default the server listens on port 3001.\nThe URL is:\n\n \n\nThere are two configuration modes:\n\n- Env mode: BRAVE_MCP_CONFIG is unset. Feature toggles come from environment variables exactly as in previous releases.\n- File mode: BRAVE_MCP_CONFIG=/path/to/config.toml is set. The TOML file becomes the single source of truth for feature settings, and overlapping feature env vars are ignored with warnings.\n\nEnvironment-only settings\n\nThese settings are always read from the process environment, regardless of mode:\n\n- BRAVE_API_KEY (required): Brave Search API key.\n- PORT (optional): HTTP port (default: 3001).\n- HOST (optional): Interface to bind to (default: 0.0.0.0).\n- BRAVE_MCP_CONFIG (optional): Absolute or relative path to a TOML config file for feature settings.\n\nEnv mode feature settings\n\nWhen BRAVE_MCP_CONFIG is not set, these feature env vars are supported:\n\n- ALLOWED_HOSTS (HTTP mode only): Comma-separated list of allowed hostnames for Host header validation.\n - Example: ALLOWED_HOSTS=localhost,127.0.0.1,my-app.ngrok-free.app\n - Use hostnames only (no scheme/path), e.g. my-app.ngrok-free.app not https://my-app.ngrok-free.app/mcp\n- BRAVE_MCP_POLICY_FILE: JSON policy file path.\n- BRAVE_MCP_POLICY_REDACT: true to redact matched text instead of blocking it.\n- BRAVE_MCP_REQUEST_LIMIT: Positive integer request cap.\n- BRAVE_MCP_WINDOW_SECONDS: Non-negative integer rolling window size.\n- BRAVE_MCP_COOLDOWN_SECONDS: Non-negative integer cooldown after the limit is exceeded.\n- BRAVE_MCP_AUDIT_LOG: true to emit audit logs.\n- BRAVE_MCP_AUDIT_LOG_RAW: true to include raw query text in audit logs.\n- BRAVE_MCP_REQUIRE_JUSTIFICATION: true to reject tool calls without a justification string.\n\nExamples:\n\n \n\n \n\nFile mode (BRAVE_MCP_CONFIG)\n\nWhen BRAVE_MCP_CONFIG is set, the file controls feature configuration, including the HTTP host allowlist.\n\n \n\nNotes:\n\n- BRAVE_API_KEY, PORT, and HOST remain environment-only even in file mode.\n- If you set overlapping feature env vars such as BRAVE_MCP_REQUEST_LIMIT or ALLOWED_HOSTS alongside BRAVE_MCP_CONFIG, startup warns that they are being ignored.\n- Unknown TOML keys also emit warnings so typos like [guardrails] are visible before you debug runtime behavior.\n\nTo validate a config file without starting the server, use the packaged entrypoint after building t", 'readme_raw_len': 10161, 'one_line_zh': '通过 Brave 搜索 API 进行网页、图片、新闻、视频及本地兴趣点搜索', 'one_line_plain': 'Brave Search API lets users search the web, images, news, videos, and local points of interest'}, {'id': 2589, 'key': 'deusxmachina-dev-memorylane', 'kind': 'mcp', 'name': 'deusXmachina-dev/memorylane', 'one_line': '📇 ☁️ 🏠 🍎 - Desktop app that captures screen activity, stores AI summaries and OCR text locally, and exposes…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/deusXmachina-dev/memorylane', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/deusXmachina-dev/memorylane', 'downloads': None, 'stars': 120, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/deusXmachina-dev.png?size=64', 'official_desc': '📇 ☁️ 🏠 🍎 - Desktop app that captures screen activity, stores AI summaries and OCR text locally, and exposes the history via semantic search and a timeline.', 'stars_own': 120, 'stars_creator': None, 'stars_own_repo': 'deusXmachina-dev/memorylane', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'MemoryLane\n\n \n\nApp Download\n\n \n\n \n\nWhat is MemoryLane?\n\nMemoryLane is a tray app for macOS and Windows that captures your screen as you work, summarizes each capture into text through your model endpoint, then deletes the screenshot. Only the summaries, OCR text, and embeddings are kept — in a local SQLite database with full-text and vector search. From there it detects recurring workflows, surfaces automation opportunities, and makes everything queryable from any AI chat over MCP.\n\n**Capture → summarize → store locally → detect patterns → query from any AI chat via MCP**\n\n \n \n \n\n🎬 Demo\n\nUse your own models\n\nBy default, captures are summarized through a managed OpenRouter endpoint. You can instead point MemoryLane at your own LLM — a local model via Ollama, vLLM, or LM Studio, or your own OpenRouter account — all from Settings, no rebuild. With a local endpoint, captures never leave your machine. See Using Your Own Models.\n\nPrivacy & Permissions\n\nSee Privacy & Permissions.\n\nCLI\n\nInstall the CLI from npm: @deusxmachina-dev/memorylane-cli\n\nCommunity\n\nQuestions, feedback, and feature ideas are welcome in our Discord server.\n\nJoin the MemoryLane Discord', 'readme_raw_len': 1167, 'one_line_zh': '记录屏幕活动的桌面应用，把 AI 摘要与 OCR 文本存在本地，并通过 MCP 暴露出来。', 'one_line_plain': 'Desktop app that captures screen activity, stores AI summaries and OCR text locally, and exposes them for use'}, {'id': 257, 'key': 'surescaleai-openai-gpt-image-mcp', 'kind': 'mcp', 'name': 'SureScaleAI/openai-gpt-image-mcp', 'one_line': "📇 ☁️ - Image generation and editing with OpenAI's GPT image models", 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/SureScaleAI/openai-gpt-image-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/SureScaleAI/openai-gpt-image-mcp', 'downloads': None, 'stars': 115, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/SureScaleAI.png?size=64', 'official_desc': "A Model Context Protocol (MCP) tool server for OpenAI's GPT-4o/gpt-image-1 image generation and editing APIs.", 'stars_own': 115, 'stars_creator': None, 'stars_own_repo': 'SureScaleAI/openai-gpt-image-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "openai-gpt-image-mcp\n\n \n \n \n \n \n \n \n\n---\n\nA Model Context Protocol (MCP) tool server for OpenAI's GPT-4o/gpt-image-1 image generation and editing APIs.\n\n- **Generate images** from text prompts using OpenAI's latest models.\n- **Edit images** (inpainting, outpainting, compositing) with advanced prompt control.\n- **Supports**: Claude Desktop, Cursor, VSCode, Windsurf, and any MCP-compatible client.\n\n---\n\n✨ Features\n\n- **create-image**: Generate images from a prompt, with advanced options (size, quality, background, etc).\n- **edit-image**: Edit or extend images using a prompt and optional mask, supporting both file paths and base64 input.\n- **File output**: Save generated images directly to disk, or receive as base64.\n\n---\n\n🚀 Installation\n\n \n\n---\n\n🔑 Configuration\n\nAdd to Claude Desktop or VSCode (including Cursor/Windsurf) config:\n\n \n\nAlso supports Azure deployments:\n\n \n\nAlso supports supplying an environment files:\n\n \n\n---\n\n⚡ Advanced\n\n- For create-image, set n to generate up to 10 images at once.\n- For edit-image, provide a mask image (file path or base64) to control where edits are applied.\n- Provide an environment file with --env-file path/to/file/.env\n- See src/index.ts for all options.\n\n---\n\n🧑\u200d💻 Development\n\n- TypeScript source: src/index.ts\n- Build: yarn build\n- Run: node dist/index.js\n\n---\n\n📝 License\n\nMIT\n\n---\n\n🩺 Troubleshooting\n\n- Make sure your OPENAI_API_KEY is valid and has image API access.\n- You must have a verified OpenAI organization. After verifying, it can take 15–20 minutes for image API access to activate.\n- File paths must be absolute.\n - **Unix/macOS/Linux**: Starting with / (e.g., /path/to/image.png)\n - **Windows**: Drive letter followed by : (e.g., C:/path/to/image.png or C:\\path\\to\\image.png)\n- For file output, ensure the directory is writable.\n- If you see errors about file types, check your image file extensions and formats.\n\n---\n\n⚠️ Limitations & Large File Handling\n\n- **1MB Payload Limit:** MCP clients (including Claude Desktop) have a hard 1MB limit for tool responses. Large images (especially high-res or multiple images) can easily exceed this limit if returned as base64.\n- **Auto-Switch to File Output:** If the total image size exceeds 1MB, the tool will automatically save images to disk and return the file path(s) instead of base64. This ensures compatibility and prevents errors like result exceeds maximum length of 1048576.\n- **Default File Location:** If you do not specify a file_output path, images will be saved to /tmp (or the directory set by the MCP_HF_WORK_DIR environment variable) with a unique filename.\n- **Environment Variable:**\n - MCP_HF_WORK_DIR: Set this to control where large images and file outputs are saved. Example: export MCP_HF_WORK_DIR=/your/desired/dir\n- **Best Practice:** For large or production images, always use file output and ensure your client is configured to handle file paths.\n\n---\n\n📚 References\n\n- OpenAI Images API Documentation\n\n---\n\n🙏 Credits\n\n- Built with @modelcontextprotocol/sdk\n- Uses openai Node.js SDK \n- Built by SureScale.ai\n- Contributions from Axle Research and Technology", 'readme_raw_len': 3097, 'one_line_zh': '📇 ☁️ - 使用 OpenAI 的 GPT 图像模型进行图像生成与编辑', 'one_line_plain': "Image generation and editing with OpenAI's GPT image models"}, {'id': 3416, 'key': 'just-every-mcp-screenshot-website-fast', 'kind': 'mcp', 'name': 'just-every/mcp-screenshot-website-fast', 'one_line': '📇 🏠 - Capture website screenshots, tiling full pages into 1072x1072 chunks for Claude Vision, with…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Quickly screenshots webpages and converts to an LLM friendly size', 'url': 'https://github.com/just-every/mcp-screenshot-website-fast', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/just-every/mcp-screenshot-website-fast', 'downloads': None, 'stars': 110, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/just-every.png?size=64', 'official_desc': '📇 🏠 - Capture website screenshots, tiling full pages into 1072x1072 chunks for Claude Vision, with configurable viewports and wait strategies for dynamic content.', 'stars_own': 110, 'stars_creator': None, 'stars_own_repo': 'just-every/mcp-screenshot-website-fast', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '@just-every/mcp-screenshot-website-fast\n\nFast, efficient screenshot capture of web pages - optimized for CLI coding tools. Automatically tiles full pages into 1072x1072 chunks for optimal processing.\n\n \n \n \n\n \n \n\nOverview\n\nBuilt specifically for AI vision workflows, this tool captures high-quality screenshots with automatic resolution limiting and tiling for optimal processing by Claude Vision API and other AI models. It ensures screenshots are perfectly sized at 1072x1072 pixels (1.15 megapixels) for maximum compatibility.\n\nFeatures\n\n- 📸 **Fast screenshot capture** using Puppeteer headless browser\n- 🎯 **Claude Vision optimized** with automatic resolution limiting (1072x1072 for optimal 1.15 megapixels)\n- 🔲 **Automatic tiling** - Full pages are automatically split into 1072x1072 tiles\n- 🎬 **Screencast capture** - Record series of screenshots over time with configurable intervals\n- 🔄 **Always fresh content** - No caching ensures up-to-date screenshots\n- 📱 **Configurable viewports** for responsive testing\n- ⏱️ **Wait strategies** for dynamic content (networkidle, custom delays)\n- 📄 **Full page capture** by default for complete page screenshots\n- 🎥 **Animated WebP export** - Save screencasts as high-quality animated WebP files\n- 💉 **JavaScript injection** - Execute custom JS before screencast capture\n- 📦 **Minimal dependencies** for fast npm installs\n- 🔌 **MCP integration** for seamless AI workflows\n- 🪟 **Windows-compatible launcher** for npm-installed MCP usage\n- 🔋 **Resource efficient** - Automatic browser cleanup after 60 seconds of inactivity\n- 🧹 **Memory management** - Pages are closed after each screenshot to prevent leaks\n\nInstallation\n\nClaude Code\n\n \n\nVS Code\n\n \n\nCursor\n\n \n\nJetBrains IDEs\n\nSettings → Tools → AI Assistant → Model Context Protocol (MCP) → Add\n\nChoose "As JSON" and paste:\n\n \n\nRaw JSON (works in any MCP client)\n\n \n\nDrop this into your client\'s mcp.json (e.g. .vscode/mcp.json, ~/.cursor/mcp.json, or .mcp.json for Claude).\n\nPrerequisites\n\n- Node.js 20.x or higher\n- npm or npx\n- Chrome/Chromium (automatically downloaded by Puppeteer)\n\nQuick Start\n\nMCP Server Usage\n\nOnce installed in your IDE, the following tools are available:\n\nAvailable Tools\n\n- take_screenshot - Captures a high-quality screenshot of a webpage\n - Parameters:\n - url (required): The HTTP/HTTPS URL to capture\n - width (optional): Viewport width in pixels (max 1072, default: 1072)\n - height (optional): Viewport height in pixels (max 1072, default: 1072)\n - fullPage (optional): Capture full page screenshot with tiling (default: true)\n - waitUntil (optional): Wait until event: load, domcontentloaded, networkidle0, networkidle2 (default: domcontentloaded)\n - waitFor (optional): Additional wait time in milliseconds\n - directory (optional): Directory to save screenshots - returns file paths instead of base64 images\n\n- capture_selector - Captures a screenshot of a specific DOM element matched by a CSS selector\n - Parameters:\n - url (required): The HTTP/HTTPS URL to capture\n - selector (required): CSS selector for the element to capture\n - width (optional): Viewport width in pixels (max 1072, default: 1072)\n - height (optional): Viewport height in pixels (max 1072, default: 1072)\n - waitUntil (optional): Wait until event: load, domcontentloaded, networkidle0, networkidle2 (default: domcontentloaded)\n - waitForMS (optional): Additional wait time in milliseconds\n - selectorTimeoutMS (optional): How long to wait for the selector to appear before failing (default: 5000)\n\nUsage Examples\n\n**Default usage (returns base64 images):**\n \n\n**Save to directory (returns file paths):**\n \n\n**Capture a specific element:**\n \n\nWhen using the directory parameter:\n- Screenshots are saved as PNG files with timestamps\n- File paths are returned instead of base64 data\n- For tiled screenshots, each tile is saved as a separate file\n- Directory is created automatically if it doesn\'t exist\n\ntake_screencast\n\nCaptures a series of screenshots over time to create a screencast. Only captures the top tile (1072x1072) of the viewport.\n\nParameters\n- url (required): The URL to capture\n- duration (optional): Total duration in seconds (default: 10)\n- interval (optional): Interval between screenshots in seconds (default: 2)\n- jsEvaluate (optional): JavaScript code to execute at the start\n- waitUntil (optional): Wait strategy: \'load\', \'domcontentloaded\', \'networkidle0\', \'networkidle2\'\n- waitForMS (optional): Additional wait time before starting\n- directory (optional): Save as animated WebP to directory (captures every 1 second)\n\nUsage Examples\n\n**Basic screencast (5 frames over 10 seconds):**\n \n\n**Custom timing:**\n \n\n**With JavaScript execution:**\n \n\n**Save as animated WebP:**\n \n\nWhen using the directory parameter:\n- An animated WebP is created with 1-second intervals\n- Individual frames are also saved as PNG files\n- The animation loops forever by default\n- WebP provides excellent quality:\n - Full color support (no 256 color limitation)\n - Efficient compression for web animations\n - Perfect for gradient backgrounds and smooth animations\n - Smaller file sizes compared to GIF with better quality\n\nDevelopment Usage\n\nInstall\n\n \n\nCapture screenshot\n \n\nCLI Options\n\n- -w, --width - Viewport width (max 1072, default: 1072)\n- -h, --height - Viewport height (max 1072, default: 1072)\n- --no-full-page - Disable full page capture and tiling\n- --wait-until - Wait until event: load, domcontentloaded, networkidle0, networkidle2\n- --wait-for - Additional wait time in milliseconds\n- -o, --output - Output file path (required for tiled output)\n\nAuto-Restart Feature\n\nThe MCP server includes automatic restart capability by default for improved reliability:\n\n- Automatically restarts the server if it crashes\n- Handles unhandled exceptions and promise rejections\n- Implements exponential backoff (max 10 attempts in 1 minute)\n- Logs all restart attempts for monitoring\n- Gracefully handles shutdown signals (SIGINT, SIGTERM)\n\nFor development/debugging without auto-restart:\n \n\nArchitecture\n\n \n\nDevelopment\n\n \n\nWhy This Tool?\n\nBuilt specifically for AI vision workflows:\n\n1. **Optimized for Claude Vision API** - Automatic resolution limiting to 1072x1072 pixels (1.15 megapixels)\n2. **Automatic tiling** - Full pages split into perfect chunks for AI processing\n3. **Always fresh** - No caching ensures you get the latest content\n4. **MCP native** - First-class integration with AI development tools\n5. **Simple API** - Clean, straightforward interface for capturing screenshots\n\nContributing\n\nContributions are welcome! Please:\n\n1. Fork the repository\n2. Create a feature branch\n3. Add tests for new functionality\n4. Submit a pull request\n\nTroubleshooting\n\nPuppeteer Issues\n- Ensure Chrome/Chromium can be downloaded\n- Check firewall settings\n- Try setting PUPPETEER_SKIP_CHROMIUM_DOWNLOAD=true and provide custom executable\n\nScreenshot Quality\n- Adjust viewport dimensions\n- Use appropriate wait strategies\n- Check if site requires authentication\n\nTimeout Errors\n- Increase wait time with --wait-for flag\n- Use different --wait-until strategies\n- Check if site is accessible\n\nLicense\n\nMIT', 'readme_raw_len': 7103, 'one_line_zh': '截取网站截图，将整页切成 1072x1072 分块供 Claude Vision 使用', 'one_line_plain': 'Website screenshot tool lets you capture full pages tiled into 1072x1072 chunks for Claude Vision'}, {'id': 4182, 'key': 'awkoy-replicate-flux-mcp', 'kind': 'mcp', 'name': 'awkoy/replicate-flux-mcp', 'one_line': '📇 ☁️ - Generate images via the Replicate API', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP for Replicate Flux Model - A powerful tool for generating customized images and SVG assets that match specific coding vibes and aesthetic styles. Streamline your visual asset creation process with AI-powered design generation tailored for developers.', 'url': 'https://github.com/awkoy/replicate-flux-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/awkoy/replicate-flux-mcp', 'downloads': None, 'stars': 105, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/awkoy.png?size=64', 'official_desc': '— MCP server for Replicate Flux image and SVG generation ☆`105`', 'stars_own': 105, 'stars_creator': None, 'stars_own_repo': 'awkoy/replicate-flux-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Replicate Flux MCP\n\nEnglish | 中文\n\n \n \n \n \n\n \n \n \n \n\n \n \n \n\n**Replicate Flux MCP** is an advanced Model Context Protocol (MCP) server that empowers AI assistants to generate high-quality images and vector graphics. By default it uses black-forest-labs/flux-schnell for raster images and recraft-ai/recraft-v3-svg for SVG output. You can override the curated image/SVG models through environment variables, and image tools also accept a per-call model_id override from the built-in allowlist.\n\n📑 Table of Contents\n\n- Getting Started & Integration\n - Setup Process\n - Cursor Integration\n - Claude Desktop Integration\n - Smithery Integration\n- Glama.ai Integration\n- Codex Integration\n- Features\n- Documentation\n - Available Tools\n - Available Resources\n - Available Prompts\n - Structured Output\n - Environment Variables\n- Development\n - Testing\n- Technical Details\n- Troubleshooting\n- Contributing\n- License\n- Resources\n- Examples\n\n🚀 Getting Started & Integration\n\nSetup Process\n\n1. **Obtain a Replicate API Token**\n - Sign up at Replicate\n - Create an API token in your account settings\n\n2. **Choose Your Integration Method**\n - Follow one of the integration options below based on your preferred MCP client\n\n3. **Ask Your AI Assistant to Generate an Image**\n - Simply ask naturally: "Can you generate an image of a serene mountain landscape at sunset?"\n - Or be more specific: "Please create an image showing a peaceful mountain scene with a lake reflecting the sunset colors in the foreground"\n\n4. **Explore Advanced Features**\n - Try different parameter settings for customized results\n - Experiment with SVG generation using generate_svg\n - Use batch image generation or variant generation features\n\nCursor Integration\n\nMethod 1: Using mcp.json\n\n1. Create or edit the .cursor/mcp.json file in your project directory:\n\n \n\n2. Replace YOUR_TOKEN with your actual Replicate API token\n3. Restart Cursor to apply the changes\n\nMethod 2: Manual Mode\n\n1. Open Cursor and go to Settings\n2. Navigate to the "MCP" or "Model Context Protocol" section\n3. Click "Add Server" or equivalent\n4. Enter the following command in the appropriate field:\n\n \n\n5. Replace YOUR_TOKEN with your actual Replicate API token\n6. Save the settings and restart Cursor if necessary\n\nClaude Desktop Integration\n\n1. Create or edit the mcp.json file in your configuration directory:\n\n \n\n2. Replace YOUR_TOKEN with your actual Replicate API token\n3. Restart Claude Desktop to apply the changes\n\nSmithery Integration\n\nThis MCP server is available as a hosted service on Smithery, allowing you to use it without setting up your own server.\n\n1. Visit Smithery and create an account if you don\'t have one\n2. Navigate to the Replicate Flux MCP server page\n3. Click "Add to Workspace" to add the server to your Smithery workspace\n4. Configure your MCP client (Cursor, Claude Desktop, etc.) to use your Smithery workspace URL\n\nFor more information on using Smithery with your MCP clients, visit the Smithery documentation.\n\nGlama.ai Integration\n\nThis MCP server is also available as a hosted service on Glama.ai, providing another option to use it without local setup.\n\n1. Visit Glama.ai and create an account if you don\'t have one\n2. Go to the Replicate Flux MCP server page\n3. Click "Install Server" to add the server to your workspace\n4. Configure your MCP client to use your Glama.ai workspace\n\nFor more information, visit the Glama.ai MCP servers documentation.\n\nCodex Integration\n\nAdd the server to ~/.codex/config.toml:\n\n \n\nReplace the env values as needed. If you omit REPLICATE_IMAGE_MODEL_ID / REPLICATE_SVG_MODEL_ID, the server uses black-forest-labs/flux-schnell for images and recraft-ai/recraft-v3-svg for SVGs.\n\nCurated tools validate model overrides against built-in allowlists:\n\n- **Image generation**: black-forest-labs/flux-schnell, google/imagen-4, black-forest-labs/flux-kontext-pro, ideogram-ai/ideogram-v3-turbo, black-forest-labs/flux-1.1-pro, black-forest-labs/flux-dev\n- **SVG generation**: recraft-ai/recraft-v3-svg\n- **Legacy run_model extras**: minimax/video-01, luma/reframe-video, topazlabs/video-upscale, topazlabs/image-upscale, szcho/codeformer, tencentarc/gfpgan\n\n🌟 Features\n\n- **🖼️ High-Quality Image Generation** — Flux Schnell raster images by default, with environment-variable and per-tool image model overrides.\n- **🎨 Vector Graphics** — Recraft V3 SVG for logos, icons, and diagrams.\n- **📊 Batch + Variants** — Generate N images from N prompts or N variants of one prompt (seed-based or prompt-modifier-based).\n- **🧩 Arbitrary Replicate Models** — run_replicate_model escape hatch accepts any owner/name[:version] reference, with get_model_schema introspection for the OpenAPI input schema. Optional allowlist via REPLICATE_MODEL_ALLOWLIST.\n- **📦 Structured Output** — Every generate_* tool returns machine-readable structuredContent alongside human-readable content, matching a per-tool outputSchema (URL, prompt, format, aspect ratio, per-variant seed, etc).\n- **⏳ Progress Notifications** — Batch and variant generation emit notifications/progress for clients that opt in via progressToken, so long runs aren\'t black-boxed.\n- **💬 Curated Prompts** — 5 ready-made prompt templates (logo, portrait, svg-icon, product-shot, isometric-diagram) surfaced in Claude Desktop\'s slash palette and Cursor\'s @-menu.\n- **🏷️ Proper Tool Annotations** — readOnlyHint / destructiveHint / openWorldHint / idempotentHint set correctly so clients can reason about safety and cost.\n- **🪵 Structured Logging** — Server-side errors travel over notifications/message instead of stderr.\n- **🔌 Universal MCP Compatibility** — MCP protocol 2025-11-25; works with Claude Desktop, Cursor, Cline, Zed, and any spec-compliant client.\n- **🔍 Generation History** — Browse past runs through imagelist, svglist, and predictionlist resources.\n\n📚 Documentation\n\nAvailable Tools\n\ngenerate_image\n\nGenerates an image based on a text prompt using the configured image model (allowlist only).\n\n \n\ngenerate_multiple_images\n\nGenerates multiple images based on an array of prompts using the configured image model (allowlist only).\n\n \n\ngenerate_image_variants\n\nGenerates multiple variants of the same image from a single prompt using the configured image model (allowlist only).\n\n \n\ngenerate_svg\n\nGenerates SVG/vector output based on a text prompt using the configured SVG model (allowlist only).\n\n \n\nprediction_list\n\nRetrieves a list of your recent predictions from Replicate.\n\n \n\nget_prediction\n\nGets detailed information about a specific prediction.\n\n \n\nrun_model\n\nRuns a whitelisted Replicate model with a raw input payload (useful for video or restoration models).\n\n \n\nrun_replicate_model\n\nRuns any model hosted on Replicate by its owner/name[:version] reference. Use this as an escape hatch when none of the curated tools fit. Call get_model_schema first if you don\'t know the input shape.\n\n \n\nSet the REPLICATE_MODEL_ALLOWLIST env var (comma-separated owner/name entries) to restrict which models can be invoked. Unset = any model allowed. Set-but-empty = deny all (the server fails closed rather than silently allowing everything).\n\nget_model_schema\n\nFetches the OpenAPI input schema and description for a Replicate model so you can pass the right parameters to run_replicate_model.\n\n \n\nAvailable Resources\n\nimagelist\n\nBrowse your history of generated images created with the configured image model.\n\nsvglist\n\nBrowse your history of generated SVG outputs created with the configured SVG model.\n\npredictionlist\n\nBrowse all your Replicate predictions history.\n\nAvailable Prompts\n\nCurated templates surfaced in Claude Desktop\'s slash menu and Cursor\'s @-palette. Each one fills in sensible defaults then delegates to the relevant generation tool.\n\n| Prompt | Description | Arguments |\n| --- | --- | --- |\n| logo | Brand/product logo | brand, style?, palette? |\n| portrait | Photoreal portrait | subject, mood?, lens? |\n| svg-icon | Single-concept vector icon | concept, style? |\n| product-shot | Studio product photography | product, surf', 'readme_raw_len': 12942, 'one_line_zh': '📇 ☁️ - 通过 Replicate API 生成图像', 'one_line_plain': 'Generate images via the Replicate API'}, {'id': 1061, 'key': 'christianhinge-dicom-mcp', 'kind': 'mcp', 'name': 'ChristianHinge/dicom-mcp', 'one_line': '🐍 ☁️ 🏠 - DICOM integration to query, read and move medical images and reports from PACS and other…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol (MCP) for interacting with dicom servers (PACS etc.)', 'url': 'https://github.com/ChristianHinge/dicom-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ChristianHinge/dicom-mcp', 'downloads': None, 'stars': 103, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ChristianHinge.png?size=64', 'official_desc': '🐍 ☁️ 🏠 - DICOM integration to query, read and move medical images and reports from PACS and other DICOM-compliant systems.', 'stars_own': 103, 'stars_creator': None, 'stars_own_repo': 'ChristianHinge/dicom-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'DICOM MCP Server for Medical Imaging Systems 🏥\n\n \n \n \n\nThe dicom-mcp server enables AI assistants to query, read, and move data on DICOM servers (PACS, VNA, etc.). \n\n \n\n🤝 **Contribute** •\n📝 **Report Bug** •\n📝 **Blog Post 1** \n\n \n\n \n\n✨ Core Capabilities\n\ndicom-mcp provides tools to:\n\n* **🔍 Query Metadata**: Search for patients, studies, series, and instances using various criteria.\n* **📄 Read DICOM Reports (PDF)**: Retrieve DICOM instances containing encapsulated PDFs (e.g., clinical reports) and extract the text content.\n* **➡️ Send DICOM Images**: Send series or studies to other DICOM destinations, e.g. AI endpoints for image segmentation, classification, etc.\n* **⚙️ Utilities**: Manage connections and understand query options.\n\n🚀 Quick Start\n📥 Installation\nInstall using uv or pip:\n\n \nOr by cloning the repository:\n\n \n\n⚙️ Configuration\n\ndicom-mcp requires a YAML configuration file (config.yaml or similar) defining DICOM nodes and calling AE titles. Adapt the configuration or keep as is for compatibility with the sample ORTHANC Server.\n\n \n> [!WARNING]\nDICOM-MCP is not meant for clinical use, and should not be connected with live hospital databases or databases with patient-sensitive data. Doing so could lead to both loss of patient data, and leakage of patient data onto the internet. DICOM-MCP can be used with locally hosted open-weight LLMs for complete data privacy. \n\n(Optional) Sample ORTHANC server\nIf you don\'t have a DICOM server available, you can run a local ORTHANC server using Docker:\n\nClone the repository and install test dependencies pip install -e ".[dev]\n\n \nUI at http://localhost:8042\n\n🔌 MCP Integration\n\nAdd to your client configuration (e.g. claude_desktop_config.json):\n\n \n\nFor development:\n\n \n\n🛠️ Tools Overview\n\ndicom-mcp provides four categories of tools for interaction with DICOM servers and DICOM data. \n\n🔍 Query Metadata\n\n* **query_patients**: Search for patients based on criteria like name, ID, or birth date.\n* **query_studies**: Find studies using patient ID, date, modality, description, accession number, or Study UID.\n* **query_series**: Locate series within a specific study using modality, series number/description, or Series UID.\n* **query_instances**: Find individual instances (images/objects) within a series using instance number or SOP Instance UID\n📄 Read DICOM Reports (PDF)\n\n* **extract_pdf_text_from_dicom**: Retrieve a specific DICOM instance containing an encapsulated PDF and extract its text content.\n\n➡️ Send DICOM Images\n\n* **move_series**: Send a specific DICOM series to another configured DICOM node using C-MOVE.\n* **move_study**: Send an entire DICOM study to another configured DICOM node using C-MOVE.\n\n⚙️ Utilities\n\n* **list_dicom_nodes**: Show the currently active DICOM node and list all configured nodes.\n* **switch_dicom_node**: Change the active DICOM node for subsequent operations.\n* **verify_connection**: Test the DICOM network connection to the currently active node using C-ECHO.\n* **get_attribute_presets**: List the available levels of detail (minimal, standard, extended) for metadata query results. \n\nExample interaction\nThe tools can be chained together to answer complex questions:\n\n \n \n \n\n📈 Contributing\nRunning Tests\n\nTests require a running Orthanc DICOM server. You can use Docker:\n\n \n\nRun tests using pytest:\n\n \n\nStop the Orthanc container:\n\n \n\nDebugging\n\nUse the MCP Inspector for debugging the server communication:\n\n \n\n🙏 Acknowledgments\n\n* Built using pynetdicom\n* Uses PyPDF2 for PDF text extraction', 'readme_raw_len': 3507, 'one_line_zh': '🐍 ☁️ 🏠 - DICOM 集成：查询、读取和移动来自 PACS 等系统的医学影像与报告…', 'one_line_plain': 'DICOM integration lets you query, read, and move medical images and reports from PACS and other systems'}, {'id': 1671, 'key': 'wise-vision-mcp-server-ros-2', 'kind': 'mcp', 'name': 'WiseVision/mcp_server_ros_2', 'one_line': '🐍 - ROS 2 integration for AI-driven robotics applications and services', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/wise-vision/ros2_mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/wise-vision/mcp_server_ros_2', 'downloads': None, 'stars': 93, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/wise-vision.png?size=64', 'official_desc': '🐍 - ROS 2 integration for AI-driven robotics applications and services.', 'stars_own': 93, 'stars_creator': None, 'stars_own_repo': 'wise-vision/ros2_mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'ROS2 MCP\n\n \n \n \n \n \n \n \n \n \n\n \n\n \n\n**ROS2 MCP** is an open-source (MPL-2.0) **Model Context Protocol** server that lets AI agents (Claude, Cursor, Codex and any other MCP client) list, subscribe to, publish on and call ROS 2 topics, services and actions on a live robot. It talks to ROS 2 directly through rclpy: no rosbridge, no changes to your robots. Transports: **stdio** (default), **Streamable HTTP** and SSE (deprecated by the MCP spec).\n\nEvery tool is free and open source. Commercial support is available if you want help putting it on a real machine.\n\n⚡ Quickstart (30 seconds, Docker)\n\nYou need Docker and an MCP client. ROS 2 itself does not have to be installed: the image contains it.\n\n \n\nAny other MCP client: run docker run -i --rm -e ROS2_MCP_READONLY=1 wisevision/ros2_mcp:jazzy as its stdio command. Then ask: *"List the ROS 2 topics"* and *"Read three messages from /chatter"*.\n\nNo robot at hand? Start a demo publisher first:\n\n \n\nPrefer uv? On a machine with ROS 2 installed and sourced (the server needs rclpy; use the Python that matches your distro, e.g. 3.12 on Jazzy):\n\n \n\nRemove -e ROS2_MCP_READONLY=1 (or the --read-only flag) only when you want the agent to be able to move things; see Security. Client-specific setup (VS Code Copilot, Claude Desktop, Warp, Codex CLI, custom messages): installation guide. Docs: wisevision.tech/docs.\n\n> mcp/ros2 in Docker\'s official MCP catalog (13,750 pulls at the time of writing) is built by Docker and lags our releases: it was last updated on 2026-02-04 and predates read-only mode. wisevision/ros2_mcp is built and pushed by this repository\'s CI and is the one the commands above use.\n\n🧰 Tools\n\n24 tools. ⚠ marks tools that can change robot state; read-only mode removes them. The complete, generated reference with input schemas is docs/generated/tools.md (a CI check keeps it in sync with the code).\n\n| Area | Tools |\n|---|---|\n| **Discover** | ros2_topic_list, ros2_service_list, ros2_list_actions, ros2_interface_list, ros2_get_message_fields (field names and types of a message type) |\n| **Topics** | ros2_topic_subscribe, ros2_subscribe_multiple_topics (images come back as PNG), ⚠ ros2_topic_publish, ⚠ ros2_publish_multiple_topics (set frequency and duration) |\n| **Services** | ⚠ ros2_service_call |\n| **Actions** | ⚠ ros2_send_action_goal (optionally waits for the result), ros2_action_request_result, ros2_action_subscribe_feedback, ros2_action_subscribe_status, ⚠ ros2_cancel_action_goal |\n| **Perception** | ros2_get_map_as_image (nav_msgs/OccupancyGrid to PNG), ros2_get_pointcloud_as_bev (sensor_msgs/PointCloud2 to bird\'s-eye-view PNG) |\n| **History** | ros2_get_messages_stored_in_influx_data_base (past messages from the WiseVision Data Black Box, an InfluxDB-based alternative to Rosbag2) |\n| **Viewer app** | ros2_viewer_app, ros2_viewer_config, ros2_stream_start, ros2_stream_next, ros2_stream_next_image, ros2_stream_stop (interactive image / point cloud / plot viewer for MCP clients that support Apps; the stream tools also serve clients that cannot render it) |\n\nAlso included: four built-in prompts, automatic QoS selection for topics, nested message fields, and custom message types (source your package before starting; see the installation guide).\n\n🧭 Supported ROS 2 distributions\n\n| Distro | Status |\n|---|---|\n| **Jazzy Jalisco** (Ubuntu 24.04, Python 3.12) | Supported. Tests run in CI on ros:jazzy-ros-base; Docker image wisevision/ros2_mcp:jazzy. |\n| **Humble Hawksbill** (Ubuntu 22.04, Python 3.10) | Supported. Tests run in CI on ros:humble-ros-base; Docker image wisevision/ros2_mcp:humble. |\n| Iron, Kilted, Rolling | Not tested and not shipped. It may work from source; we make no promise. Open an issue if you need one. |\n\nROS 1 is not supported (the server uses rclpy). Python 3.10 or newer.\n\n🆚 How it compares\n\nOther open-source ROS MCP servers exist. They make different trade-offs; pick by what you need. Facts below were read from each project\'s README, pyproject.toml and GitHub page on 2026-10-14.\n\n| | **ROS2 MCP** (this repo) | robotmcp/ros-mcp-server | LCAS/ros2_mcp |\n|---|---|---|---|\n| How it reaches ROS | rclpy, directly on the ROS 2 graph (runs where ROS 2 is sourced, or in the Docker image) | rosbridge WebSocket; the MCP server can run on a different machine from the robot | ROS 2 package built in your colcon workspace |\n| ROS versions | ROS 2 Humble, Jazzy | ROS 1 and ROS 2 | ROS 2 Humble, Iron, Jazzy (per its README) |\n| Install | Docker image wisevision/ros2_mcp, or uvx/uv from git | uvx ros-mcp (on PyPI) | git clone, rosdep, colcon build; Docker Compose with an SSE endpoint |\n| Transports | stdio, Streamable HTTP, SSE | stdio, http, streamable-http (--transport) | SSE endpoint documented |\n| Read-only mode | Yes: mutating tools are not registered, and a test enforces the classification | No such switch found in its code; tools carry the MCP readOnlyHint annotation | Its README lists observation features only |\n| Publish / services / actions | Yes, including goal, result, feedback, status and cancel | Yes (topics, services and actions tools) | Not listed in its README |\n| Nodes and parameters | **No** dedicated tools yet | Yes (node and parameter tools) | Node listing |\n| Images and maps | Image topics as PNG, occupancy-grid map, point-cloud bird\'s-eye view | Image tool | Image retrieval for VLM analysis |\n| License | MPL-2.0 | Apache-2.0 | Apache-2.0 |\n| GitHub stars | 90 | 1,493 | 9 |\n\nWhen to pick another one: you need **ROS 1**, node or parameter tools, or you want the MCP server **off the robot** with only rosbridge exposed there: use robotmcp/ros-mcp-server. We do not claim to be better on every axis. What we offer: native rclpy access (no bridge process between the agent and DDS), a read-only mode that fails closed (a tool is registered in that mode only if it was explicitly reviewed as read-only, and a test fails when a new tool is unclassified), images, maps and point clouds returned as PNGs, the complete action lifecycle (goal, result, feedback, status, cancel), and commercial support if you need it. Something here is wrong or out of date? Open an issue and we will fix it.\n\n🗺️ Roadmap (proposed, not committed)\n\nWhat users ask for that is **not** in ROS2 MCP today. No dates; open an issue or a Discussion to vote or to pay for one.\n\n- **Parameter tools**: get / set / list node parameters.\n- **TF tools**: transform lookup and frame tree.\n- **Authentication for Streamable HTTP** (bearer token or a documented reverse-proxy recipe). The transport itself is in #55; until auth exists, keep it on loopback or a private network.\n- **Node tools** (list nodes, node info), Nav2-specific helpers, a PyPI package, ROS 1 (not planned).\n\n🤖 For AI agents\n\nIf you are an agent (or building one) using this server:\n\n- **Discover before you act.** Call ros2_topic_list, ros2_service_list, ros2_list_actions and ros2_interface_list, then ros2_get_message_fields for the exact message type, before any publish, service call or action goal. Do not guess field names.\n- **Prefer read-only.** If ros2_topic_publish, ros2_service_call, ros2_send_action_goal are absent from list_tools, the operator started the server in read-only mode; do not try to work around it.\n- **Mutating tools move real machines.** Confirm intent with the human before calling a ⚠ tool, start with small values and a short duration, and keep ros2_cancel_action_goal in mind.\n- **Right after container start the graph may be incomplete** (DDS discovery). If a topic you expect is missing, retry once before concluding it does not exist; see DDS discovery warm-up.\n- **Machine-readable references:** docs/generated/tools.json (every tool and its input schema), server.json (MCP registry manifest), AGENTS.md (repo layout, how to build, test and add a tool), wisevision.tech/llms.txt.\n\n🏢 Commercial support & integration\n\nROS2 MCP is free software under the **MPL-2.0**: you can use it in a commercial product without asking us. Nothing in this section changes that.', 'readme_raw_len': 14949, 'one_line_zh': '面向 AI 驱动机器人应用与服务的 ROS 2 集成', 'one_line_plain': 'ROS 2 integration for AI-driven robotics applications and services'}, {'id': 4307, 'key': 'tan-yong-sheng-ai-vision-mcp', 'kind': 'mcp', 'name': 'tan-yong-sheng/ai-vision-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Image, video and object-detection analysis with Google Gemini and Vertex AI for UI/UX evaluation,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol (MCP) server that provides vision capabilities to analyze image and video', 'url': 'https://github.com/tan-yong-sheng/ai-vision-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/tan-yong-sheng/ai-vision-mcp', 'downloads': None, 'stars': 80, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/tan-yong-sheng.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Image, video and object-detection analysis with Google Gemini and Vertex AI for UI/UX evaluation, visual regression testing and interface understanding.', 'stars_own': 80, 'stars_creator': None, 'stars_own_repo': 'tan-yong-sheng/ai-vision-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "AI Vision MCP Server\n\nA powerful Model Context Protocol (MCP) server that provides AI-powered image and video analysis using Google Gemini and Vertex AI models.\n\nFeatures\n\n- **Dual Provider Support**: Choose between Google Gemini API and Vertex AI\n- **Multimodal Analysis**: Support for both image and video content analysis\n- **Flexible File Handling**: Upload via multiple methods (URLs, local files, base64)\n- **Storage Integration**: Built-in Google Cloud Storage support\n- **Comprehensive Validation**: Zod-based data validation throughout\n- **Error Handling**: Robust error handling with retry logic and circuit breakers\n- **TypeScript**: Full TypeScript support with strict type checking\n\nQuick Start\n\nPre-requisites\n\nYou could choose either to use google provider or vertex_ai provider. For simplicity, google provider is recommended.\n\nBelow are the environment variables you need to set based on your selected provider. (Note: It’s recommended to set the timeout configuration to more than 5 minutes for your MCP client).\n\n(i) **Using Google AI Studio Provider**\n\n \n\nGet your Google AI Studio's api key here\n\n(ii) **Using Vertex AI Provider**\n\n \n\nRefer to the guideline here on how to set this up.\n\nInstallation\n\nBelow are the installation guide for this MCP on different MCP clients, such as Claude Desktop, Claude Code, Cursor, Cline, etc.\n\n \n Claude Desktop \n\nAdd to your Claude Desktop configuration:\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n\n \n\n \n Claude Code \n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n\nNote: Increase the MCP startup timeout to 1 minutes and MCP tool execution timeout to about 5 minutes by updating ~\\.claude\\settings.json as follows:\n\n \n\n \n\n \n Cursor \n\nGo to: Settings -> Cursor Settings -> MCP -> Add new global MCP server\n\nPasting the following configuration into your Cursor ~/.cursor/mcp.json file is the recommended approach. You may also install in a specific project by creating .cursor/mcp.json in your project folder. See Cursor MCP docs for more info.\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n \n\n \n Cline \n\nCline uses a JSON configuration file to manage MCP servers. To integrate the provided MCP server configuration:\n\n1. Open Cline and click on the MCP Servers icon in the top navigation bar.\n2. Select the Installed tab, then click Advanced MCP Settings.\n3. In the cline_mcp_settings.json file, add the following configuration:\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n \n\n \n\n Other MCP clients \n\nThe server uses stdio transport and follows the standard MCP protocol. It can be integrated with any MCP-compatible client by running:\n\n \n \n\nMCP Tools\n\nThe server provides four main MCP tools:\n\n1) analyze_image\n\nAnalyzes an image using AI and returns a detailed description.\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the image\n- prompt (string): Question or instruction for the AI\n- mode (string, optional): Analysis mode - one of:\n - general (default) - General image analysis\n - palette - Extract design tokens (colors, spacing, typography)\n - hierarchy - Analyze visual hierarchy and eye flow\n - components - Catalog UI components and design system maturity\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Examples:**\n\n1. **General image analysis:**\n \n\n2. **Extract design tokens:**\n \n\n3. **Analyze visual hierarchy:**\n \n\n4. **Component inventory:**\n \n\n2) compare_images\n\nCompares multiple images using AI and returns a detailed comparison analysis.\n\n**Parameters:**\n- imageSources (array): Array of image sources (URLs, base64 data, or file paths) - minimum 2, maximum 4 images\n- prompt (string): Question or instruction for comparing the images\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Examples:**\n\n1. **Compare images from URLs:**\n \n\n2. **Compare mixed sources:**\n \n\n3) detect_objects_in_image\n\nDetects objects in an image using AI vision models and generates annotated images with bounding boxes. Returns detected objects with coordinates and either saves the annotated image to a file or temporary directory.\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the image\n- prompt (string): Custom detection prompt describing what to detect or recognize in the image\n- outputFilePath (string, optional): Explicit output path for the annotated image\n\n**Configuration:**\nThis function uses optimized default parameters for object detection and does not accept runtime options parameter. To customize the AI parameters (temperature, topP, topK, maxTokens), use environment variables:\n\n \n\n**File Handling Logic:**\n1. **Explicit outputFilePath provided** → Saves to the exact path specified\n2. **If not explicit outputFilePath** → Automatically saves to temporary directory\n\n**Response Types:**\n- Returns file object when explicit outputFilePath is provided\n- Returns tempFile object when explicit outputFilePath is not provided so the image file output is auto-saved to temporary folder\n- Always includes detections array with detected objects and coordinates\n- Includes summary with percentage-based coordinates for browser automation\n\n**Examples:**\n\n1. **Basic object detection:**\n \n\n2. **Save annotated image to specific path:**\n \n\n3. **Custom detection prompt:**\n \n\n4) audit_design\n\nAudits UI/UX design compliance with pixel-level analysis and AI critique.\n\nThis tool provides automated design compliance auditing using pure TypeScript/JavaScript pixel analysis combined with Gemini Vision API critique. It extracts dominant colors, detects visual complexity, validates WCAG contrast ratios, and generates actionable design recommendations.\n\n**Inspired by:** Automating UX/UI Design Analysis with Python, Machine Learning, and LLMs by Jade Graham\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the design image\n- prompt (string, optional): Custom audit context or focus areas\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Features:**\n- **Dominant Colors**: K-means clustering to extract 5 primary colors\n- **Edge Complexity**: Sobel operator for visual structure analysis\n- **WCAG Contrast**: W3C relative luminance formula validation (AA/AAA)\n- **Luminance Stats**: Mean brightness and standard deviation calculations\n- **Design Issues**: Automated detection of contrast, complexity, and brightness problems\n- **AI Critique**: Gemini-powered recommendations for design improvements\n\n**Examples:**\n\n1. **Basic design audit:**\n \n\n2. **Audit local design file:**\n \n\n5) analyze_video\n\nAnalyzes a video using AI and returns a detailed description.\n\n**Parameters:**\n- videoSource (string): YouTube URL, GCS URI, or local file path to the video\n- prompt (string): Question or instruction for the AI\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Supported video sources:**\n- YouTube URLs (e.g., https://www.youtube.com/watch?v=...)\n- Local file paths (e.g., C:\\Users\\username\\Downloads\\video.mp4)\n\n**Examples:**\n\n1. **Analyze video from YouTube URL:**\n \n\n2. **Analyze local video file:**\n \n\n**Note:** Only YouTube URLs are supported for public video URLs. Other public video URLs are not currently supported.\n\nEnvironment Configuration\n\nFor basic setup, you only need to configure the provider selection and required credentials:\n\nGoogle AI Studio Provider (Recommended)\n \n\nVertex AI Provider (Production)\n \n\n📖 **Detailed Configuration Guide**\n\nFor comprehensive environment variable documentation, including:\n- Complete configuration reference (60+ environment variables)\n- Function-specific optimization examples\n- Advanced configuration patterns\n- Troubleshooting guidance\n\n👉 **See Environment Variable Guide**\n\nConfiguration Priority Overview\n\nThe server uses a hierarchical configuration system where more specific settings override general ones:\n\n1. **LLM-assigned values** (ru", 'readme_raw_len': 10342, 'one_line_zh': '使用 Google Gemini 和 Vertex AI 进行图像、视频和物体检测分析，用于 UI/UX 评估', 'one_line_plain': 'Analyse images, video and objects with Google Gemini and Vertex AI for UI/UX evaluation'}, {'id': 213, 'key': 'hamflx-imagen3-mcp', 'kind': 'mcp', 'name': 'hamflx/imagen3-mcp', 'one_line': "📇 🏠 🪟 🍎 🐧 - Generate images from text prompts with Google's Imagen 3 API, with photography, artistic and…", 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/hamflx/imagen3-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/hamflx/imagen3-mcp', 'downloads': None, 'stars': 76, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/hamflx.png?size=64', 'official_desc': "📇 🏠 🪟 🍎 🐧 - Generate images from text prompts with Google's Imagen 3 API, with photography, artistic and photorealistic controls.", 'stars_own': 76, 'stars_creator': None, 'stars_own_repo': 'hamflx/imagen3-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "Imagen3-MCP\n\nEnglish Version\n\n基于 Google 的 Imagen 3.0 的图像生成工具，通过 MCP（Model Control Protocol）提供服务。\n\n效果\n\n画一只奔跑的杰克罗素犬，长焦镜头，阳光透过狗狗的毛发，照片级画质\n\n \n\n画一个科技感十足的苹果\n\n \n\n安装要求\n\n- 有效的 Google Gemini API 密钥\n\n安装步骤——Cherry Studio\n\n1. 从 GitHub Releases 下载最新版本的可执行文件\n2. 将下载的可执行文件放置在系统中的任意位置，例如 C:\\bin\\imagen3-mcp.exe\n3. 在 Cherry Studio 中配置：\n - Command 字段填写可执行文件路径，例如 C:\\bin\\imagen3-mcp.exe\n - 环境变量 GEMINI_API_KEY 中填写你的 Gemini API 密钥\n - [可选] 环境变量 BASE_URL 中填写代理地址，例如 https://lingxi-proxy.hamflx.dev/api/provider/google（这个地址可以解决 GFW 的问题，但是解决不了 Google 对 IP 的限制问题，因此还是得挂梯子）。\n - [可选] 环境变量 SERVER_LISTEN_ADDR：设置服务器监听的 IP 地址（默认为 127.0.0.1）。\n - [可选] 环境变量 SERVER_PORT：设置服务器监听的端口和图片 URL 使用的端口（默认为 9981）。\n - [可选] 环境变量 IMAGE_RESOURCE_SERVER_ADDR：设置图片 URL 中使用的服务器地址（默认为 127.0.0.1）。这在服务器运行在容器或远程机器上时很有用。\n\n \n\n安装步骤——Cursor\n\n \n\n许可证\n\nMIT\n\n---\n\nImagen3-MCP (English)\n\nAn image generation tool based on Google's Imagen 3.0, providing services through MCP (Model Control Protocol).\n\nExamples\n\nA running Jack Russell Terrier, telephoto lens, sunlight filtering through the dog's fur, photorealistic quality\n\n \n\nA high-tech apple\n\n \n\nRequirements\n\n- Valid Google Gemini API key\n\nInstallation Steps—Cherry Studio\n\n1. Download the latest executable from GitHub Releases\n2. Place the downloaded executable anywhere in your system, e.g., C:\\bin\\imagen3-mcp.exe\n3. Configure in Cherry Studio:\n - Fill in the Command field with the executable path, e.g., C:\\bin\\imagen3-mcp.exe\n - Enter your Gemini API key in the GEMINI_API_KEY environment variable\n - [Optional] Enter a proxy URL in the BASE_URL environment variable, e.g., https://your-proxy.com.\n - [Optional] Set the SERVER_LISTEN_ADDR environment variable: The IP address the server listens on (defaults to 127.0.0.1).\n - [Optional] Set the SERVER_PORT environment variable: The port the server listens on and uses for image URLs (defaults to 9981).\n - [Optional] Set the IMAGE_RESOURCE_SERVER_ADDR environment variable: The server address used in the image URLs (defaults to 127.0.0.1). Useful if the server runs in a container or remote machine.\n\n \n\nInstallation Steps—Cursor\n\n \n\nLicense\n\nMIT", 'readme_raw_len': 2098, 'one_line_zh': '📇 🏠 🪟 🍎 🐧 - 通过 Google Imagen 3 API 依据文本 Prompt 生成图像，支持摄影、艺术等风格…', 'one_line_plain': "Generate images from text prompts with Google's Imagen 3 API"}, {'id': 3976, 'key': 'pab1it0-tripadvisor-mcp', 'kind': 'mcp', 'name': 'pab1it0/tripadvisor-mcp', 'one_line': '📇 🐍 - Tripadvisor API access: location data, reviews and photos', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol (MCP) server for Tripadvisor Content API. This provides access to Tripadvisor location data, reviews, and photos through standardized MCP interfaces, allowing AI assistants to search for travel destinations and experiences.', 'url': 'https://github.com/pab1it0/tripadvisor-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/pab1it0/tripadvisor-mcp', 'downloads': None, 'stars': 64, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/pab1it0.png?size=64', 'official_desc': '📇 🐍 - Tripadvisor API access: location data, reviews and photos.', 'stars_own': 64, 'stars_creator': None, 'stars_own_repo': 'pab1it0/tripadvisor-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Tripadvisor MCP Server\n\nA [Model Context Protocol][mcp] (MCP) server for Tripadvisor Content API.\n\nThis provides access to Tripadvisor location data, reviews, and photos through standardized MCP interfaces, allowing AI assistants to search for travel destinations and experiences.\n\n[mcp]: https://modelcontextprotocol.io\n\nFeatures\n\n- [x] Search for locations (hotels, restaurants, attractions) on Tripadvisor\n- [x] Get detailed information about specific locations\n- [x] Retrieve reviews and photos for locations\n- [x] Search for nearby locations based on coordinates\n- [x] API Key authentication\n- [x] Docker containerization support\n\n- [x] Provide interactive tools for AI assistants\n\nThe list of tools is configurable, so you can choose which tools you want to make available to the MCP client.\n\nUsage\n\n1. Get your Tripadvisor Content API key from the Tripadvisor Developer Portal.\n\n2. Configure the environment variables for your Tripadvisor Content API, either through a .env file or system environment variables:\n\n \n\n3. Add the server configuration to your client configuration file. For example, for Claude Desktop:\n\n \n\n> Note: if you see Error: spawn uv ENOENT in Claude Desktop, you may need to specify the full path to uv or set the environment variable NO_UV=1 in the configuration.\n\nDocker Usage\n\nThis project includes Docker support for easy deployment and isolation.\n\nBuilding the Docker Image\n\nBuild the Docker image using:\n\n \n\nRunning with Docker\n\nYou can run the server using Docker in several ways:\n\nUsing docker run directly:\n\n \n\nUsing docker-compose:\n\nCreate a .env file with your Tripadvisor API key and then run:\n\n \n\nRunning with Docker in Claude Desktop\n\nTo use the containerized server with Claude Desktop, update the configuration to use Docker with the environment variables:\n\n \n\nThis configuration passes the environment variables from Claude Desktop to the Docker container by using the -e flag with just the variable name, and providing the actual values in the env object.\n\nDevelopment\n\nContributions are welcome! Please open an issue or submit a pull request if you have any suggestions or improvements.\n\nThis project uses uv to manage dependencies. Install uv following the instructions for your platform:\n\n \n\nYou can then create a virtual environment and install the dependencies with:\n\n \n\nProject Structure\n\nThe project has been organized with a src directory structure:\n\n \n\nTesting\n\nThe project includes a test suite that ensures functionality and helps prevent regressions.\n\nRun the tests with pytest:\n\n \n\nTools\n\n| Tool | Category | Description |\n| --- | --- | --- |\n| search_locations | Search | Search for locations by query text, category, and other filters |\n| search_nearby_locations | Search | Find locations near specific coordinates |\n| get_location_details | Retrieval | Get detailed information about a location |\n| get_location_reviews | Retrieval | Retrieve reviews for a location |\n| get_location_photos | Retrieval | Get photos for a location |\n\nLicense\n\nMIT\n\n---\n\n[mcp]: https://modelcontextprotocol.io', 'readme_raw_len': 3053, 'one_line_zh': '提供 Tripadvisor API 访问：获取地点数据、评论和照片', 'one_line_plain': 'Get Tripadvisor location data, reviews and photos'}, {'id': 371, 'key': 'raveenb-fal-mcp-server', 'kind': 'mcp', 'name': 'raveenb/fal-mcp-server', 'one_line': '🐍 ☁️ - Generate images, videos and music with Fal.ai models such as FLUX, Stable Diffusion and MusicGen', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/luminarylane/fal-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/raveenb/fal-mcp-server', 'downloads': None, 'stars': 61, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/raveenb.png?size=64', 'official_desc': 'Fal.ai MCP Server A Model Context Protocol (MCP) server that enables Claude Desktop (and other MCP clients) to generate images, videos, music, and audio using Fal.ai models.', 'stars_own': 61, 'stars_creator': None, 'stars_own_repo': 'luminarylane/fal-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': '🎨 Fal.ai MCP Server\n\n \n \n \n \n \n \n \n \n\nA Model Context Protocol (MCP) server that enables Claude Desktop (and other MCP clients) to generate images, videos, music, and audio using Fal.ai models.\n\n \n \n \n\n✨ Features\n\n🚀 Performance\n- **Native Async API** - Uses fal_client.run_async() for optimal performance\n- **Queue Support** - Long-running tasks (video/music) use queue API with progress updates\n- **Non-blocking** - All operations are truly asynchronous\n\n🌐 Transport Modes (New!)\n- **STDIO** - Traditional Model Context Protocol communication\n- **HTTP/SSE** - Web-based access via Server-Sent Events\n- **Dual Mode** - Run both transports simultaneously\n\n🎨 Media Generation (18 Tools)\n\n**Image Generation:**\n- 🖼️ **generate_image** - Create images from text prompts (Flux, SDXL, etc.)\n- 🎯 **generate_image_structured** - Fine-grained control over composition, lighting, subjects\n- 🔄 **generate_image_from_image** - Transform existing images with style transfer\n\n**Image Editing:**\n- ✂️ **remove_background** - Remove backgrounds from images (transparent PNG)\n- 🔍 **upscale_image** - Upscale images 2x or 4x while preserving quality\n- ✏️ **edit_image** - Edit images using natural language instructions\n- 🎭 **inpaint_image** - Edit specific regions using masks\n- 📐 **resize_image** - Smart resize for social media (Instagram, YouTube, TikTok, etc.)\n- 🏷️ **compose_images** - Overlay images (watermarks, logos) with precise positioning\n\n**Video Tools:**\n- 🎬 **generate_video** - Text-to-video and image-to-video generation\n- 📹 **generate_video_from_image** - Animate images into videos\n- 🔀 **generate_video_from_video** - Video restyling and motion transfer\n\n**Audio Tools:**\n- 🎵 **generate_music** - Create instrumental music or songs with vocals\n\n**Utility Tools:**\n- 🔍 **list_models** - Discover 600+ available models with smart filtering\n- 💡 **recommend_model** - AI-powered model recommendations for your task\n- 💰 **get_pricing** - Check costs before generating content\n- 📊 **get_usage** - View spending history and usage stats\n- ⬆️ **upload_file** - Upload local files for use with generation tools\n\n🔍 Dynamic Model Discovery (New!)\n- **600+ Models** - Access all models available on Fal.ai platform\n- **Auto-Discovery** - Models are fetched dynamically from the Fal.ai API\n- **Smart Caching** - TTL-based cache for optimal performance\n- **Flexible Input** - Use full model IDs or friendly aliases\n\n🚀 Quick Start\n\nPrerequisites\n\n- Python 3.10 or higher\n- Fal.ai API key (free tier available)\n- Claude Desktop (or any MCP-compatible client)\n\nInstallation\n\nOption 0: Claude Code Plugin (Simplest for Claude Code Users) 🔌\n\nIf you\'re using Claude Code, install directly via the plugin system:\n\n \n\nOr install directly without adding the marketplace:\n \n\n> **Note:** You\'ll need to set FAL_KEY in your environment before using the plugin.\n\nOption 1: uvx (Recommended - Zero Install) ⚡\n\nRun directly without installation using uv:\n\n \n\n**Claude Desktop Configuration for uvx:**\n \n\n> **Note:** Install uv first: curl -LsSf https://astral.sh/uv/install.sh | sh\n\nOption 2: Docker (Recommended for Production) 🐳\n\nOfficial Docker image available on GitHub Container Registry.\n\n**Step 1: Start the Docker container**\n\n \n\n**Step 2: Configure Claude Desktop to connect**\n\nAdd to your Claude Desktop config file:\n- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n- Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n\n \n\n> **Note:** This uses mcp-remote to connect to the HTTP/SSE endpoint. Alternatively, if you have curl available: "command": "curl", "args": ["-N", "http://localhost:8080/sse"]\n\n**Step 3: Restart Claude Desktop**\n\nThe fal-ai tools should now be available.\n\n**Docker Environment Variables:**\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| FAL_KEY | (required) | Your Fal.ai API key |\n| FAL_MCP_TRANSPORT | http | Transport mode: http, stdio, or dual |\n| FAL_MCP_HOST | 0.0.0.0 | Host to bind the server to |\n| FAL_MCP_PORT | 8080 | Port for the HTTP server |\n\n**Using Docker Compose:**\n\n \n\n**⚠️ File Upload with Docker:**\n\nThe upload_file tool requires volume mounts to access host files:\n\n \n\nThen use container paths like /downloads/image.png instead of host paths.\n\n| Feature | stdio (uvx) | Docker (HTTP/SSE) |\n|---------|:-----------:|:-----------------:|\n| upload_file | ✅ Full filesystem | ⚠️ Needs volume mounts |\n| Security | Runs as user | Sandboxed container |\n\nOption 3: Install from PyPI\n\n \n\nOr with uv:\n \n\nOption 4: Install from source\n\n \n\nConfiguration\n\n1. Get your Fal.ai API key from fal.ai\n\n2. Configure Claude Desktop by adding to:\n - macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n - Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n\nFor PyPI/pip Installation:\n \n\n> **Note:** For Docker configuration, see Option 2: Docker above.\n\nFor Source Installation:\n \n\n3. Restart Claude Desktop\n\n💬 Usage\n\nWith Claude Desktop\n\nOnce configured, ask Claude to:\n\n- "Generate an image of a sunset"\n- "Create a video from this image"\n- "Generate 30 seconds of ambient music"\n- "Convert this text to speech"\n- "Transcribe this audio file"\n\nDiscovering Available Models\n\nUse the list_models tool to discover available models:\n\n- "What image models are available?"\n- "List video generation models"\n- "Search for flux models"\n\nUsing Any Fal.ai Model\n\nYou can use any model from the Fal.ai platform:\n\n \n\nHTTP/SSE Transport (New!)\n\nRun the server with HTTP transport for web-based access:\n\n \n\nConnect from web clients via Server-Sent Events:\n- SSE endpoint: http://localhost:8080/sse (Docker) or http://localhost:8000/sse (pip)\n- Message endpoint: POST http://localhost:8080/messages/\n\nSee Docker Documentation and HTTP Transport Documentation for details.\n\n📦 Supported Models\n\nThis server supports **600+ models** from the Fal.ai platform through dynamic discovery. Use the list_models tool to explore available models, or use any model ID directly.\n\nPopular Aliases (Quick Reference)\n\nThese friendly aliases are always available for commonly used models:\n\n| Alias | Model ID | Type |\n|-------|----------|------|\n| flux_schnell | fal-ai/flux/schnell | Image |\n| flux_dev | fal-ai/flux/dev | Image |\n| flux_pro | fal-ai/flux-pro | Image |\n| sdxl | fal-ai/fast-sdxl | Image |\n| stable_diffusion | fal-ai/stable-diffusion-v3-medium | Image |\n| svd | fal-ai/stable-video-diffusion | Video |\n| animatediff | fal-ai/fast-animatediff | Video |\n| kling | fal-ai/kling-video | Video |\n| musicgen | fal-ai/musicgen-medium | Audio |\n| musicgen_large | fal-ai/musicgen-large | Audio |\n| bark | fal-ai/bark | Audio |\n| whisper | fal-ai/whisper | Audio |\n\nUsing Full Model IDs\n\nYou can also use any model directly by its full ID:\n\n \n\nUse list_models with category filters to discover more:\n- list_models(category="image") - All image generation models\n- list_models(category="video") - All video generation models\n- list_models(category="audio") - All audio models\n- list_models(search="flux") - Search for specific models\n\n📚 Documentation\n\n| Guide | Description |\n|-------|-------------|\n| Installation Guide | Detailed setup instructions for all platforms |\n| API Reference | Complete tool documentation with parameters |\n| Examples | Usage examples for image, video, and audio generation |\n| Docker Guide | Container deployment and configuration |\n| HTTP Transport | Web-based SSE transport setup |\n| Local Testing | Running CI locally with act |\n\n📖 **Full documentation site**: raveenb.github.io/fal-mcp-server\n\n🔌 Claude Code Plugin Marketplace\n\nThis project is part of the **Luminary Lane Tools** marketplace for Claude Code plugins.\n\n**Add the marketplace:**\n \n\n**Available plugins:**\n| Plugin | Description |\n|--------|-------------|\n| fal-ai | Generate images, videos, and music using 600+ Fal.ai models |\n\n*More plugins coming soon!*\n\n🔧 Troubleshooting\n\nCommon Errors\n\nFAL_KEY not set\n \n**Solution:** Set your Fal.ai API key:\n \n\nModel not found\n \n**Solution:** Use list_models to discover available models, or check the ', 'readme_raw_len': 9308, 'one_line_zh': '使用 FLUX、Stable Diffusion、MusicGen 等 Fal.ai 模型生成图像、视频和音乐', 'one_line_plain': 'Fal.ai models such as FLUX, Stable Diffusion and MusicGen let users generate images, videos and music'}]/2026-10-20 https://anclum.com/en/[{'id': 88, 'key': 'code-yeongyu-oh-my-openagent', 'kind': 'repo', 'name': 'code-yeongyu/oh-my-openagent', 'one_line': 'OmO: Just type "mass ulw" keyword with your prompt', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'OmO: Just type "mass ulw" keyword with your prompt. Now you are the master of graph engineering.', 'url': 'https://github.com/code-yeongyu/oh-my-openagent', 'source': 'github', 'source_url': 'https://github.com/code-yeongyu/oh-my-openagent', 'downloads': None, 'stars': 70200, 'installs': None, 'mentions': 2, 'score': 342.84, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 31, 'icon': 'https://github.com/code-yeongyu.png?size=64', 'official_desc': 'OmO: Just type "mass ulw" keyword with your prompt. Now you are the master of graph engineering.', 'stars_own': 70200, 'stars_creator': None, 'stars_own_repo': 'code-yeongyu/oh-my-openagent', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npm i -g omo-ai', 'install_src': 'HEAD/README.md', 'install_conf': 'low', 'desc_src': 'github-api', 'readme_body': '> [!NOTE]\n> **OmO 5.0: OmO ❤️ Pi**\n> Install it with curl -fsSL https://get.omo.dev/install.sh | bash. Memory system, CodeMode, Anthropic subscriptions, all covered.\n> \n> *Just type "mass ulw" with your prompt - now you are the master of graph engineering. Multi-model ultracode, together with a better memory system. (The right panel is omo-herdr-dag.)*\n\n> **Sponsors**\n> These are our sponsors. They help keep OmO going as a personal side project.\n> | | **OpenGateway** sponsors OmO. An OpenAI-compatible gateway that puts many model providers behind one API. Thank you for backing open source. |\n> | :-----| :----- |\n\n> [!NOTE]\n>\n> \n> > **OmO is maintained by Jobdori, the AI assistant shown above. Meet your own Jobdori, Dori. Join the waitlist here.**\n\n> [!TIP]\n> Be with us!\n>\n> | | Join our Discord community to connect with contributors and fellow OmO users. |\n> | :-----| :----- |\n> | | Updates for OmO used to be posted on my X account. Since it was mistakenly suspended, @justsisyphus now posts updates on my behalf. |\n> | | Follow @code-yeongyu on GitHub for more projects. |\n\n -->\n\n \n\n \n\nOmO\n\n**Your tool for real work. But it\'s an agent.**\n\n \n \n \n \n \n \n\nEnglish | 简体中文 | 日本語 | Русский | 한국어\n\n \n\n -->\n\nOmO is one omo command that turns your tokens into finished work: research across ten thousand sources, a deck people actually get, backends, frontends, code. It runs on senpi, our fork of pi, with everything below built in.\n\nInstall\n\n \n\nOn Windows, in PowerShell: irm https://get.omo.dev/install.ps1 | iex. The script installs the native omo binary for your OS and CPU into ~/.local/bin, checks it against the release checksums, and falls back to GitHub Releases when the mirror is unreachable. Add -s -- beta (or a version such as -s -- 5.1.1) after bash to pick another channel. Windows CMD, fixing your PATH, updating and uninstalling are covered at omo.dev/docs/install.\n\nPrefer a package manager? bun add -g omo-ai (or npm i -g omo-ai) installs the same OmO. The package is omo-ai; the unrelated omo package on npm is someone else\'s.\n\nOpen your project, run omo, describe the job. That\'s the whole setup.\n\nIn a session, /thinking sets how hard the model thinks (/thinking alone opens a picker), Shift+Tab cycles through the levels, and /efforts sets the reasoning effort directly on models with effort grades. To pick up earlier work, /resume (or /sessions) opens your past sessions; from the shell, omo -c continues the last one and omo -r opens the picker. /help lists everything else.\n\nComing from the OpenCode edition or LazyCodex? Run omo setup once. It carries your provider keys, custom providers, MCP servers, skills and model picks across. omo doctor then shows which task categories your providers can run and what the old install left behind. The full walkthrough is Migrating from OpenCode.\n\nTo update, run the install line again (a package-manager install updates with omo update). rm ~/.local/bin/omo removes the binary install; bun remove -g omo-ai (or npm uninstall -g omo-ai) removes the package install.\n\nWhy OmO\n\n**ultrawork.** Skip the study. If a task feels hard, add ultrawork (or ulw) to your prompt. The agent reads the codebase before touching a line, plans, proves each step before moving on, checks the result on the real surface, and stops when it\'s done.\n\n**Hundreds of agents, one keyword.** Type mass ulw and the whole job becomes a graph of agents: deep research over thousands of sources, a full machine-learning pipeline, a migration nobody wants to do. Each part runs on the model that fits it, in parallel, and gets checked before anything is called done.\n\n**Kibitzer. Your agent doesn\'t forget you, or your work.** Memory lives in a git repository of markdown files. A small, cheap model sits beside the expensive main agent, reads what you\'ve done before, and nudges the main agent before it repeats a mistake. You stop re-explaining yourself.\n\n**Mix models without thinking about it.** Use the frontier models you love together with the cheap ones. Just sign in: /login covers Claude, ChatGPT, Kimi and GLM subscriptions. Every model ships with a system prompt tuned for it, so you never pick the wrong one.\n\n**High-quality skills, only the ones you need.** Your model is smart, but it still doesn\'t know a lot about solving real-world problems. OmO ships the missing knowledge as skills, browser use included, and loads only what the task needs.\n\n**Everything else is crafted.** Independent tool calls run together in one small program, so twenty reads are one round trip. Malformed tool arguments are repaired before they run. Waiting on a build or a deploy is a subscription, not polling. Long work keeps a record of what was checked, so a restarted session picks up where it stopped. Config, skills and prompts reload live.\n\nConfiguration\n\nOpinionated defaults, adjustable if you insist. Your settings live in ~/.omo/omo.jsonc, and a project can add its own .omo/omo.jsonc; the closest file wins. Every key is in the configuration reference.\n\nDocs\n\n- omo.dev/docs\n- Migrating from OpenCode\n- Configuration reference\n- Features\n- Computer use (experimental)\n- Ultrawork Manifesto\n\nReviews\n\n> "It made me cancel my Cursor subscription. Unbelievable things are happening in the open source community." - Arthur Guiot\n\n> "If Claude Code does in 7 days what a human does in 3 months, OmO does it in 1 hour." - B, Quant Researcher\n\n> "You guys should pull this into core and recruit him. Seriously. It\'s really, really, really good." - Henning Kilset\n\nAuthor\'s Note\n\nWhen I have to take the wheel back from an agent, that isn\'t teamwork. The system failed. OmO is built on that idea. You say what you want; the agent researches, plans, writes code you couldn\'t tell from a senior engineer\'s, verifies it, and keeps going until it\'s finished. It spends tokens when they buy real speed and routes the easy parts to cheap models.\n\nI\'m this project\'s most obsessive user. Which model has the sharpest logic? Who writes the best prose? What did everyone else ship this week? OmO is where those answers land. Have a better way? PRs welcome.\n\nThe goal is an agent you stop noticing. You flip the switch, the light turns on. The long version is the Ultrawork Manifesto.\n\nNo affiliation with any project or model mentioned. The Ultragoal and UltraQA ideas come from oh-my-codex, reimplemented from concept for OmO.\n\nLoved by professionals at\n\nIndent, makers of Spray (influencer marketing solution), vovushop (cross-border commerce platform) and vreview (AI commerce review marketing solution). Google. Microsoft. Vercel. ELESTYLE, makers of elepay (multi-mobile payment gateway) and OneQR (mobile application SaaS for cashless solutions). Deepgram.', 'readme_raw_len': 6698, 'one_line_zh': 'OmO：在 Prompt 中输入 mass ulw 关键词即可', 'one_line_plain': 'Type "mass ulw" keyword with your prompt to trigger OmO'}, {'id': 2762, 'key': 'upstash-context7', 'kind': 'mcp', 'name': 'upstash/context7', 'one_line': '📇 ☁️ - Up-to-date code documentation for LLMs and AI code editors', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Context7 Platform Up-to-date docs for AI agents', 'url': 'https://github.com/upstash/context7', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/upstash/context7', 'downloads': None, 'stars': 63321, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/upstash.png?size=64', 'official_desc': '📇 ☁️ - Up-to-date code documentation for LLMs and AI code editors.', 'stars_own': 63321, 'stars_creator': None, 'stars_own_repo': 'upstash/context7', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Context7 - Up-to-date Docs for AI Agents\n\n \n\n \n\n❌ Without Context7\n\nLLMs rely on outdated or generic information about the libraries you use. You get:\n\n- ❌ Code examples are outdated and based on year-old training data\n- ❌ Hallucinated APIs that don\'t even exist\n- ❌ Generic answers for old package versions\n\n✅ With Context7\n\nContext7 pulls up-to-date, version-specific documentation and code examples straight from the source — and gives them to your coding agent.\n\n \n\n \n\n \n\nContext7 fetches up-to-date code examples and documentation right into your coding agent\'s context. No tab-switching, no hallucinated APIs that don\'t exist, no outdated code generation.\n\nWorks in two modes:\n\n- **CLI + Skills** — installs a skill that guides your agent to fetch docs using ctx7 CLI commands (no MCP required)\n- **MCP** — registers a Context7 MCP server so your agent can call documentation tools natively\n\nInstallation\n\n> [!NOTE]\n> **API Key Recommended**: Get a free API key at context7.com/dashboard for higher rate limits.\n\nSet up Context7 for your coding agents with a single command. The ctx7 CLI requires Node.js 22.13 or newer.\n\n \n\nAuthenticates via OAuth, generates an API key, and installs the appropriate skill. You can choose between CLI + Skills or MCP mode. Use --cursor, --claude, or --opencode to target a specific agent.\n\nTo remove the generated setup later, run npx ctx7 remove. If you globally installed the CLI with npm install -g ctx7, remove that package separately with npm uninstall -g ctx7.\n\nTo configure manually, use the Context7 server URL https://mcp.context7.com/mcp with your MCP client and pass your API key via the Authorization: Bearer YOUR_API_KEY header. See the link below for client-specific setup instructions.\n\n**Manual Installation / Other Clients →**\n\nImportant Tips\n\nUse Library Id\n\nIf you already know exactly which library you want to use, add its Context7 ID to your prompt. That way, Context7 can skip the library-matching step and directly retrieve docs.\n\n \n\nThe slash syntax tells Context7 exactly which library to load docs for.\n\nSpecify a Version\n\nTo get documentation for a specific library version, just mention the version in your prompt:\n\n \n\nContext7 will automatically match the appropriate version.\n\nAdd a Rule\n\nIf you installed via ctx7 setup, a skill is configured automatically that triggers Context7 for library-related questions. To set up a rule manually instead, add one to your coding agent:\n\n- **Cursor**: Cursor Settings > Rules\n- **Claude Code**: CLAUDE.md\n- Or the equivalent in your coding agent\n\n**Example rule:**\n\n \n\nAvailable Tools\n\nCLI Commands\n\n- ctx7 library : Searches the Context7 index by library name and returns matching libraries with their IDs.\n- ctx7 docs : Retrieves documentation for a library using a Context7-compatible library ID (e.g., /mongodb/docs, /vercel/next.js).\n\nMCP Tools\n\n- resolve-library-id: Resolves a general library name into a Context7-compatible library ID.\n - query (required): The user\'s question or task (used to rank results by relevance)\n - libraryName (required): The name of the library to search for\n- query-docs: Retrieves documentation for a library using a Context7-compatible library ID.\n - libraryId (required): Exact Context7-compatible library ID (e.g., /mongodb/docs, /vercel/next.js)\n - query (required): The question or task to get relevant documentation for\n - language (optional): The programming language you write in, used to rank code examples in that language first\n\nMore Documentation\n\n- CLI Reference - Full CLI documentation\n- MCP Clients - Manual MCP installation for 30+ clients\n- Adding Libraries - Submit your library to Context7\n- Troubleshooting - Common issues and solutions\n- API Reference - REST API documentation\n- Developer Guide - Run Context7 MCP locally\n\nPackages\n\n- @upstash/context7-mcp - MCP server\n- ctx7 - CLI\n- @upstash/context7-sdk - TypeScript SDK\n- @upstash/context7-tools-ai-sdk - Vercel AI SDK tools\n- @upstash/context7-pi - pi.dev extension\n\nDisclaimer\n\n1- Context7 projects are community-contributed and while we strive to maintain high quality, we cannot guarantee the accuracy, completeness, or security of all library documentation. Projects listed in Context7 are developed and maintained by their respective owners, not by Context7. If you encounter any suspicious, inappropriate, or potentially harmful content, please use the "Report" button on the project page to notify us immediately. We take all reports seriously and will review flagged content promptly to maintain the integrity and safety of our platform. By using Context7, you acknowledge that you do so at your own discretion and risk.\n\n2- This repository hosts the MCP server’s source code. The supporting components — API backend, parsing engine, and crawling engine — are private and not part of this repository.\n\n🤝 Connect with Us\n\nStay updated and join our community:\n\n- 📢 Follow us on X for the latest news and updates\n- 🌐 Visit our Website\n- 💬 Join our Discord Community\n\n📺 Context7 In Media\n\n- Better Stack: "Free Tool Makes Cursor 10x Smarter"\n- Cole Medin: "This is Hands Down the BEST MCP Server for AI Coding Assistants"\n- Income Stream Surfers: "Context7 + SequentialThinking MCPs: Is This AGI?"\n- Julian Goldie SEO: "Context7: New MCP AI Agent Update"\n- JeredBlu: "Context 7 MCP: Get Documentation Instantly + VS Code Setup"\n- Income Stream Surfers: "Context7: The New MCP Server That Will CHANGE AI Coding"\n- AICodeKing: "Context7 + Cline & RooCode: This MCP Server Makes CLINE 100X MORE EFFECTIVE!"\n- Sean Kochel: "5 MCP Servers For Vibe Coding Glory (Just Plug-In & Go)"\n\n📄 License\n\nMIT', 'readme_raw_len': 5621, 'one_line_zh': '向 LLM 和 AI 代码编辑器提供最新代码文档', 'one_line_plain': 'Provide up-to-date code documentation for LLMs and AI code editors'}, {'id': 1454, 'key': 'idosal-git-mcp', 'kind': 'mcp', 'name': 'idosal/git-mcp', 'one_line': '📇 ☁️ - Generic remote server that provides documentation for any GitHub repository or project', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Put an end to code hallucinations GitMCP is a free, open-source, remote MCP server for any GitHub project', 'url': 'https://github.com/idosal/git-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/idosal/git-mcp', 'downloads': None, 'stars': 8471, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/idosal.png?size=64', 'official_desc': '📇 ☁️ - Generic remote server that provides documentation for any GitHub repository or project.', 'stars_own': 8471, 'stars_creator': None, 'stars_own_repo': 'idosal/git-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'GitMCP\n\n \n \n \n\n \n What is GitMCP •\n Features •\n Getting Started •\n How It Works •\n Badge •\n Examples •\n FAQ •\n Privacy •\n Contributing •\n License \n \n \n\n \n \n \n \n\n \n \n \n\n🤔 What is GitMCP?\n**Stop vibe-hallucinating and start vibe-coding!**\n\nGitMCP is a free, open-source, remote Model Context Protocol (MCP) server that transforms **any** GitHub project (repositories or GitHub pages) into a documentation hub. It enables AI tools like Cursor to access up-to-date documentation and code, even if the LLM has never encountered them, thereby eliminating code hallucinations seamlessly.\n\nGitMCP supports **two flavors** -\n\n* **Specific Repository (gitmcp.io/{owner}/{repo} or {owner}.gitmcp.io/{repo}):** Use these when you primarily work with a select number of libraries. This ensures your AI assistant always targets the correct project, enhancing security and relevance by preventing access to unintended repositories.\n* **Generic Server (gitmcp.io/docs):** Use this for maximum flexibility when you need to switch between different repositories frequently. The AI assistant will prompt you (or decide based on context) which repository to access for each request. Be mindful that this relies on correctly identifying the target repository each time.\n\n**With GitMCP:**\n\n* AI assistants access the *latest* documentation and code directly from the source.\n* Get accurate API usage and reliable code examples.\n* Work effectively even with niche, new, or rapidly changing libraries.\n* Significantly reduced hallucinations and improved code correctness.\n\nFor example, this side-by-side comparison shows the result for the same one-shot prompt in Cursor when creating a three.js scene -\n\nhttps://github.com/user-attachments/assets/fbf1b4a7-f9f0-4c0e-831c-4d64faae2c45\n\n✨ Features\n\n- 😎 **Latest Documentation on ANY GitHub Project**: Grant your AI assistant seamless access to the GitHub project\'s documentation and code. The built-in smart search capabilities help find exactly what the AI needs without using too many tokens!\n- 🧠 **No More Hallucinations**: With GitMCP, your AI assistant can provide accurate and relevant answers to your questions.\n- ☁️ **Zero Setup**: GitMCP runs in the cloud. Simply add the chosen GitMCP URL as an MCP server in your IDE — no downloads, installations, signups, or changes are required.\n- 💬 **Embedded Chat**: Start quickly by chatting directly with the repository\'s documentation through our in-browser chat!\n- ✅ **Open, Free, and Private**: GitMCP is open-source and completely free to use. It doesn\'t collect personal information or store queries. You can even self-host it!\n\n \n\n🚀 Getting Started\n\nUsing GitMCP is easy! Simply follow these steps:\n\nStep 1: Choose the type of server you want\n\nChoose one of these URL formats depending on what you want to connect to:\n\n- For GitHub repositories: gitmcp.io/{owner}/{repo}\n- For GitHub Pages sites: {owner}.gitmcp.io/{repo}\n- For a generic tool that supports any repository (dynamic): gitmcp.io/docs\n\nReplace {owner} with the GitHub username or organization name, and {repo} with the repository name.\n\nFor your convenience, you can also use the conversion tool on the landing page to format the GitHub URL into an MCP URL!\n\nStep 2: Connect your AI assistant\n\nSelect your AI assistant from the options below and follow the configuration instructions:\n\nConnecting Cursor\n\nUpdate your Cursor configuration file at ~/.cursor/mcp.json:\n \n\nConnecting Claude Desktop\n\n1. In Claude Desktop, go to Settings > Developer > Edit Config\n2. Replace the configuration with:\n \n\nConnecting Windsurf\n\nUpdate your Windsurf configuration file at ~/.codeium/windsurf/mcp_config.json:\n \n\nConnecting VSCode\n\nUpdate your VSCode configuration file at .vscode/mcp.json:\n \n\nConnecting Cline\n\nUpdate your Cline configuration file at ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json:\n \n\nConnecting Highlight AI\n\n1. Open Highlight AI and click the plugins icon (@ symbol) in the sidebar\n2. Click **Installed Plugins** at the top of the sidebar\n3. Select **Custom Plugin**\n4. Click **Add a plugin using a custom SSE URL**\n\nPlugin name: gitmcp\nSSE URL: https://gitmcp.io/{owner}/{repo}\n\nFor more details on adding custom MCP servers to HighlightAI, refer to the documentation.\n\nConnecting Augment Code\n\n1. Open Augment Code settings\n2. Navigate to the MCP section\n3. Add a new MCP server with the following details:\n\nName the MCP server: git-mcp Docs\n\nUse this command:\n \n\nOr use the following configuration:\n \n\nConnecting Msty AI\n1. Open Msty Studio\n2. Go to Tools > Import Tools from JSON Clipboard\n3. Paste the following configuration:\n\n \n\nFor more details on configuring MCP servers in Augment Code, visit the Augment Code documentation.\n\n> **Note:** Remember to replace {owner} and {repo} with the actual GitHub username/organization and repository name. You can also use the dynamic endpoint https://gitmcp.io/docs to allow your AI to access any repository on demand.\n\n⚙ How It Works\n\nGitMCP connects your AI assistant to GitHub repositories using the Model Context Protocol (MCP), a standard that lets AI tools request additional information from external sources.\n\nWhat happens when you use GitMCP:\n\n1. **You provide the GitMCP URL** to your AI assistant (e.g., gitmcp.io/microsoft/typescript). GitMCP exposes tools like documentation fetching, smart search, code search, etc.\n2. **Prompt the AI assistant** on documentation/code-related questions.\n3. **Your AI sends requests** to GitMCP to use its tools (with your approval).\n4. **GitMCP executes the AI\'s request** and returns the requested data.\n5. **Your AI receives the information** and generates a more accurate, grounded response without hallucinations.\n\nSupported Documentation\n\nGitMCP currently supports the following documents (in order of priority):\n1. llms.txt\n2. AI-optimized version of the project\'s documentation\n3. README.md/root\n\n💡 Examples\n\nHere are some examples of how to use GitMCP with different AI assistants and repositories:\n\nExample 1: Using Windsurf with a specific repository\n\nFor the GitHub repository https://github.com/microsoft/playwright-mcp, add https://gitmcp.io/microsoft/playwright-mcp as an MCP server to Windsurf.\n\n**Prompt to Claude:**\n> "How do I use the Playwright MCP"\n\nWindsurf will pull the relevant documentation from GitMCP to implement the memory feature correctly.\n\nExample 2: Using Cursor with a GitHub Pages site\n\nFor the GitHub Pages site langchain-ai.github.io/langgraph, add https://langchain-ai.gitmcp.io/langgraph as an MCP server to Cursor.\n\n**Prompt to Cursor:**\n> "Add memory to my LangGraph agent"\n\nCursor will pull the relevant documentation and code from GitMCP to correctly implement the memory feature.\n\nExample 3: Using Claude Desktop with the dynamic endpoint\n\nYou don\'t have to pick specific repositories. The generic gitmcp.io/docs endpoint allows AI to pick the GitHub project on the fly!\n\n**Prompt to any AI assistant:**\n> "I want to learn about the OpenAI Whisper speech recognition model. Explain how it works."\n\nClaude will pull the data from GitMCP and answer the question.\n\n🛠️ Tools\n\nGitMCP provides AI assistants with several valuable tools to help them access, understand, and query GitHub repositories.\n\nfetch_ _documentation\n\nThis tool gets the primary documentation from a GitHub repository. It works by retrieving relevant documentation (e.g., llms.txt). This gives the AI a good overview of what the project is about\n\n**When it\'s useful:** For general questions about a project\'s purpose, features, or how to get started\n\nsearch_ _documentation\n\nThis tool lets the AI search through a repository\'s documentation by providing a specific search query. Instead of loading all the documentation (which could be very large), it uses intelligent search to find just the relevant parts.\n\n**When it\'s useful:** For specific questions about particular features, functions, or concepts within a project\n\nfetch_url_content\n\nThis tool helps the AI get informati', 'readme_raw_len': 12214, 'one_line_zh': '通用远程服务器，为任意 GitHub 仓库或项目提供文档', 'one_line_plain': 'Provide documentation for any GitHub repository or project from a generic remote server'}, {'id': 1441, 'key': 'haris-musa-excel-mcp-server', 'kind': 'mcp', 'name': 'haris-musa/excel-mcp-server', 'one_line': '🐍 🏠 - Excel workbook creation, data operations, formatting, charts, pivot tables and formulas', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Excel MCP server: let AI (Claude, Cursor, Codex) create and edit Excel files with charts, PivotTables, slicers and formulas. No Excel required.', 'url': 'https://github.com/haris-musa/excel-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/haris-musa/excel-mcp-server', 'downloads': None, 'stars': 4248, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/haris-musa.png?size=64', 'official_desc': '🐍 🏠 - Excel workbook creation, data operations, formatting, charts, pivot tables and formulas.', 'stars_own': 4248, 'stars_creator': None, 'stars_own_repo': 'haris-musa/excel-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Excel MCP Server\n\nAn open-source Model Context Protocol (MCP) server that lets\nClaude, Cursor, VS Code Copilot, Codex and other AI clients create, read and edit Excel files:\ncharts, PivotTables, formulas, tables and more. It is built on openpyxl, needs **no Microsoft\nExcel**, and runs on Windows, macOS and Linux.\n\n**Install and try it at excelmcpserver.com**, or see\nQuick start below.\n\nFeatures\n\n**Charts and PivotTables**\n- Create Excel charts with AI: column, bar, line, area, pie, doughnut, radar, scatter and\n bubble, with combos, secondary axes, trendlines and error bars\n- The Excel 2016 chart types: waterfall, histogram, Pareto, box and whisker, treemap, sunburst\n and funnel\n- Real PivotTables that Excel refreshes (grouping, calculated fields, "show values as",\n layouts, filters), plus slicers and timelines for PivotTables and tables\n- Sparklines (line, column, win/loss)\n\n**Formulas and dynamic arrays**\n- Formulas are calculated without Excel by a built-in engine (about 260 functions, checked\n against more than 2,500 formulas recorded from real Excel), so read_range returns values\n for files that were never opened in Excel\n- Dynamic arrays (FILTER, SORT, UNIQUE, SEQUENCE, LET) are stored as Excel stores\n them and spill into the cells next to them\n\n**Data, tables and rules**\n- Read and write cells, dates and hyperlinks; tables with totals rows and calculated columns\n- Conditional formatting (scales, data bars, icon sets, top/bottom, duplicates, text, dates),\n data validation (dropdowns, limits, input messages)\n- Sort, filter (with criteria), find and replace, remove duplicates, text to columns, fill\n series, paste special\n- Fonts, fills, borders, number formats, merged cells, frozen panes, grouping, print setup,\n sheet protection, images, notes and defined names. Inserting or deleting rows and columns\n updates every reference, as in Excel\n\n**Large files**\n- Reads stream the workbook and return pages, so memory stays flat on sheets with hundreds of\n thousands of rows; every call is bounded by size and cell limits\n\n**Macros (opt-in)**\n- Read the VBA code in .xlsm files. Writing VBA is off unless you start the server with\n --allow-vba-write; the server never runs macros\n\n**Safe by design**\n- Optional folder confinement, a formula safety check, read-only mode, localhost-only HTTP by\n default, and atomic saves that never leave a half-written file\n\nWorks with .xlsx, .xlsm (macros are preserved), .xltx and .xltm files. Existing\nthreaded comments, shapes, form controls and other content the tools cannot edit are kept as\nthey are.\n\nQuick start\n\nYou need uv. Every client runs the\nserver with uvx excel-mcp-server stdio; replace /path/to/workbooks with the folder the\nserver may use. excelmcpserver.com has the same steps with copy\nbuttons and one-click install links for Cursor and VS Code.\n\n**Claude Desktop (Chat)**: download\nexcel-mcp-server.mcpb\nfrom the latest release and open it. Claude asks which folder the server may use.\n\n**Claude Code** (the CLI, and the Code tab in Claude Desktop):\n\n \n\n**Cursor** (~/.cursor/mcp.json), and most clients that use an mcpServers config:\n\n \n\n**VS Code with GitHub Copilot** (.vscode/mcp.json, note the servers key):\n\n \n\n \n OpenAI Codex, Gemini CLI, Devin Desktop, and Claude Desktop without the bundle \n\n**OpenAI Codex** (CLI, IDE extension and app):\n\n \n\n**Gemini CLI**:\n\n \n\n**Devin Desktop**:\n\n \n\n**Claude Desktop, manual setup**: open Settings, Developer, Edit Config, add the mcpServers\nJSON above to claude_desktop_config.json, and restart Claude.\n\n \n\nDesktop apps often cannot find uvx, because they do not see your shell\'s PATH (common\non macOS). Use its full path instead, which which uvx prints.\n\nChoosing which files it can use\n\nWith --allow-dir DIR, the server only opens workbooks inside DIR (subfolders included)\nand relative paths such as reports/q1.xlsx start there. Repeat the flag to allow several\nfolders, or set EXCEL_FILES_PATH (separate folders with : on macOS/Linux and ; on\nWindows).\n\nWithout --allow-dir, any absolute path to an Excel file works. In every mode the server\nonly touches Excel files and never silently overwrites an existing workbook.\n\nRemote use (Streamable HTTP)\n\n \n\nClients connect to http://127.0.0.1:8017/mcp. Workbooks live in the --allow-dir folder\n(default ./excel_files), and export_workbook / import_workbook move files between the\nserver and the client.\n\nThe server listens on localhost only. To accept other machines, set a token and a host:\n\n \n\nClients then send Authorization: Bearer change-me. Put a TLS-terminating reverse proxy in\nfront of it for use across networks.\n\nDocker\n\n \n\nConfiguration\n\n| Flag | Environment variable | Default | Meaning |\n| --- | --- | --- | --- |\n| --allow-dir DIR | EXCEL_FILES_PATH | none (stdio), ./excel_files (HTTP) | Folders workbooks must be in |\n| --read-only | EXCEL_MCP_READ_ONLY=1 | off | Only offer tools that do not change files |\n| --allow-vba-write | EXCEL_MCP_ALLOW_VBA_WRITE=1 | off | Add tools that write VBA macros (see warning below); ignored with --read-only |\n| --max-file-mb N | | 100 | Largest workbook the server opens. Also sets the unpacked-size limit (10 times this) that every opened or uploaded workbook must stay under; parts compressed over 500:1 are refused |\n| --log-level LEVEL | | WARNING | Logging on stderr |\n| --host HOST | EXCEL_MCP_HOST | 127.0.0.1 | HTTP listen address |\n| --port PORT | EXCEL_MCP_PORT | 8017 | HTTP port |\n| | EXCEL_MCP_AUTH_TOKEN | none | Bearer token required on HTTP requests |\n| --allow-unauthenticated | | off | Allow a non-local --host without a token |\n\nTools\n\n| Area | Tools |\n| --- | --- |\n| Workbooks | create_workbook, describe_workbook, set_workbook_settings, list_workbooks, export_workbook, import_workbook |\n| Sheets | describe_sheet, create_sheet, rename_sheet, copy_sheet, delete_sheet, insert_rows_or_columns, delete_rows_or_columns |\n| Cells | read_range, write_range, clear_range, copy_range, sort_range, transform_range, find_cells, replace_cells |\n| Formatting | format_range, merge_cells, set_sheet_layout, add_conditional_format, add_data_validation |\n| Objects | set_table, create_chart, delete_chart, create_pivot_table, delete_pivot_table, add_slicer, delete_slicer, insert_image, delete_image, add_sparklines, delete_sparklines |\n| Names and notes | set_defined_name, delete_defined_name, set_note, delete_note |\n| Macros | read_vba; with --allow-vba-write: write_vba_module, delete_vba_module |\n\nEvery parameter is documented in TOOLS.md. Parameters mean the same in every tool:\n\n| Parameter | Meaning |\n| --- | --- |\n| path, sheet | The workbook and the worksheet |\n| range, at | A block of cells (A1:D20), and the top-left cell of whatever is placed there: written values, copies, charts, images, PivotTables, slicers |\n| source | Where data comes from, optionally on another sheet (Data!A1:E200) |\n| name | The name of a table, chart, image, PivotTable, slicer or defined name |\n| _cm, _pt, _chars | Units of sizes: width_cm, row_heights_pt, column_widths_chars |\n\nTools that change a workbook return what they changed (sheet, range, name) so that the\nnext call can refer to it. Charts and images are selected by name, as listed by describe_sheet.\n\nWriting macros (opt-in)\n\nWith --allow-vba-write (or EXCEL_MCP_ALLOW_VBA_WRITE=1) the server can set the code of\nstandard, class, workbook and sheet modules in .xlsm and .xltm files, delete standard and\nclass modules, and create new .xlsm/.xltm workbooks. The server only stores the code; it\nnever runs it, and Excel asks before enabling macros. Digitally signed VBA projects are\nrefused, since any change would invalidate the signature.\n\n> **Warning:** macro code runs with your user\'s rights once you enable macros in Excel. A\n> model that reads untrusted content could be tricked into writing harmful code. Enable this\n> option only for trusted workflows, keep --allow-dir narrow, and **read the code before\n> you enable macros**. The tools are never offered in --read-only mode.\n\nSecurity\n\n- Fo', 'readme_raw_len': 13970, 'one_line_zh': '创建 Excel 工作簿，支持数据操作、格式设置、图表、数据透视表和公式', 'one_line_plain': 'Create Excel workbooks with data operations, formatting, charts, pivot tables, and formulas'}, {'id': 5317, 'key': 'evalstate-fast-agent', 'kind': 'repo', 'name': 'evalstate/fast-agent', 'one_line': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'url': 'https://github.com/evalstate/fast-agent', 'source': 'github', 'source_url': 'https://github.com/evalstate/fast-agent', 'downloads': None, 'stars': 3943, 'installs': None, 'mentions': 1, 'score': 110.86, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-19', 'times_seen': 18, 'icon': 'https://github.com/evalstate.png?size=64', 'official_desc': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'stars_own': 3943, 'stars_creator': None, 'stars_own_repo': 'evalstate/fast-agent', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'uvx fast-agent-mcp@latest', 'install_src': 'HEAD/README.md', 'install_conf': 'medium', 'desc_src': 'github-api', 'readme_body': 'Start Here\n\n> [!TIP]\n> Please see https://fast-agent.ai for latest documentation.\n\n**fast-agent** is a flexible way to interact with LLMs, excellent for use as a Coding Agent, Development Toolkit, Evaluation or Workflow platform.\n\nTo start an interactive session with shell support, install uv and run\n\n \n\nTo start coding with Hugging Face inference providers or use your OpenAI Codex plan:\n\n \n\nEnter a shell with !, or run shell commands e.g. ! cd web && npm run build.\n\nManage skills with the /skills command, and connect to MCP Servers with /connect. The default **fast-agent** registry contains skills to let you set up LSP, Agent and Tool Hooks, Compaction strategies, Automation and more.\n\n \n\nIt\'s recommended to install **fast-agent** to set up the shell aliases and other tooling.\n\n \n\nUse local models with the generic provider, or automatically create the correct configuration for llama.cpp:\n\n \n\nAny **fast-agent** setup or program can be used with any ACP client - the simplest way is to use fast-agent-acp:\n\n \n\n**fast-agent** enables you to create and interact with sophisticated multimodal Agents and Workflows in minutes. It is the first framework with complete, end-to-end tested MCP Feature support including Sampling and Elicitations.\n\nfast-agent is CLI-first, with an optional prompt_toolkit-powered interactive terminal prompt (TUI-style input, completions, and in-terminal menus); responses can stream live to the terminal via rich without relying on full-screen curses UIs or external GUI overlays.\n\n \n\nThe simple declarative syntax lets you concentrate on composing your Prompts and MCP Servers to build effective agents.\n\nModel support is comprehensive with native support for Anthropic, OpenAI and Google providers as well as Azure, Ollama, Deepseek and dozens of others via TensorZero. Structured Outputs, PDF and Vision support is simple to use and well tested. Passthrough and Playback LLMs enable rapid development and test of Python glue-code for your applications.\n\nRecent features include:\n\n- Agent Skills (SKILL.md)\n- MCP Apps\n- OpenAI Apps SDK\n- Shell Mode\n- Advanced MCP Transport Diagnsotics\n- MCP Elicitations\n\n \n\nfast-agent is the only tool that allows you to inspect Streamable HTTP Transport usage - a critical feature for ensuring reliable, compliant deployments. OAuth is supported with KeyRing storage for secrets. Use the fast-agent auth command to manage.\n\n> [!IMPORTANT]\n>\n> Documentation is included in this repository under docs/. Use the docs helper script from the\n> repository root to install, generate, build, serve, screenshot, and assess the site.\n\nAgent Application Development\n\nPrompts and configurations that define your Agent Applications are stored in simple files, with minimal boilerplate, enabling simple management and version control.\n\nChat with individual Agents and Components before, during and after workflow execution to tune and diagnose your application. Agents can request human input to get additional context for task completion.\n\nSimple model selection makes testing Model MCP Server interaction painless. You can read more about the motivation behind this project here\n\n \n\nGet started:\n\nStart by installing the uv package manager for Python. Then:\n\n \n\nWith no configured model, an interactive run opens the model picker.\nAutomation and server runs must pass --model, set FAST_AGENT_MODEL, configure\ndefault_model, or declare a model on the agent.\n\nFor packaged starter agents, use fast-agent go --pack --model .\nThis installs the pack into the selected fast-agent home if needed, then\nstarts go normally. --model is a fallback for cards without an explicit\nmodel setting; a model declared directly in an AgentCard still wins.\n\nOther quickstart examples include a Researcher Agent (with Evaluator-Optimizer workflow) and Data Analysis Agent (similar to the ChatGPT experience), demonstrating MCP Roots support.\n\n> [!TIP]\n> Windows Users - there are a couple of configuration changes needed for the Filesystem and Docker MCP Servers - necessary changes are detailed within the configuration files.\n\nBasic Agents\n\nDefining an agent is as simple as:\n\n \n\nWe can then send messages to the Agent:\n\n \n\nOr start an interactive chat with the Agent:\n\n \n\nHere is the complete sizer.py Agent application, with boilerplate code:\n\n \n\nThe Agent can then be run with uv run sizer.py.\n\nSpecify a model with the --model switch - for example uv run sizer.py --model sonnet.\n\nModel strings also accept query overrides. For example:\n\n- uv run sizer.py --model "gpt-5?reasoning=low"\n- uv run sizer.py --model "claude-sonnet-4-6?web_search=on"\n- uv run sizer.py --model "claude-sonnet-4-5?context=1m"\n- uv run sizer.py --model "gpt-5?poll_period=240"\n\nFor Anthropic models, ?context=1m is only needed for earlier Sonnet 4 / Sonnet 4.5\nmodels that still require the explicit 1M context opt-in. Claude Sonnet 4.6 and\nClaude Opus 4.6 already use their long context window by default, so ?context=1m\nis accepted for backward compatibility but is unnecessary there.\n\nCombining Agents and using MCP Servers\n\n_To generate examples use fast-agent quickstart workflow. This example can be run with uv run workflow/chaining.py. Place fast-agent.yaml in the active fast-agent home, or pass an explicit config path when needed._\n\nAgents can be chained to build a workflow, using MCP Servers defined in the fast-agent.yaml file:\n\n \n\nAll Agents and Workflows respond to .send("message") or .prompt() to begin a chat session.\n\nSaved as social.py we can now run this workflow from the command line with:\n\n \n\nAdd the --quiet switch to disable progress and message display and return only the final response - useful for simple automations.\n\nMAKER\n\nMAKER (“Massively decomposed Agentic processes with K-voting Error Reduction”) wraps a worker agent and samples it repeatedly until a response achieves a k-vote margin over all alternatives (“first-to-ahead-by-k” voting). This is useful for long chains of simple steps where rare errors would otherwise compound.\n\n- Reference: Solving a Million-Step LLM Task with Zero Errors\n- Credit: Lucid Programmer (PR author)\n\n \n\nAgents As Tools\n\nThe Agents As Tools workflow takes a complex task, breaks it into subtasks, and calls other agents as tools based on the main agent instruction.\n\nThis pattern is inspired by the OpenAI Agents SDK Agents as tools feature.\n\nWith child agents exposed as tools, you can implement routing, parallelization, and orchestrator-workers decomposition directly in the instruction (and combine them). Multiple tool calls per turn are supported and executed in parallel.\n\nCommon usage patterns may combine:\n\n- Routing: choose the right specialist tool(s) based on the user prompt.\n- Parallelization: fan out over independent items/projects, then aggregate.\n- Orchestrator-workers: break a task into scoped subtasks (often via a simple JSON plan), then coordinate execution.\n\n \n\nExtended example and all params sample is available in the repository as\nexamples/workflows/agents_as_tools_extended.py.\n\nMCP OAuth (v2.1)\n\nFor SSE and HTTP MCP servers, OAuth is enabled by default with minimal configuration. A local callback server is used to capture the authorization code, with a paste-URL fallback if the port is unavailable.\n\n- Minimal per-server settings in fast-agent.yaml:\n\n \n\n- The OAuth client uses PKCE and in-memory token storage (no tokens written to disk).\n- Token persistence: by default, tokens are stored securely in your OS keychain via keyring. If a keychain is unavailable (e.g., headless container), in-memory storage is used for the session.\n- To force in-memory only per server, set:\n\n \n\n- To disable OAuth for a specific server , set auth.oauth: false for that server.\n\nMCP Ping (optional)\n\nThe MCP ping utility can be enabled by either peer (client or server). See the Ping overview.\n\nClient-side pinging is configured per server (default: 30s interval, 3 missed pings):\n\n \n\n**Global tools (@fast.tool)** — available to all agents that don\'t declare their own tools:\n\n \n\nAgen', 'readme_raw_len': 10194, 'one_line_zh': '编写、构建与评估 Agent，出色的 Model 与 Skills/MCP/ACP/A2A 支持', 'one_line_plain': 'Create, run and test code agents with support for Model, Skills, MCP, ACP and A2A protocols'}, {'id': 4118, 'key': 'taylorwilsdon-google-workspace-mcp', 'kind': 'mcp', 'name': 'taylorwilsdon/google_workspace_mcp', 'one_line': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace integration covering Calendar, Drive, Gmail, Docs, Forms, Chat, Slides and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search & Drive with AI - Comprehensive Google Workspace MCP Server & CLI Tool', 'url': 'https://github.com/taylorwilsdon/google_workspace_mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/taylorwilsdon/google_workspace_mcp', 'downloads': None, 'stars': 3376, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/taylorwilsdon.png?size=64', 'official_desc': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace integration covering Calendar, Drive, Gmail, Docs, Forms, Chat, Slides and Sheets.', 'stars_own': 3376, 'stars_creator': None, 'stars_own_repo': 'taylorwilsdon/google_workspace_mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Google Workspace MCP Server \n\n \n \n \n \n \n \n\n \n\n*Full natural language control over Google Calendar, Drive, Gmail, Docs, Sheets, Slides, Forms, Tasks, Contacts, and Chat through all MCP clients, AI assistants and developer tools.*\n*Includes a full featured CLI & Code Mode for use with tools like Claude Code and Codex!*\n\n**The most feature-complete Google Workspace MCP server** is in a class of it\'s own: it can do things that Google\'s own tooling and the built in integrations with Claude and ChatGPT can\'t come close to with multi-user support, rich fine-grained editing tools and the most extensive coverage of any Workspace AI integration in existence. \n\nBy leveraging native OAuth 2.1, stateless deployment capability and external auth server & gateway passthrough auth support, it\'s also the only Workspace MCP you can host for your whole organization centrally & securely!\n\nSupports all free Google accounts & Google Workspace plans with expanded app options like Chat & Spaces. Interested in a managed cloud instance? That can be arranged (starting at $5/mo).\n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n---\n\n**See it in action:**\n \n \n \n\n---\n\nWhat It Does\n\nWorkspace MCP connects AI assistants to all twelve major Google Workspace services - 120+ tools behind a single MCP server, with OAuth 2.1 multi-user auth, three progressive tool tiers, read-only mode, a full CLI, and stateless container deployment. It runs locally over stdio for legacy clients and remotely over streamable HTTP with full implementation of the latest MCP spec.\n\nThe README covers just enough to get you running, with extensive documentation on the website:\n\n| Where to go | What you\'ll find |\n|:---|:---|\n| **Quick Start** | Google Cloud setup, credentials, and client connection with screenshots |\n| **Full Documentation** | Every tool, parameter, and auth mode |\n| **Advanced Deployment** | Reverse proxy & nginx config, origin validation, credential store backends (GCS/CMEK), trusted-gateway identity, and the complete environment variable reference |\n| **Client Setup Guides** | Claude Desktop/web Connectors, ChatGPT Developer Mode, and more |\n| **FAQ & Troubleshooting** | Enabling Google APIs, OAuth errors, redirect URIs, Google Chat setup, client quirks |\n\nSecurity & Compliance \n\n \n \n \n\n**For Security Teams**\n\nBy default, this server sends no data anywhere except Google\'s APIs, on behalf of the authenticated user, using your own OAuth client credentials. There is no usage reporting, analytics, license server, or SaaS dependency outside optional OTel support for your own usage.\n\n- **Fully open source** — every line is auditable in this repo\n- **Your OAuth client, your GCP project** — credentials never leave your environment & you control scopes\n- **You control the network** — deploy behind your reverse proxy, in your VPC, on your own terms\n- **Stateless mode** — zero disk writes for locked-down container environments\n- **Sensitive path blocking** — local file reads default to the managed attachment directory, and validate_file_path() still blocks .env* files plus common home-directory credential stores such as ~/.ssh/ and ~/.aws/ even if ALLOWED_FILE_DIRS is broadened\n\nFull dependency tree in pyproject.toml, pinned in uv.lock.\n\n \n \n\n**For Legal & Procurement**\n\nThis project is MIT licensed — not "open core," not "source available," not "free with a CLA." There is no dual licensing, no commercial tier gating features, and no contributor license agreement.\n\n- **Use commercially without restriction** — build products, sell services, deploy internally\n- **Fork, embed, redistribute** — MIT requires only attribution\n- **No CLA** — contributions remain under MIT\n- **No built-in telemetry to disclose** — optional tracing is off unless you configure it\n- **No network effects** — the server never contacts any endpoint you didn\'t configure\n- **Standard dependency licenses** — MIT, Apache 2.0, and BSD throughout the dependency chain; no copyleft, no AGPL\n \n \n \n\nServices\n\n \n \n \n 📧 Gmail \n 15 tools - search, send, draft, labels, filters, attachments \n \n \n 📁 Drive \n 16 tools - search, create, share, import Office files \n \n \n 📅 Calendar \n 7 tools - events, free/busy, Out of Office, Focus Time \n \n \n 📝 Docs \n 19 tools - edit, style, tables, tabs, comments, export \n \n \n \n \n 📊 Sheets \n 14 tools - ranges, tables, formatting, conditional rules \n \n \n 🖼️ Slides \n 7 tools - create, batch update, thumbnails, comments \n \n \n 📋 Forms \n 6 tools - build forms, publish, read responses \n \n \n ✅ Tasks \n 6 tools - tasks & lists with hierarchy \n \n \n \n \n 👤 Contacts \n 8 tools - people, groups, batch operations \n \n \n 💬 Chat \n 6 tools - spaces, messages, search, reactions \n \n \n 🔍 Custom Search \n 2 tools - programmable web search \n \n \n ⚡ Apps Script \n 15 tools - write, deploy, run & debug scripts \n \n \n \n\nEach page lists every tool with its tier, parameters, required scopes, and example prompts. The complete reference covers all twelve in one place.\n\n> 💬 **Google Chat** needs a one-time Chat app configuration and a Workspace account - see the Chat setup FAQ.\n\nQuick Start\n\n> Set credentials → pick a launch command → connect your client. Full walkthrough with screenshots: **workspacemcp.com/quick-start**\n\nYou\'ll need an OAuth client from Google Cloud Console in a project with the Google APIs enabled for the services you plan to use. The docs have one-click enable links for every API plus a single gcloud services enable command that covers them all, and the quick start guide walks through the whole setup in about five minutes.\n\n \n \n \n\n**Confidential Client**\n\n \n\n \n \n\n**OAuth 2.1 (PKCE)**\n\n \n\n \n \n \n\n**Tool tiers** keep context windows lean: core is the essential set, extended adds management operations, complete loads everything. Combine with --tools ..., --read-only, or per-service --permissions, and subtract individual tools with --disabled-tools ... - details in the server modes docs.\n\nConnect Your Client\n\n**Claude Desktop, web & mobile** - run the server in HTTP mode and add it as a **Connector** (Settings → Connectors → Add custom connector). This is the recommended path; the Connector guide has step-by-step screenshots. Legacy stdio configuration remains available for clients without Connector support - see the FAQ.\n\n**Claude Code**\n\n \n\n**ChatGPT** - connect via Developer Mode with the ChatGPT guide.\n\n**VS Code, LM Studio, Open WebUI, and everything else** - any MCP client works over streamable HTTP (recommended) or stdio. Client-specific walkthroughs live in the guides and FAQ.\n\nCLI\n\nworkspace-cli lists and calls tools against a running server with encrypted, disk-backed OAuth token caching - authenticate once, script forever:\n\n \n\nInstall globally with uv tool install . from this repo. ⚠️ Don\'t use uvx workspace-cli - an abandoned PyPI package squats that name.\n\nDeployment & Advanced Configuration\n\nEverything you need to run this in production lives in two places. The documentation covers auth modes and server configuration:\n\n- **OAuth 2.1 multi-user auth** - bearer tokens, required for remote or shared HTTP endpoints\n- **Stateless container mode** - zero disk writes for locked-down deployments\n- **OAuth proxy storage backends** - memory, disk, or Valkey/Redis for distributed setups\n- **External OAuth provider mode** - bring your own auth server, validate bearer tokens only\n- **Service accounts with domain-wide delegation** - per-request user impersonation with an optional domain allowlist\n- **Trusted-gateway identity** - proxy-verified per-user isolation with Pomerium, Cloudflare Access, oauth2-proxy, or any JWKS-verifiable gateway\n- **OpenTelemetry tracing** - optional, off unless you configure an OTLP endpoint\n- **Docker** - docker build -t workspace-mcp . && docker run -p 8000:8000 workspace-mcp\n\nThe **Advanced Deployment guide** covers self-hosting specifics: reverse proxy setup with WORKSPACE_EXTERNAL_URL (including the nginx Origin: null consent workaround, the WORKSPACE_MCP_ALLOW_NULL_ORIGIN_CONSENT escape', 'readme_raw_len': 11461, 'one_line_zh': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace 集成，覆盖 Calendar、Drive、Gmail、Docs、Forms、Chat、Slides 等', 'one_line_plain': 'Access Google Workspace tools including Calendar, Drive, Gmail, Docs, Forms, Chat, and Slides'}, {'id': 5242, 'key': 'xiaomacoltai-math-modeling-skill', 'kind': 'repo', 'name': 'XiaoMaColtAI/math-modeling-skill', 'one_line': '数学建模技能 - 面向 CUMCM、MCM/ICM 等数学建模竞赛的三阶段工作流：建模分析、Python/MATLAB 编程与 DOCX 论文生成', 'one_line_ok': 1, 'why_bad': None, 'fields': '["Claude 技能"]', 'desc_raw': '数学建模技能 - 面向 CUMCM、MCM/ICM 等数学建模竞赛的三阶段工作流：建模分析、Python/MATLAB 编程与 DOCX 论文生成。包含丰富的算法资源库(优化/预测/评价/图论/机器学习等)、角色指导文档、论文模板和实用工具脚本', 'url': 'https://github.com/XiaoMaColtAI/math-modeling-skill', 'source': 'github', 'source_url': 'https://github.com/XiaoMaColtAI/math-modeling-skill', 'downloads': None, 'stars': 1876, 'installs': None, 'mentions': 1, 'score': 106.72, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-19', 'times_seen': 19, 'icon': 'https://github.com/XiaoMaColtAI.png?size=64', 'official_desc': '数学建模技能 - 面向 CUMCM、MCM/ICM 等数学建模竞赛的三阶段工作流：建模分析、Python/MATLAB 编程与 DOCX 论文生成。包含丰富的算法资源库(优化/预测/评价/图论/机器学习等)、角色指导文档、论文模板和实用工具脚本', 'stars_own': 1876, 'stars_creator': None, 'stars_own_repo': 'XiaoMaColtAI/math-modeling-skill', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx skills', 'install_src': 'HEAD/README.md', 'install_conf': 'low', 'desc_src': 'github-api', 'readme_body': '🎯 Math Modeling Skill\n\n \n\n**面向数学建模竞赛与建模项目的三阶段工作流**\n\n \n \n\n**关注我**\n\n \n \n \n\n \n\n---\n\n📖 简介\n\n本 Skill 将数学建模任务拆分为 **建模分析 → 代码实现 → 论文撰写** 三个阶段。既可以按顺序完成整道题，也可以只执行其中一个阶段。\n\n当前版本：1.3.0\n\n> 生成的论文仅供参考。论文结构与格式必须以目标竞赛当届官方规则和官方模板为准。\n\n✨ 核心能力\n\n- 🧠 **建模分析**：读题、检查附件、拆分子问题、选择模型、设计求解与验证方案。\n- 💻 **双语言实现**：支持 Python 和 MATLAB，按选中的模型与功能动态检查依赖。\n- 📊 **完整结果输出**：生成结果表格、原始数据图、模型运行过程图和最终结果图，以及必须的总体建模流程图。\n- 🎨 **出版级科学可视化**：先剖析数据和论证目标再选图，提供 Python/MATLAB 统一样式、色觉友好编码、SVG + 300 DPI PNG 导出与成图自检闭环。\n- 🔁 **可复现运行**：记录随机种子、输入文件 SHA-256、运行时与依赖版本、关键参数和唯一复现命令。\n- 🔎 **双引擎论文搜索**：并行调用 OpenAlex 与 AnySearch，按 DOI 或题名交叉核验。\n- 📄 **Word / LaTeX 论文生成**：支持官方模板、嵌套主入口、整篇 LaTeX→DOCX、Word 原生 OMML 公式、真实 PDF 编译、权威资源—源码—产物哈希绑定和完整质量门禁。默认只生成 Word 论文，LaTeX 可选。\n- 🧾 **论文组件规范**：使用“符号、含义、单位”三列符号说明表和标准数学排版，按当届规则组织附录。\n- 🛡️ **阶段内独立质检**：默认只在建模终检、最小可运行结果、编程终检、论文证据大纲和论文终检节点派发只读 Subagent，发现问题立即返工复验。\n- 🤝 **可选 Subagent 协作**：用户可按需启用规则核验、附件盘点、文献与模型调研、算法原型、独立实验、双语言对照或术语核验；默认全部关闭。\n- 🧩 **渐进式加载**：只读取当前阶段需要的角色规范、算法资料和工具说明。\n\n🔄 三阶段工作流\n\n \n \n \n\n| 阶段 | 角色 | 核心任务 | 独立门禁 | 固定交付物 |\n|:---:|---|---|---|---|\n| ① | 建模手 | 理解题目、设计模型、定义算法和验证方案 | M1 建模终检 | 题目分析报告.md、术语表格.md |\n| ② | 编程手 | 编写并运行 Python/MATLAB，生成结果与图 | P1 最小可运行结果、P2 编程终检 | 代码、结果表格、三类各至少 3 张且覆盖全部子问题的候选图、至少 1 幅总体建模流程图、results/复现清单.json |\n| ③ | 论文手 | 基于真实结果构建论证并生成 Word 论文 | W1 证据大纲、W2 论文终检 | 至少 8 幅且覆盖全部子问题的正式图；默认交付 完整论文.docx；用户显式要求时同时交付 LaTeX 源码项目、PDF 与哈希清单 |\n\n质检 Subagent 是阶段内只读验收者，不是第四个固定角色。默认只启用固定质检；其他协作仅在用户明确选择后运行。P1 在全量计算和正式出图前执行，W1 在长篇正文和双格式排版前执行；禁止等全流程结束后才首次质检。完整协议见 Subagent 调度与阶段门禁。\n\n阶段反馈\n\n- 编程手发现公式、约束或参数无法实现时，携带实际报错返回建模手修正。\n- 论文手发现关键结论缺少真实结果、图表或文献支撑时，返回对应阶段补齐。\n- 任一独立门禁返回 FAIL 时，由原阶段执行者按证据修正并重新派发复验；主 Agent 不得自行覆盖失败结论。\n- 修正后从被阻断阶段继续，不重复已经通过的阶段。\n\n🚀 快速开始\n\n推荐 Agent\n\n本 Skill 可用于支持本地 Skills 或 Agent 工作流的工具，例如 Claude Code、Codex、Cursor、Trae 和 Qoder。具体加载方式以对应工具的当前文档为准。\n\nDeepSeek Harness 插件\n\n本仓库同时提供 DeepSeek Harness（dsh）的 **Agent 预设**：dsh-plugin/math-modeling-agent/，把三阶段工作流、五门禁质检、任务看板与完成判定封装为 mm_* 工具，供 dsh 桌面端使用。\n\n**安装**：把整个 dsh-plugin/math-modeling-agent/ 目录复制到本机 dsh 预设根目录（ \\.agent-presets\\，Windows 默认 C:\\Users\\ \\AppData\\Roaming\\dsh-desktop\\dsh-home\\.agent-presets），目录名即预设 id（如 math-modeling）。也可直接复制 dsh-plugin/README.md 中附带的安装提示词给 dsh Agent 自动完成安装。\n\n**使用**：新建 dsh 会话 → 选择预设「数学建模 Workbench」，即可使用 mm_project_init / mm_phase_enter / mm_todo / mm_gate / mm_check_deliverables / mm_complete / mm_state 等工具，并通过 skill 工具加载内置 math-modeling 知识库。\n\n> 插件为**自包含**设计：知识库随预设持久化，不依赖外部仓库路径，可整体复制到任意机器使用。\n\n安装\n\nGit 克隆\n\n \n\n克隆后，将仓库放入所用 Agent 的 Skills 目录或按其方式加载本目录。\n\nnpx 安装\n\n \n\n也可以下载仓库 ZIP，解压后放入对应 Skills 目录。\n\n使用示例\n\n完整流程：\n\n \n\n单阶段执行：\n\n \n\n主入口见 SKILL.md。\n\n📁 工作目录约定\n\n- SKILL_ROOT：本仓库根目录，只读；角色规范、算法资料、脚本和模板从这里读取。\n- PROJECT_ROOT：用户题目所在目录；所有运行产物只写入这里。\n- 题目与附件保持只读；需要修改模板时，先复制到 PROJECT_ROOT。\n\n典型产物结构：\n\n \n\n🛠️ 集成工具\n\n| 工具 | 用途 |\n|---|---|\n| 科研可视化 | 数据剖析、选图决策、Nature/SCI 出版级绘制、建模流程图、自检闭环、多格式导出 |\n| 双引擎论文搜索 | OpenAlex + AnySearch 搜索、融合和交叉核验 |\n| DOCX 工具 | 官方模板、递归 LaTeX→DOCX、警告发布门禁、OMML 公式、三线表、修订、批注和校验 |\n| LaTeX 工具 | 环境诊断、官方模板溯源、真实编译、哈希绑定、引用与 PDF 质量校验 |\n| Excel 工具 | XLSX 模板处理、公式重算和错误检查 |\n| PDF 工具 | 读取题目 PDF，提取文本、表格和图片 |\n\n双引擎论文搜索\n\n \n\n- OpenAlex 可通过 --email 提供礼貌池邮箱。\n- AnySearch 需要密钥时设置环境变量 ANYSEARCH_API_KEY。\n- 正式检索默认同时运行两个引擎；单引擎参数只用于诊断。\n\n动态依赖检查\n\nPython 只检查实际需要的功能：\n\n \n\nMATLAB 使用：\n\n \n\n🧮 算法资料\n\n算法资料覆盖七类问题：\n\n| 类别 | 代表方向 |\n|---|---|\n| 优化 | 线性、整数、非线性、多目标和启发式优化 |\n| 预测 | 灰色预测、时间序列、回归和机器学习预测 |\n| 评价 | AHP、TOPSIS、熵权、灰色关联和 DEA |\n| 图论 | 最短路、网络流、生成树和匹配 |\n| 统计 | 检验、聚类、降维和多元统计 |\n| 综合 | 蒙特卡洛、排队、博弈、马尔科夫和微分方程 |\n| 机器学习 | 随机森林、集成学习和异常检测 |\n\n先读取 算法索引，再按问题类型加载对应资料。每道子问题最多使用两个独立模型体系；物理题中同一机理的基础近似与高精度展开按一个模型族计数。\n\n📄 论文生成\n\n默认只生成 Word 论文；用户显式要求时同时生成 LaTeX/PDF 论文。当届官方提交要求仍决定实际可提交的版本。\n\n- **Word**：从官方模板构建，LaTeX 严格转换为原生 OMML 公式，校验篇幅、公式、图表、编号引用、参考文献、DOCX 结构与渲染页数；已有 LaTeX 主稿可用 Pandoc 整篇转换。\n- **LaTeX（可选）**：完整复制官方模板项目并记录哈希，绑定权威代码/图表后真实编译 PDF，校验资源/源码/PDF 哈希、空白页、页面尺寸、字体嵌入与图片 DPI。\n- 没有官方模板时才使用内置构建基线；同时生成两种格式时必须使用相同的数据、图表、公式、参考文献和结论。\n- CUMCM 默认以约 15000 字词单位、约 20 页作为完整度质量目标（非官方要求）；所有竞赛采用相同的至少 8 幅正式图质量基线，每个子问题至少一幅正式结果图。\n\n完整流程与门禁见 tools/docx/SKILL.md、tools/latex/SKILL.md。\n\n📸 示例展示\n\n以下图表展示本项目可视化规范生成的候选图效果。\n\n2025 年国赛 A 题：烟幕干扰弹的投放策略\n\n \n \n \n 投放方案对比与关键参数分析 \n \n \n \n Pareto 前沿与策略效果对比 \n \n\n2025 年国赛 B 题：碳化硅外延层厚度的确定\n\n \n \n \n 厚度拟合结果与方法对比 \n \n \n \n 误差分布与预测一致性分析 \n \n\n🏆 适用场景\n\n工作流可用于 CUMCM、MCM/ICM、APMCM、MathorCup、认证杯、数维杯等数学建模竞赛和一般建模项目。不同竞赛的页面、摘要、编号、页数和提交格式必须按当届官方要求配置。\n\n📂 仓库结构\n\n \n\n✅ 验证\n\n \n\n回归测试覆盖双引擎搜索、公式转换、DOCX、LaTeX 模板与校验、Excel 重算、论文结构、动态依赖、复现清单和科学绘图工具。\n\n📋 版本与更新日志\n\n当前版本：1.3.0\n\n1.3.0 完善算法资料与建模方法论，新增评阅人抓分方法论、求解稳健性、机器学习算法扩充，并补充交付与截止时间保护协议与 DeepSeek Harness 插件支持。详细内容见 CHANGELOG.md。\n\n采用语义化版本 MAJOR.MINOR.PATCH：\n\n- MAJOR：固定交付物、目录契约、命令参数或数据结构发生不兼容变化。\n- MINOR：增加向后兼容的新能力。\n- PATCH：向后兼容的错误修复、文档校正或测试补充。\n\n完整记录见 CHANGELOG.md。\n\n⭐ GitHub Star 历史\n\n \n\n \n\n该图每日读取 GitHub 官方累计 Star 数自动更新；画布为 16:9，纵轴每 50 Star 一格，并在当前数值上方保留一个完整刻度。\n\n \n\n🙏 致谢\n\n- AnySearch Skill：为学术垂直搜索提供参考。\n- Nature Skills：为科学可视化与写作方法提供参考。\n- SciPilot Figure Skill：为数据剖析、图型决策、色觉可达性和成图自检闭环提供参考。\n\n---\n\n \n\n**算法索引 · 使用文档 · 角色说明 · 更新日志**', 'readme_raw_len': 4790, 'one_line_zh': None, 'one_line_plain': None}, {'id': 5749, 'key': 'skillsmp-com-creators-daymade-claude-code-skills-frontend-visual-qa', 'kind': 'skill', 'name': 'frontend-visual-qa', 'one_line': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'downloads': None, 'stars': 1452, 'installs': None, 'mentions': 5, 'score': 503.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/daymade.png?size=64', 'official_desc': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard, design-system, or Electron/native app — via real-browser/native-app journeys and a Playwright sweep. Use after UI implementation to find typography, overflow, responsive, routing, data-viz, browser-output', 'stars_own': None, 'stars_creator': 1477, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'daymade/claude-code-skills', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '审计已渲染的界面，以及 Markdown 与 Obsidian 中的图片排版。', 'one_line_plain': 'The tool audits already-rendered UI and Markdown/Obsidian image layout for web apps, decks, dashboards, and more'}, {'id': 988, 'key': 'softeria-ms-365-mcp-server', 'kind': 'mcp', 'name': 'softeria/ms-365-mcp-server', 'one_line': '📇 ☁️ - Connect to Microsoft Office and the Microsoft 365 suite via the Graph API, including Outlook mail,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol (MCP) server for interacting with Microsoft 365 and Microsoft Office services through the Graph API', 'url': 'https://github.com/softeria/ms-365-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/softeria/ms-365-mcp-server', 'downloads': None, 'stars': 1058, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/softeria.png?size=64', 'official_desc': '📇 ☁️ - Connect to Microsoft Office and the Microsoft 365 suite via the Graph API, including Outlook mail, files, Excel and calendar.', 'stars_own': 1058, 'stars_creator': None, 'stars_own_repo': 'softeria/ms-365-mcp-server', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'ms-365-mcp-server\n\n \n\nMicrosoft 365 MCP Server\n\nA Model Context Protocol (MCP) server for interacting with Microsoft 365 and Microsoft Office services through the Graph\nAPI.\n\nSupported Clouds\n\nThis server supports multiple Microsoft cloud environments:\n\n| Cloud | Description | Auth Endpoint | Graph API Endpoint |\n| -------------------- | ---------------------------------- | ------------------------- | ------------------------------- |\n| **Global** (default) | International Microsoft 365 | login.microsoftonline.com | graph.microsoft.com |\n| **China** (21Vianet) | Microsoft 365 operated by 21Vianet | login.chinacloudapi.cn | microsoftgraph.chinacloudapi.cn |\n\nTo route Graph traffic through a proxy of your own (one that terminates the client\'s plain HTTP and originates TLS to Microsoft), set MS365_MCP_GRAPH_BASE_URL to its base URL; see the environment variable list under CLI Options, including what still bypasses it.\n\nPrerequisites\n\n- Node.js >= 20 (recommended)\n- Node.js 14+ may work with dependency warnings\n\nFeatures\n\n- Authentication via Microsoft Authentication Library (MSAL)\n- Comprehensive Microsoft 365 service integration\n- Read-only mode support for safe operations\n- Tool filtering for granular access control\n- Tool presets and dynamic discovery to shrink the tool surface and token usage\n\nOutput Format: JSON vs TOON\n\nThe server supports two output formats that can be configured globally:\n\nJSON Format (Default)\n\nStandard JSON output with pretty-printing:\n\n \n\n(experimental) TOON Format\n\nToken-Oriented Object Notation for efficient LLM token usage:\n\n \n\n**Benefits:**\n\n- 30-60% fewer tokens vs JSON\n- Best for uniform array data (lists of emails, calendar events, files, etc.)\n- Ideal for cost-sensitive applications at scale\n\n**Usage:**\n(experimental) Enable TOON format globally:\n\nVia CLI flag:\n\n \n\nVia Claude Desktop configuration:\n\n \n\nVia environment variable:\n\n \n\nSupported Services & Tools\n\nThe server provides 300+ tools covering most of the Microsoft Graph API surface. Each tool maps 1-to-1 to a Graph API endpoint and is defined declaratively in src/endpoints.json.\n\nPersonal Account Tools (Available by default)\n\nEmail (Outlook), Calendar, OneDrive Files, Excel, OneNote, To Do Tasks, Planner, Contacts, User Profile, Search\n\nOrganization Account Tools (Requires --org-mode flag)\n\nTeams & Chats, Online Meetings, Transcripts & Recordings, Attendance Reports, SharePoint Sites & Lists, Shared Mailboxes & Calendars, User Management, Presence, Virtual Events\n\nCustom Teams emojis are available in organization mode through list-custom-emojis\nand create-custom-emoji (teams and work presets). These use the Microsoft Graph\nbeta API and request the delegated permissions TeamworkCustomEmoji.Read and\nTeamworkCustomEmoji.Create, respectively. Read-only mode exposes only the list tool.\nExisting deployments may need consent for these new scopes and reauthentication;\nadding tool support does not upgrade an already-issued token.\n\nListing can return contentBytes: null by default. To request base64 PNG/GIF images,\npass select: "displayName,contentBytes" or select: ["displayName", "contentBytes"].\nUse a small top to keep image responses manageable.\nTo create an emoji, pass body: { displayName, contentBytes }\nwith the exact approved name and base64 PNG/GIF file bytes. See Microsoft\'s\nlist\nand create\ncontracts. These tools do not post messages or reactions.\n\nRequired Graph API Permissions\n\nPermissions are requested dynamically based on which tools are enabled. Use --list-permissions to see the exact permissions for your configuration:\n\n \n\nThis is useful for enterprise environments where Graph API permissions must be pre-approved and admin-consented before deploying a new version.\n\nThe --list-permissions JSON includes:\n\n- toolPermissions: permissions implied by the tool surface before --allowed-scopes filtering\n- effectivePermissions: permissions implied by the tools that remain enabled after --allowed-scopes\n- permissions: legacy alias for effectivePermissions, kept for compatibility with existing scripts\n- allowedScopes: the configured scope allowlist, when provided\n- disabledTools: tools hidden because their required Graph scopes are not covered by allowedScopes\n- missingAllowedScopesForTools: unique missing scopes across disabled tools\n- extraAllowedScopesNotUsedByTools: allowed scopes that are not used by the current tool surface\n\nAllowed Scopes\n\nBy default, MSAL requests the scopes implied by the enabled tools, and the tool surface is controlled by --enabled-tools, --preset, --org-mode, and --read-only.\n\nEnterprise and headless deployments can add a scope boundary with --allowed-scopes or MS365_MCP_ALLOWED_SCOPES. When configured, the server first computes the normal tool surface, then hides Graph tools whose required scopes are not covered by the allowlist. OAuth metadata and login flows request only the effective permissions for the tools that remain enabled.\n\n \n\nCLI value takes precedence over MS365_MCP_ALLOWED_SCOPES; if neither is set, the default tool-derived scope behavior is unchanged. Supplying an empty value fails at startup so deployments do not accidentally fall back to a wider tool surface.\n\nScope coverage is hierarchy-aware: for example, Mail.ReadWrite covers tools that require Mail.Read, and Files.ReadWrite.All covers tools that require Files.Read.\n\nSharePoint supports two enterprise permission models:\n\n- Broad tenant scopes such as Sites.Read.All, Sites.ReadWrite.All, and Sites.Manage.All.\n- Microsoft Graph Sites.Selected, where SharePoint site access is granted to the app on specific site collections and Graph evaluates the signed-in user\'s own permissions at request time.\n\nThe default org-mode behavior continues to request the broad SharePoint scopes used by existing deployments. Enterprises that want selected-site SharePoint access can set an allowlist containing Sites.Selected instead of broad Sites.*.All scopes. Direct site/list/item tools that target an explicit SharePoint site, and the /drives/{drive-id}/... item tools (list, get, upload, folder, move/rename, copy, versions) for drives of a granted site, can run with Sites.Selected; tenant-wide SharePoint discovery and search tools still require broad SharePoint scopes.\n\n \n\nIn HTTP mode, OAuth discovery advertises the effective filtered permissions so clients request the same consent surface. On-Behalf-Of mode (--obo) still advertises /access_as_user for protected-resource metadata and requests it on /authorize; --allowed-scopes does not override OBO, it only narrows the tool surface.\n\nRestricting user profile fields\n\nDeployments can set a comma-separated field filter for selected user-directory paths with\n--user-fields or MS365_MCP_USER_FIELDS. It narrows the $select sent to Microsoft Graph\non /users reads and projects responses on the covered paths listed below. This is a\npath-specific data filter, **not a complete Graph authorization boundary**: other Graph\nroutes and resource shapes can expose overlapping profile data.\n\n \n\nThe filter is keyed on known Graph paths rather than tool names. It applies to direct tools,\ndiscovery mode\'s execute-tool, and matching graph-batch subrequests. Batch subrequest URLs\nare rewritten on covered /users reads, and responses are projected on all covered profile\npaths. The byte-passthrough tools (download-bytes, download-bytes-to-file,\nget-download-url) return responses verbatim and refuse targets matching the covered user\nprofile paths while the filter is active.\n\nTwo levels of enforcement apply, depending on what Graph returns:\n\n| Path | Enforcement |\n| ------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| /users, /users/{id} | $select is narrowed', 'readme_raw_len': 50658, 'one_line_zh': '📇 ☁️ - 通过 Graph API 连接 Microsoft Office 和 Microsoft 365 套件，包括 Outlook 邮件、…', 'one_line_plain': 'Connect to Microsoft Office and the Microsoft 365 suite via the Graph API, including Outlook mail'}, {'id': 1170, 'key': 'xing5-mcp-google-sheets', 'kind': 'mcp', 'name': 'xing5/mcp-google-sheets', 'one_line': '🐍 ☁️ - Create, read, update and manage spreadsheets through the Google Sheets API', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'This MCP server integrates with your Google Drive and Google Sheets, to enable creating and modifying spreadsheets.', 'url': 'https://github.com/xing5/mcp-google-sheets', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/xing5/mcp-google-sheets', 'downloads': None, 'stars': 1012, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/xing5.png?size=64', 'official_desc': '🐍 ☁️ - Create, read, update and manage spreadsheets through the Google Sheets API.', 'stars_own': 1012, 'stars_creator': None, 'stars_own_repo': 'xing5/mcp-google-sheets', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'mcp-google-sheets \n\n \n \n Your AI Assistant\'s Gateway to Google Sheets! 📊\n \n\n \n \n \n \n \n\n---\n\n🤔 What is this?\n\nmcp-google-sheets is a Python-based MCP server that acts as a bridge between any MCP-compatible client (like Claude Desktop) and the Google Sheets API. It allows you to interact with your Google Spreadsheets using a defined set of tools, enabling powerful automation and data manipulation workflows driven by AI.\n\n---\n\n🚀 Quick Start (Using uvx)\n\nEssentially the server runs in one line: uvx mcp-google-sheets@latest. \n\nThis command will automatically download the latest code and run it. **We recommend always using @latest** to ensure you have the newest version with the latest features and bug fixes.\n\n_Refer to the ID Reference Guide for more information about the IDs used below._\n\n1. **☁️ Prerequisite: Google Cloud Setup**\n * You **must** configure Google Cloud Platform credentials and enable the necessary APIs first. We strongly recommend using a **Service Account**.\n * ➡️ Jump to the **Detailed Google Cloud Platform Setup** guide below.\n\n2. **🐍 Install uv**\n * uvx is part of uv, a fast Python package installer and resolver. Install it if you haven\'t already:\n \n *Follow instructions in the installer output to add uv to your PATH if needed.*\n\n3. **🔑 Set Essential Environment Variables (Service Account Recommended)**\n * You need to tell the server how to authenticate. Set these variables in your terminal:\n * **(Linux/macOS)**\n \n * **(Windows CMD)**\n \n * **(Windows PowerShell)**\n \n * ➡️ See **Detailed Authentication & Environment Variables** for other options (OAuth, CREDENTIALS_CONFIG).\n\n4. **🏃 Run the Server!**\n * uvx will automatically download and run the latest version of mcp-google-sheets:\n \n * The server will start and print logs indicating it\'s ready.\n * \n * > **💡 Pro Tip:** Always use @latest to ensure you get the newest version with bug fixes and features. Without @latest, uvx may use a cached older version.\n\n5. **🔌 Connect your MCP Client**\n * Configure your client (e.g., Claude Desktop) to connect to the running server.\n * Depending on the client you use, you might not need step 4 because the client can launch the server for you. But it\'s a good practice to test run step 4 anyway to make sure things are set up properly.\n * ➡️ See **Usage with Claude Desktop** for examples.\n\n6. **⚡ Optional: Enable Tool Filtering (Reduce Context Usage)**\n * By default, all 19 tools are enabled (~13K tokens). To reduce context usage, enable only the tools you need.\n * ➡️ See **Tool Filtering** for details.\n\nYou\'re ready! Start issuing commands via your MCP client.\n\n---\n\n✨ Key Features\n\n* **Seamless Integration:** Connects directly to Google Drive & Google Sheets APIs.\n* **Comprehensive Tools:** Offers a wide range of operations (CRUD, listing, batching, sharing, formatting, etc.).\n* **Flexible Authentication:** Supports **Service Accounts (recommended)**, OAuth 2.0, and direct credential injection via environment variables.\n* **Easy Deployment:** Run instantly with uvx (zero-install feel) or clone for development using uv.\n* **AI-Ready:** Designed for use with MCP-compatible clients, enabling natural language spreadsheet interaction.\n* **Tool Filtering:** Reduce context window usage by enabling only the tools you need with --include-tools or ENABLED_TOOLS environment variable.\n\n---\n\n🎯 Tool Filtering (Reduce Context Usage)\n\n**Problem:** By default, this MCP server exposes all 19 tools, consuming ~13,000 tokens before any conversation begins. If you only need a few tools, this wastes valuable context window space.\n\n**Solution:** Use tool filtering to enable only the tools you actually use.\n\nHow to Enable Tool Filtering\n\nYou can filter tools using either:\n\n1. **Command-line argument** --include-tools:\n \n\n2. **Environment variable** ENABLED_TOOLS:\n \n\nAvailable Tool Names\n\nWhen filtering, use these exact tool names (comma-separated, no spaces):\n\n**Most Common Tools (recommended subset):**\n- get_sheet_data - Read from spreadsheets\n- update_cells - Write to spreadsheets\n- list_spreadsheets - Find spreadsheets\n- list_sheets - Navigate tabs\n\n**All Available Tools:**\n- add_columns\n- add_rows\n- batch_update\n- batch_update_cells\n- copy_sheet\n- create_sheet\n- create_spreadsheet\n- find_in_spreadsheet\n- get_multiple_sheet_data\n- get_multiple_spreadsheet_summary\n- get_sheet_data\n- get_sheet_formulas\n- list_folders\n- list_sheets\n- list_spreadsheets\n- rename_sheet\n- search_spreadsheets\n- share_spreadsheet\n- update_cells\n\n**Note:** If neither --include-tools nor ENABLED_TOOLS is specified, all tools are enabled (default behavior).\n\n---\n\n🛠️ Available Tools & Resources\n\nThis server exposes the following tools for interacting with Google Sheets:\n\n_Refer to the ID Reference Guide for more information about the IDs used below._\n\n*(Input parameters are typically strings unless otherwise specified)*\n\n* **list_spreadsheets**: Lists spreadsheets in the configured Drive folder (Service Account) or accessible by the user (OAuth).\n * folder_id (optional string): Google Drive folder ID to search in. Get from its URL. If omitted, uses the configured default folder or searches \'My Drive\'.\n * _Returns:_ List of objects [{id: string, title: string}]\n* **create_spreadsheet**: Creates a new spreadsheet.\n * title (string): The desired title for the spreadsheet. Example: "Quarterly Report Q4".\n * folder_id (optional string): Google Drive folder ID where the spreadsheet should be created. Get from its URL. If omitted, uses configured default or root.\n * _Returns:_ Object with spreadsheet info, including spreadsheetId, title, and folder.\n* **get_sheet_data**: Reads data from a range in a sheet/tab.\n * spreadsheet_id (string): The spreadsheet ID (from its URL).\n * sheet (string): Name of the sheet/tab (e.g., "Sheet1").\n * range (optional string): A1 notation (e.g., \'A1:C10\', \'Sheet1!B2:D\'). If omitted, reads the whole sheet/tab specified by sheet.\n * include_grid_data (optional boolean, default False): If True, returns full grid data including formatting and metadata (much larger). If False, returns values only (more efficient).\n * _Returns:_ If include_grid_data=True, full grid data with metadata (get response). If False, a values result object from the Values API (values.get response).\n* **get_sheet_formulas**: Reads formulas from a range in a sheet/tab.\n * spreadsheet_id (string): The spreadsheet ID (from its URL).\n * sheet (string): Name of the sheet/tab (e.g., "Sheet1").\n * range (optional string): A1 notation (e.g., \'A1:C10\', \'Sheet1!B2:D\'). If omitted, reads all formulas in the sheet/tab specified by sheet.\n * _Returns:_ 2D array of cell formulas (array of arrays) (values.get response).\n* **update_cells**: Writes data to a specific range. Overwrites existing data.\n * spreadsheet_id (string): The spreadsheet ID (from its URL).\n * sheet (string): Name of the sheet/tab (e.g., "Sheet1").\n * range (string): A1 notation range to write to (e.g., \'A1:C3\').\n * data (array of arrays): 2D array of values to write. Example: [[1, 2, 3], ["a", "b", "c"]].\n * _Returns:_ Update result object (values.update response).\n* **batch_update_cells**: Updates multiple ranges in one API call.\n * spreadsheet_id (string): The spreadsheet ID (from its URL).\n * sheet (string): Name of the sheet/tab (e.g., "Sheet1").\n * ranges (object): Dictionary mapping range strings (A1 notation) to 2D arrays of values. Example: { "A1:B2": [[1, 2], [3, 4]], "D5": [["Hello"]] }.\n * _Returns:_ Result of the operation (values.batchUpdate response).\n* **add_rows**: Adds (inserts) empty rows to a sheet/tab at a specified index.\n * spreadsheet_id (string): The spreadsheet ID (from its URL).\n * sheet (string): Name of the sheet/tab (e.g., "Sheet1").\n * count (integer): Number of empty rows to insert.\n * start_row (optional integer, default 0): 0-based row index to start inserting rows. If omitted, defaults to 0 (inserts at the beginning).\n * _Returns:_ Result of the operation (batchUpdate response).\n* **list_sheets**: Lists all sheet/tab n', 'readme_raw_len': 23184, 'one_line_zh': '通过 Google Sheets API 创建、读取、更新和管理电子表格', 'one_line_plain': 'Create, read, update, and manage spreadsheets via the Google Sheets API'}, {'id': 743, 'key': 'aymericzip-intlayer', 'kind': 'mcp', 'name': 'aymericzip/intlayer', 'one_line': '📇 ☁️ 🏠 - IDE assistance for the Intlayer i18n/CMS tool: CLI access and documentation', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Per-component Internationalisation solution for JS application. Type-Safe. Translate with AI. Edit Visually.', 'url': 'https://github.com/aymericzip/intlayer', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/aymericzip/intlayer', 'downloads': None, 'stars': 855, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/aymericzip.png?size=64', 'official_desc': '📇 ☁️ 🏠 - IDE assistance for the Intlayer i18n/CMS tool: CLI access and documentation.', 'stars_own': 855, 'stars_creator': None, 'stars_own_repo': 'aymericzip/intlayer', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Per-component i18n \n \n\n \n AI-powered translation. Visual Editor. Multilingual CMS. \n \n\n \n\n \n Docs •\n Next.js •\n React + Vite •\n CMS •\n Discord \n \n \n \n \n \n \n \n \n \n\n \n\n \n \n \n\nWhat is Intlayer?\n\nMost i18n libraries are either too complex, too rigid, or not built for modern frameworks.\n\nIntlayer is a **modern i18n solution** for web and mobile apps. \nIt’s framework-agnostic, **AI-powered**, and includes a free **CMS & visual editor**.\n\nWith **per-locale content files**, **TypeScript autocompletion**, **tree-shakable dictionaries**, and **CI/CD integration**, Intlayer makes internationalization **faster, cleaner, and smarter**.\n\nKeys benefits of Intlayer:\n\n| Feature | Description |\n| --------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| | **Cross-Frameworks Support** Intlayer is compatible with all major frameworks and libraries, including Next.js, React, Vite, Vue.js, Nuxt, Preact, Express, and more. |\n| | **JavaScript-Powered Content Management** Harness the flexibility of JavaScript to define and manage your content efficiently. - Content declaration |\n| | **Per-Locale Content Declaration File** Speed up your development by declaring your content once, before auto generation. - Per-Locale Content Declaration File |\n| | **Compiler** The Intlayer Compiler extract automatically the content from the components and generate the dictionary files. - Compiler |\n| | **Type-Safe Environment** Leverage TypeScript to ensure your content definitions and code are error-free, while also benefiting from IDE autocompletion. - TypeScript configuration |\n| | **Simplified Setup** Get up and running quickly with minimal configuration. Adjust settings for internationalization, routing, AI, build, and content handling with ease. - Explore Next.js integration |\n| | **Simplified Content Retrieval** No need to call your t function for each piece of content. Retrieve all your content directly using a single hook. - React integration |\n| | **Consistent Server Component Implementation** Perfectly suited for Next.js server components, use the same implementation for both client and server components, no need to pass your t function across each server component. - Server Components |\n| | **Organized Codebase** Keep your codebase more organized: 1 component = 1 dictionary in the same folder. Translations close to their respective components, enhance maintainability and clarity. - How Intlayer works |\n| | **Enhanced Routing** Full support of app routing, adapting seamlessly to complex application structures, for Next.js, React, Vite, Vue.js, etc. - Explore Next.js integration |\n| | **Markdown Support** Import and interpret, locale files and remote Markdown for multilingual content like privacy policies, documentation, etc. Interpret and make Markdown metadata accessible in your code. - Content files |\n| | **Free Visual Editor & CMS** A free visual editor and CMS are available for content writers, removing the need for a localization platform. Keep your content synchronized using Git, or externalize it totally or partially with the CMS. - Intlayer Editor - Intlayer CMS |\n| | **Tree-shakable Content** Tree-shakable content, reducing the size of the final bundle. Loads content per component, excluding any unused content from your bundle. Supports lazy loading to enhance app loading efficiency. - App build optimization |\n| | **Static Rendering** Doesn't block Static Rendering. - Next.js integration |\n| | **AI-Powered Translation** Transform your website into 231 languages with just one click using Intlayer's advanced AI-powered translation tools using your own AI provider / API key. - CI/CD integration - Intlayer CLI - Auto fill |\n| | **MCP Server Integration** Provides an MCP (Model Context Protocol) server for IDE automation, enabling seamless content management and i18n workflows directly within your development environment. - MCP Server |\n| | **VSCode Extension** Intlayer provides a VSCode extension to help you manage your content and translations, building your dictionaries, translating your content, and more. - VSCode Extension |\n| | **Interoperability** Allow interoperability with react-i18next, next-i18next, next-intl, react-intl, vue-i18n. - Intlayer and react-intl - Intlayer and next-intl - Intlayer and next-i18next - Intlayer and vue-i18n - Intlayer compat adapters |\n| | **Performances & Benchmark** Uses advanced tree-shaking and dynamic loading to boost performances and keep the solution as light as possible. - Performances & Benchmark |\n\n📦 Installation\n\nStart your journey with Intlayer today and experience a smoother, more powerful approach to internationalization.\n\n \n \n \n\n \n\n⚡ Quick Start (Next.js)\n\n \n\n \n\n \n\n Get the full guide → \n\n🎥 Live tutorial on YouTube\n\n \n\n \n \n \n\nTable of Contents\n\nExplore our comprehensive documentation to get started with Intlayer and learn how to integrate it into your projects.\n\n \n 📘 Get Started \n \n Why Intlayer? \n Introduction \n \n \n\n \n ⚙️ Concept \n \n How Intlayer Works \n Configuration \n Intlayer CLI \n Compiler \n\n Intlayer Editor \n Intlayer CMS \n Dictionary \n \n Per-Locale Content Declaration File \n Translation \n Enumeration \n Condition \n Nesting \n Markdown \n Function Fetching \n Insertion \n File \n \n \n \n \n\n \n 🌐 Environment \n \n Intlayer with Next.js 16 \n \n Next.js 15 \n Next.js 14 (App Router) \n Next.js Page Router \n Next.js using Compiler \n \n \n React CRA \n Vite + React \n Vite + React using Compiler \n React-router-v7 \n Remix 3 \n Tanstack start \n \n Solid \n \n \n Astro \n \n React \n Vue \n Svelte \n Solid \n Lit \n \n \n\n React Native \n Vite + Svelte \n SvelteKit \n Vite + Preact \n Vite + Vue \n Vite + Nuxt \n Vite + Solid \n Solid Start \n Angular \n \n Backend \n \n Express \n NestJS \n Fastify \n AdonisJS \n Hono \n Elysia \n \n\n \n \n \n\n \n 📊 Benchmark \n \n Next.js \n TanStack Start \n Vue \n Solid \n Svelte \n \n \n\n \n 📰 Blog \n \n What is i18n \n i18n and SEO \n Intlayer and i18next \n Intlayer and react-intl \n Intlayer and next-intl \n \n \n\nMultilingual content management system\n\nMore than an i18n library, Intlayer is a complete **multilingual content management system**. A full CMS is available for free at app.intlayer.org.\n\nIntlayer connects **developers**, **copywriters**, and **AI agents** in one workflow for creating and maintaining multilingual websites effortlessly.Intlayer replaces the following stack in a single solution:\n\n- i18n solutions (e.g. i18next, next-intl, vue-i18n)\n- TMSs (Translation Management Systems) (e.g. Crowdin, Phrase, Lokalise)\n- Feature flags\n- Headless CMSs (e.g. Contentful, Strapi, Sanity)\n\n \n\n🌐 Readme in other languages\n\n \n English •\n 简体中文 •\n Русский •\n 日本語 •\n Français •\n 한국어 •\n Español •\n Deutsch •\n العربية •\n Italiano •\n English (UK) •\n Português •\n हिन्दी •\n Türkçe \n \n\n🤝 Community\n\nIntlayer is built with and for the community and we’d love your input!\n\n- Have a suggestion? Open an issue\n- Found a bug or improvement? Submit a PR\n- Need help or want to connect? Join our Discord\n\nYou can also follow us on :\n\n \n \n \n \n \n \n \n \n \n \n \n \n\nContribution\n\nFor more detailed guidelines on contributing to this project, please refer to the CONTRIBUTING.md file. It contains essential information on our development process, commit message conventions, and release procedures. Your contributions are valuable to us, and we appreciate your efforts in making this project better!\n\nContribute on GitHub, GitLab, or Bitbucket.\n\nThank You for the Support\n\nIf yo", 'readme_raw_len': 8106, 'one_line_zh': '📇 ☁️ 🏠 - 为 Intlayer i18n/CMS 工具提供 IDE 辅助：CLI 访问与文档', 'one_line_plain': 'Access IDE assistance for the Intlayer i18n/CMS tool via CLI and documentation'}, {'id': 496, 'key': 'co-browser-browser-use-mcp-server', 'kind': 'mcp', 'name': 'co-browser/browser-use-mcp-server', 'one_line': '🐍 - Browser automation with browser-use, including a Dockerfile to run Chromium in Docker with a VNC server', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/kontext-security/browser-use-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/co-browser/browser-use-mcp-server', 'downloads': None, 'stars': 847, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/co-browser.png?size=64', 'official_desc': 'browser-use-mcp-server An MCP server that enables AI agents to control web browsers using browser-use.** Want to Vibe Browse the Web?** Open-source AI-powered web browser - **Vibe Browser**.', 'stars_own': 847, 'stars_creator': None, 'stars_own_repo': 'kontext-security/browser-use-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'browser-use-mcp-server\n\n \n\n \n \n \n\n**An MCP server that enables AI agents to control web browsers using\nbrowser-use.**\n\n> **🌐 Want to Vibe Browse the Web?** Open-source AI-powered web browser - **Vibe Browser**.\n>\n> **🔗 Managing multiple MCP servers?** Simplify your development workflow with agent-browser\n\n \n\nPrerequisites\n\n- uv - Fast Python package manager\n- Playwright - Browser automation\n- mcp-proxy - Required for stdio mode\n\n \n\nEnvironment\n\nCreate a .env file:\n\n \n\nInstallation\n\n \n\nUsage\n\nSSE Mode\n\n \n\nstdio Mode\n\n \n\nClient Configuration\n\nSSE Mode Client Configuration\n\n \n\nstdio Mode Client Configuration\n\n \n\nConfig Locations\n\n| Client | Configuration Path |\n| ---------------- | ----------------------------------------------------------------- |\n| Cursor | ./.cursor/mcp.json |\n| Windsurf | ~/.codeium/windsurf/mcp_config.json |\n| Claude (Mac) | ~/Library/Application Support/Claude/claude_desktop_config.json |\n| Claude (Windows) | %APPDATA%\\Claude\\claude_desktop_config.json |\n\nFeatures\n\n- [x] **Browser Automation**: Control browsers through AI agents\n- [x] **Dual Transport**: Support for both SSE and stdio protocols\n- [x] **VNC Streaming**: Watch browser automation in real-time\n- [x] **Async Tasks**: Execute browser operations asynchronously\n\nLocal Development\n\nTo develop and test the package locally:\n\n1. Build a distributable wheel:\n\n \n\n2. Install it as a global tool:\n\n \n\n3. Run from any directory:\n\n \n\n4. After making changes, rebuild and reinstall:\n \n\nDocker\n\nUsing Docker provides a consistent and isolated environment for running the server.\n\n \n\n*Note: The :ro flag in the volume mount (-v) makes the password file read-only inside the container for added security.*\n\nVNC Viewer\n\n \n\nDefault password: browser-use (unless overridden using the custom password method)\n\n \n \n \n \n \n\nExample\n\nTry asking your AI:\n\n \n\nSupport\n\nFor issues or inquiries: cobrowser.xyz\n\nStar History', 'readme_raw_len': 1898, 'one_line_zh': '🐍 - 基于 browser-use 的浏览器自动化，并含 Dockerfile，可在 Docker 中以 VNC 运行 Chromium', 'one_line_plain': 'Automate browser tasks with browser-use, including a Dockerfile for Chromium in Docker with a VNC server'}, {'id': 5603, 'key': 'skillsmp-com-creators-thedivergentai-game-development-agentic-skills-skills-bevy-testing-p', 'kind': 'skill', 'name': 'bevy-testing-patterns', 'one_line': 'Docs-first stub for Cargo tests around Bevy work', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/thedivergentai/game-development-agentic-skills/skills-bevy-testing-patterns', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/thedivergentai/game-development-agentic-skills/skills-bevy-testing-patterns', 'downloads': None, 'stars': 834, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/thedivergentai.png?size=64', 'official_desc': 'Docs-first stub for Cargo tests around Bevy work. Godot counterpart: godot-testing-patterns. Not a GdUnit port. Keywords: Bevy, cargo test, Rust, libtest.', 'stars_own': None, 'stars_creator': 838, 'stars_own_repo': None, 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'thedivergentai/game-development-agentic-skills', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '面向 Bevy 开发、文档优先的 Cargo 测试桩', 'one_line_plain': 'Add a docs-first stub for writing Cargo tests involving Bevy work'}, {'id': 1658, 'key': 'vercel-next-devtools-mcp', 'kind': 'mcp', 'name': 'vercel/next-devtools-mcp', 'one_line': '🎖️ 📇 🏠 - Next.js runtime diagnostics, route inspection, dev server logs, docs search and upgrade guides for…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Next.js Development for Coding Agent', 'url': 'https://github.com/vercel/next-devtools-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/vercel/next-devtools-mcp', 'downloads': None, 'stars': 831, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/vercel.png?size=64', 'official_desc': '🎖️ 📇 🏠 - Next.js runtime diagnostics, route inspection, dev server logs, docs search and upgrade guides for coding agents.', 'stars_own': 831, 'stars_creator': None, 'stars_own_repo': 'vercel/next-devtools-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Next.js DevTools MCP\n\n \n\nnext-devtools-mcp is a Model Context Protocol (MCP) server that connects coding agents like Claude and Cursor to your running Next.js dev server.\n\nIt is a **thin connector**. It discovers running Next.js 16+ dev servers and proxies their built-in MCP endpoint (/_next/mcp) so agents get live runtime errors, routes, and logs. It also ships two **gateways** that point agents at tools they run directly: version-accurate docs and the agent-browser CLI.\n\n> [!NOTE]\n> Docs and migration workflows no longer live in this server. Next.js bundles its own docs at node_modules/next/dist/docs/, and upgrade / Cache Components workflows are distributed as agent skills. See Migrating from 0.3.x.\n\nRequirements\n\n- Node.js v20.19 or a newer LTS version\n- npm or pnpm\n- Next.js 16+ with a running dev server (for nextjs_index / nextjs_call)\n\nInstall\n\nInstall for all your coding agents with add-mcp:\n\n \n\nAdd -y to skip the prompt and install to all detected agents. Add -g to install globally across all projects.\n\nOr add the config to your MCP client manually:\n\n \n\n> [!NOTE]\n> next-devtools-mcp@latest keeps your client on the latest version.\n\nClient-specific setup\n\n \n Amp \n\n \n\nOr follow Amp's MCP docs with the config above.\n\n \n\n \n Claude Code \n\n \n\nOr edit your MCP settings file with the config above.\n\n \n\n \n Codex \n\n \n\n**Windows 11:** add environment variables and a longer startup timeout to .codex/config.toml:\n\n \n\n \n\n \n Cursor \n\nInstall in Cursor\n\nOr go to Cursor Settings → MCP → New MCP Server and use the config above.\n\n \n\n \n Gemini \n\n \n\n \n\n \n Google Antigravity \n\nAdd to .gemini/antigravity/mcp_config.json:\n\n \n\nSee the Antigravity MCP docs.\n\n \n\n \n VS Code / Copilot \n\n \n\nOr follow the official VS Code MCP setup guide.\n\n \n\n \n Warp \n\nSettings | AI | Manage MCP Servers → + Add:\n- Name: next-devtools\n- Command: npx\n- Arguments: -y, next-devtools-mcp@latest\n\n \n\nQuick Start\n\nStart your Next.js dev server:\n\n \n\nNext.js 16+ enables its MCP endpoint by default at http://localhost:3000/_next/mcp. next-devtools-mcp discovers and connects to it automatically — no config needed.\n\nThen ask your agent about the running app:\n\n \n\nThe agent calls nextjs_index to discover servers, then nextjs_call to query their real state.\n\nTools\n\n| Tool | What it does |\n| -------------- | ----------------------------------------------------------------------------------------- |\n| nextjs_index | Discover running Next.js dev servers and list each one's runtime MCP tools. |\n| nextjs_call | Call a runtime tool on a discovered server (errors, routes, logs, Server Actions). |\n| nextjs_docs | **Gateway.** Point the agent at version-accurate docs in node_modules/next/dist/docs/. |\n| browser_eval | **Gateway.** Point the agent at the agent-browser CLI for browser automation. |\n\nThe gateways do not do the work themselves — they tell the agent where the docs are or how to install/run the CLI, and the agent runs it directly (faster than proxying through MCP).\n\n \n nextjs_index — discover servers \n\nScans common ports for running Next.js 16+ dev servers and lists each server's built-in runtime tools at /_next/mcp. No parameters.\n\nRuntime tools exposed by Next.js (varies by version):\n- get_errors — current build, runtime, and type errors\n- get_logs — path to the dev log file (browser console + server output)\n- get_page_metadata — routes, pages, component metadata\n- get_project_metadata — project structure, config, dev server URL\n- get_server_action_by_id — resolve a Server Action ID to its source file\n\nOutput: JSON listing discovered servers (port, PID, URL) and their tools.\n\n \n\n \n nextjs_call — run a runtime tool \n\nCalls one runtime tool on a discovered server. Run nextjs_index first to find the port and tool name.\n\nInput:\n- port (required) — dev server port\n- toolName (required) — runtime tool to invoke\n- args (optional) — arguments object, only if the tool requires them\n\n \n\nOutput: JSON with the tool's result. Upstream requests, including response-body reads, have a 60-second deadline. Cancelling an MCP request aborts its upstream network work; discovery also releases response bodies used only to detect the protocol.\n\n \n\n \n nextjs_docs — find version-accurate docs \n\nDoes **not** fetch docs. Recent Next.js releases bundle their docs (markdown, matching your installed version) at node_modules/next/dist/docs/. This tool checks for those files before returning reading instructions. If an installed release has no bundled docs (including early 16.x releases), it offers https://nextjs.org/docs as a fallback and asks the agent to verify APIs against the installed version. Missing dependencies receive installation guidance instead. On Next.js below 16, it recommends npx @next/codemod@latest upgrade latest.\n\nInput: topic (optional), project_path (optional, defaults to cwd). The tool resolves next/package.json from that project, including hoisted workspace dependencies, and returns an absolute docsPath for the installed package.\n\n \n\n \n browser_eval — set up browser automation \n\nDoes **not** drive the browser. It detects whether agent-browser is installed and returns either the entry point (agent-browser skills get core --full) or the install steps (npm install -g agent-browser, then agent-browser install), so the agent runs the CLI directly.\n\nInput: task (optional) — used only to tailor the guidance.\n\n \n\nMigrating from 0.3.x\n\nStarting in 0.4.0, next-devtools-mcp is a thin connector.\n\n**Changed:**\n- nextjs_docs no longer fetches docs over the network. It points the agent at the docs Next.js bundles at node_modules/next/dist/docs/ (or recommends upgrading). The nextjs-docs://llms-index resource is removed.\n\n**Removed:**\n- init tool — it only enforced the old docs-fetch workflow.\n- upgrade_nextjs_16 and enable_cache_components tools and their prompts — now distributed as agent skills.\n- All cache-components://, nextjs16://, and nextjs-fundamentals:// resources — superseded by the bundled docs.\n\nWhat remains: nextjs_index, nextjs_call, nextjs_docs, and browser_eval.\n\nPrivacy & Telemetry\n\nnext-devtools-mcp collects anonymous usage telemetry to improve the tool:\n\n- **Tool usage** — which MCP tools are invoked (e.g. nextjs_index, nextjs_call)\n- **Error events** — anonymous error messages when tools fail\n- **Session metadata** — session ID, timestamps, basic environment (OS, Node.js version)\n\n**Not collected:** your code, file contents or paths, personal data, credentials, or tool arguments (only tool names).\n\nLocal files live under ~/.next-devtools-mcp/ (anonymous telemetry-id, telemetry-salt, and a debug log mcp.log).\n\n**Opt out** by setting the environment variable (add it to ~/.zshrc / ~/.bashrc to persist):\n\n \n\nDelete local telemetry data anytime:\n\n \n\nTroubleshooting\n\n**ERR_MODULE_NOT_FOUND referencing next-devtools-mcp/dist** — clear your npx cache and restart your MCP client. The server reinstalls fresh.\n\n**[error] No server info found** — nextjs_index / nextjs_call need a running Next.js 16+ dev server:\n1. Start it: npm run dev\n2. Confirm Next.js 16+ (the /_next/mcp endpoint only exists there)\n3. Verify it started without errors\n\nbrowser_eval and nextjs_docs work without a dev server.\n\nLocal Development\n\n \n\nPoint your MCP client at the local build:\n\n \n\nOr with Codex:\n\n \n\nSee the Next.js MCP documentation for how MCP works with Next.js and coding agents.\n\nLicense\n\nMIT", 'readme_raw_len': 7346, 'one_line_zh': 'Next.js 运行时诊断、路由检查、开发服务器日志、文档搜索与升级指南', 'one_line_plain': 'Run Next.js diagnostics, inspect routes, view dev server logs, search docs and find upgrade guides'}, {'id': 1611, 'key': 'sbroenne-mcp-server-excel', 'kind': 'mcp', 'name': 'sbroenne/mcp-server-excel', 'one_line': '', 'one_line_ok': 0, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Automate real Microsoft Excel with AI via MCP Server or CLI — Power Query, DAX, VBA, PivotTables, charts, and 412 operations.', 'url': 'https://github.com/sbroenne/mcp-server-excel', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/sbroenne/mcp-server-excel', 'downloads': None, 'stars': 828, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/sbroenne.png?size=64', 'official_desc': '#️⃣ 🏠 🪟 - Automate the Excel app on Windows: Power Query, DAX, VBA, PivotTables, tables, charts, ranges and formatting.', 'stars_own': 828, 'stars_creator': None, 'stars_own_repo': 'sbroenne/mcp-server-excel', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'ExcelMcp - MCP Server for Microsoft Excel\n\n \n \n\n \n \n\n \n \n \n \n\n**Website** ·\n**Installation** ·\n**Features** ·\n**Troubleshooting** ·\n**2-minute demo**\n\n**Automate real Microsoft Excel with AI.** Excel MCP Server lets GitHub Copilot,\nClaude, ChatGPT, and other agents control Excel through natural-language\nrequests—using either MCP or a token-efficient CLI.\n\nUnlike file-parser tools, ExcelMcp drives the **actual Excel application** through\nits official COM API. It can refresh Power Query, recalculate formulas, evaluate\nDAX, run VBA and Python =PY(), and preserve PivotTables, charts, macros, the\nData Model, and workbook formatting.\n\n**60 MCP tools across 31 feature areas, with 414 operations**, cover end-to-end Excel automation.\n\n> [!IMPORTANT]\n> Requires **Windows**, **Microsoft Excel 2016 or later**, and an interactive\n> desktop. It is not intended for Linux, macOS, or server-side batch processing.\n\n🚀 Get Started\n\n| Use case | Recommended path |\n|---|---|\n| **VS Code** | Install the extension |\n| **Claude Desktop or another MCP client** | Install the MCP Server |\n| **Coding agents (fewer AI tokens)** | Install the CLI |\n| **Not sure which to choose?** | Read the installation overview |\n\nFor **GitHub Copilot CLI**, install either plugin from the\nAwesome Copilot Excel listings:\n\n \n\nClose open Excel workbooks before starting; ExcelMcp requires exclusive access\nwhile automating them.\n\nWhat You Can Automate\n\n- **Data & analytics:**\n Power Query, DAX, Power Pivot, Excel Tables, PivotTables, and data connections.\n- **Cells & workbooks:**\n ranges, formulas, formatting, worksheets, files, calculation, and named ranges.\n- **Charts & visuals:**\n charts, slicers, conditional formatting, screenshots, drawings, and sparklines.\n- **Automation & advanced:**\n VBA, Python in Excel, Goal Seek, scenarios, data tables, windows, and XML Maps.\n\nExplore the capabilities behind all 414 operations.\nCurrent command details come from CLI help or the MCP tool descriptions.\n\nSee It in Action\n\n \n\nAsk in plain language:\n\n- *"Import products.csv with Power Query and load it to the Data Model."*\n- *"Create a PivotTable showing revenue by region, then add a column chart."*\n- *"Use Goal Seek to find the price that produces $100,000 profit."*\n- *"Show me Excel while you work."*\n\nBecause Excel performs the work, you can inspect results live and continue editing\nthe workbook normally.\n\nSee more examples and use cases.\n\nMCP Server or CLI?\n\nBoth entry points expose the same Core commands and behavior, so the choice is\ntechnical. Coding agents can use either one.\n\n| Interface | Works with | Why |\n|---|---|---|\n| **MCP Server** | Any MCP-compatible client | Tool discovery and persistent sessions |\n| **CLI (excelcli)** | Agents that can run terminal commands | Built-in help without loading MCP tool definitions, usually with fewer AI tokens |\n\nThe MCP Server calls the ExcelMcp service in-process. The CLI uses a background\ndaemon so workbook sessions persist across commands.\n\n \n\nRead the architecture or browse the\nMCP Server and\nCLI guides.\n\n⭐ GitHub Star History\n\n \n\n📋 Additional Information\n\nDocumentation ·\nChangelog ·\nContributing ·\nSecurity ·\nPrivacy\n\n**License:** MIT License - see LICENSE file\n\nRelated Projects\n\nOther projects by the author:\n\n- PowerPoint MCP Server — AI-powered PowerPoint automation via MCP, the sister project to this one\n- pytest-skill-engineering — LLM-powered testing framework for AI agents\n- Windows MCP Server — AI-powered Windows automation via MCP\n- OBS Studio MCP Server — AI-powered OBS Studio automation', 'readme_raw_len': 3550, 'one_line_zh': '#️⃣ 🏠 🪟 - 在 Windows 上自动操作 Excel 应用：Power Query、DAX、VBA、数据透视表、表格、图表、单元格区域等……', 'one_line_plain': None}, {'id': 805, 'key': 'tufantunc-ssh-mcp', 'kind': 'mcp', 'name': 'tufantunc/ssh-mcp', 'one_line': '📇 🏠 🐧 🪟 - SSH control for Linux and Windows servers: execute remote shell commands with password or SSH key…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server exposing SSH control for Linux servers via Model Context Protocol.', 'url': 'https://github.com/tufantunc/ssh-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/tufantunc/ssh-mcp', 'downloads': None, 'stars': 816, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/tufantunc.png?size=64', 'official_desc': '📇 🏠 🐧 🪟 - SSH control for Linux and Windows servers: execute remote shell commands with password or SSH key authentication.', 'stars_own': 816, 'stars_creator': None, 'stars_own_repo': 'tufantunc/ssh-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'SSH MCP Server v2\n\n \n \n \n \n \n \n \n \n\n**SSH MCP Server** is a security-first Model Context Protocol server that gives LLM agents controlled SSH access to remote hosts — with command classification, policy-based authorization, human-in-the-loop approval, and full audit logging.\n\n> **The risk this server exists to manage.** Giving an LLM shell access on a remote host puts private data, untrusted input and network egress in one place — Simon Willison\'s "lethal trifecta". Prompt injection has no general fix, so ssh-mcp assumes any command may be attacker-influenced: it classifies before executing, authorizes against a role × host-group matrix, gates destructive work behind approval, and records the decision either way. That narrows the blast radius; it does not remove the risk. Two things stay yours: **never point it at a root account**, and **never set auto approval on a production profile**. SECURITY.md has the full threat model.\n\n---\n\nQuick Start\n\n1. Install\n\n \n\n2. Configure\n\nWithout a config the server still starts, so a client or directory can complete the MCP\nhandshake and read tools/list — but **every tool call is refused** until you configure it,\nwith a message naming the path below. Nothing runs on a host until this step is done.\n\nCreate the config file at the path for your platform:\n\n| Platform | Path |\n|---|---|\n| Linux | ~/.config/ssh-mcp/config.toml (or $XDG_CONFIG_HOME/ssh-mcp/config.toml) |\n| macOS | ~/Library/Application Support/ssh-mcp/config.toml |\n| Windows | %APPDATA%\\ssh-mcp\\config.toml |\n\n \n\n \n\nThe config decides which hosts, roles and policy rules this server honours, so it checks\nthat nobody but you can read it — and treats the two platforms differently, because the\nquestion has a much clearer answer on one of them.\n\n**Linux and macOS: enforced.** The mode check above, on the file *and* the directory —\nwhich is why chmod 700 is in that command, since mkdir -p under the default umask\nleaves the directory 0755. The server refuses to start otherwise. "Only the owner" is\nunambiguous here and chmod is a one-line fix.\n\n**Windows: split by what the ACL actually allows.** There are no mode bits, so the ACL is\nread instead — and read exposure and write exposure are not treated alike, because Windows\nis much clearer about one of them than the other.\n\n| The ACL lets another account… | Default |\n|---|---|\n| only **read** the config | reported, and the server starts |\n| **change** the config | refused |\n| nothing (no ACL at all) | refused — that is full control for everyone |\n| …and if the ACL could not be read | refused, except when icacls is absent or the check timed out |\n\nA config under %APPDATA% inherits access for you, SYSTEM and Administrators and needs\nnothing done to it. One created elsewhere does not: a file under C:\\ inherits *read* for\nevery local account and *modify* for every authenticated one. The message names the two\nicacls commands that fix it either way.\n\nRead exposure is reported rather than refused because that is where Windows is genuinely\nmuddier than POSIX, and refusing over it blocked a config at the documented location\n(#138). Write exposure is refused\nbecause it is not muddy at all: another account being able to rewrite the file that decides\nwhich hosts, roles and approval policy this server honours is an authorization bypass, not\na disclosure.\n\nTwo flags move the whole thing: --strictConfigAcl refuses everything the check objects\nto, read-only grants included; --allowUncheckedConfigAcl reports everything and refuses\nnothing. Neither combination leaves you without an exit, which is the lesson of #138.\n\nExit statuses\n\n| Status | Meaning |\n|---|---|\n| 0 | Clean shutdown |\n| 1 | A defect in the server — printed with a stack trace; please report it |\n| 2 | How it was invoked or configured — printed as a message, no stack |\n\nA supervisor that treats any non-zero status as a failure needs no change. One\nthat matched on 1 to detect a startup problem should match on 2 as well.\n\nStarting with nothing configured is **not** an exit-2 condition, as of the release that\nadded introspection without a config: the server starts so it can be described, and\nrefuses each tool call instead. A supervisor that used a non-zero exit to catch an\nunconfigured deployment should watch for starting unconfigured on stderr, or read\nconfigured from GET /health when running the HTTP transport.\n\n3. Set credentials via environment variables\n\n \n\n4. Connect from your MCP client\n\n**Claude Code:**\n \n\n**Claude Desktop / Cursor / Windsurf:**\n \n\n**Never pass passwords as CLI arguments** — they\'re visible via ps aux. Use env vars, config files, SSH agent, or OS keychain.\n\n---\n\nTools (14)\n\n| Tool | Purpose | readOnly | destructive |\n|------|---------|:--------:|:----------:|\n| list-connections | Discover available hosts and connection status | ✅ | — |\n| list-sessions | List active sessions per host | ✅ | — |\n| open-session | Create a named interactive (stateful) or background session | — | — |\n| close-session | Close a session. A background session\'s command is signalled (INT/TERM/KILL) before its channel is dropped | — | ✅ |\n| read-session-output | Read output from background sessions (e.g., tail -f) | ✅ | — |\n| read-command | Execute allowlisted read-only commands (ls, cat, grep, ...) | ✅ | — |\n| run-command | Execute arbitrary commands (destructive/privileged need approval, unless approvalPolicy = "auto") | — | — |\n| privileged-command | Execute with sudo (needs approval, unless approvalPolicy = "auto") | — | ✅ |\n| sftp-upload | Upload a file via SFTP (replaces the destination unconditionally) | — | ✅ |\n| sftp-download | Download a file via SFTP | ✅ | — |\n| sftp-list | List a remote directory, bounded in entries and bytes | ✅ | — |\n| sftp-upload-file | Stream a local file to the remote host, never through model context | — | ✅ |\n| sftp-download-file | Stream a remote file to local disk, never through model context | — | ✅ |\n| signal-process | Send INT/TERM/KILL to a remote PID | — | ✅ |\n\nStreaming file transfer\n\nsftp-upload/sftp-download move file *contents* through the model\'s context:\nthe text is an argument on the way out and a response on the way back. That is\nwhat you want for a config snippet and exactly what you do not want for a 200 MB\ntarball or anything binary.\n\nThe two also differ on what they do to an existing destination, and the approved\nstring now says which is which. sftp-upload replaces unconditionally and spells\n--overwrite every time, because that is what it always does; sftp-upload-file\nrefuses unless you pass overwrite: true, and only then carries the flag. Since\nsftp-upload takes its content as an argument rather than naming a local file,\nits string also carries --bytes= --sha256= — without that, two\nuploads to the same path are the same string, which means one approval covers\nboth and an auditor cannot tell which set of bytes landed. The digest is 128\nbits rather than a short prefix because that claim has to hold against a caller\nwho picks both payloads, not only against an accidental repeat.\n\n[policy].denylist patterns are tested against that whole string and, for every\nSFTP tool, against the remote path on its own — as given, lexically normalized,\nand read the way Windows reads it (see "Policy Engine"). A rule written for the\npath, such as authorized_keys$, therefore does not depend on where the string\nputs it or on how the file is spelled. A rule anchored on the *whole* string\n(^sftp:upload /root/.*$) still does: that layout is ours to change, so anchor\non the path instead.\n\nsftp-upload-file and sftp-download-file stream between the remote host and\nlocal disk instead. Neither the bytes nor a base64 encoding of them ever reaches\nthe model — the response is a byte count and two paths.\n\nThe two transfer tools are **off until you configure defaults.transferRoot**,\nand refuse with an explanation until then. (sftp-list has no local side and\nneeds none of this.) That directory is the whole of their local reach:\n\n \n\nChecked on every call, a', 'readme_raw_len': 42268, 'one_line_zh': '📇 🏠 🐧 🪟 - 管理 Linux 与 Windows 服务器的 SSH：以密码或 SSH 密钥执行远程 shell 命令…', 'one_line_plain': 'SSH control for Linux and Windows servers: execute remote shell commands with password or SSH key'}, {'id': 1387, 'key': 'ckreiling-mcp-server-docker', 'kind': 'mcp', 'name': 'ckreiling/mcp-server-docker', 'one_line': '🐍 🏠 - Manage Docker containers, images, volumes and networks', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for Docker', 'url': 'https://github.com/ckreiling/mcp-server-docker', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ckreiling/mcp-server-docker', 'downloads': None, 'stars': 748, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ckreiling.png?size=64', 'official_desc': '🐍 🏠 - Manage Docker containers, images, volumes and networks.', 'stars_own': 748, 'stars_creator': None, 'stars_own_repo': 'ckreiling/mcp-server-docker', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '🐋 Docker MCP server\n\nAn MCP server for managing Docker with natural language!\n\n🪩 What can it do?\n\n- 🚀 Compose containers with natural language\n- 🔍 Introspect & debug running containers\n- 📀 Manage persistent data with Docker volumes\n\n❓ Who is this for?\n\n- Server administrators: connect to remote Docker engines for e.g. managing a\n public-facing website.\n- Tinkerers: run containers locally and experiment with open-source apps\n supporting Docker.\n- AI enthusiasts: push the limits of that an LLM is capable of!\n\nDemo\n\nA quick demo showing a WordPress deployment using natural language:\n\nhttps://github.com/user-attachments/assets/65e35e67-bce0-4449-af7e-9f4dd773b4b3\n\n🏎️ Quickstart\n\nInstall\n\nClaude Desktop\n\nOn MacOS: ~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n Install from PyPi with uv \n\nIf you don\'t have uv installed, follow the installation instructions for your\nsystem:\nlink\n\nThen add the following to your MCP servers file:\n\n \n\n \n\n \n Install with Docker \n\nPurely for convenience, the server can run in a Docker container.\n\nAfter cloning this repository, build the Docker image:\n\n \n\nAnd then add the following to your MCP servers file:\n\n \n\nNote that we mount the Docker socket as a volume; this ensures the MCP server\ncan connect to and control the local Docker daemon.\n\n \n\n📝 Prompts\n\n🎻 docker_compose\n\nUse natural language to compose containers. See above for a demo.\n\nProvide a Project Name, and a description of desired containers, and let the LLM\ndo the rest.\n\nThis prompt instructs the LLM to enter a plan+apply loop. Your interaction\nwith the LLM will involve the following steps:\n\n1. You give the LLM instructions for which containers to bring up\n2. The LLM calculates a concise natural language plan and presents it to you\n3. You either:\n - Apply the plan\n - Provide the LLM feedback, and the LLM recalculates the plan\n\nExamples\n\n- name: nginx, containers: "deploy an nginx container exposing it on port\n 9000"\n- name: wordpress, containers: "deploy a WordPress container and a supporting\n MySQL container, exposing Wordpress on port 9000"\n\nResuming a Project\n\nWhen starting a new chat with this prompt, the LLM will receive the status of\nany containers, volumes, and networks created with the given project name.\n\nThis is mainly useful for cleaning up, in-case you lose a chat that was\nresponsible for many containers.\n\n📔 Resources\n\nThe server exposes resource templates rather than enumerating currently-running containers:\n\n- docker://containers/{container_id}/logs (text/plain)\n- docker://containers/{container_id}/stats (application/json)\n\nRead either URI with a Docker container ID or name.\n\n🔨 Tools\n\nContainers\n\n- list_containers\n- create_container\n- run_container\n- recreate_container\n- start_container\n- fetch_container_logs\n- stop_container\n- remove_container\n\nImages\n\n- list_images\n- pull_image\n- push_image\n- build_image\n- remove_image\n\nNetworks\n\n- list_networks\n- create_network\n- remove_network\n\nVolumes\n\n- list_volumes\n- create_volume\n- remove_volume\n\n🚧 Disclaimers\n\nSensitive Data\n\n**DO NOT CONFIGURE CONTAINERS WITH SENSITIVE DATA.** This includes API keys,\ndatabase passwords, etc.\n\nAny sensitive data exchanged with the LLM is inherently compromised, unless the\nLLM is running on your local machine.\n\nIf you are interested in securely passing secrets to containers, file an issue\non this repository with your use-case.\n\nReviewing Created Containers\n\nBe careful to review the containers that the LLM creates. Docker is not a secure\nsandbox, and therefore the MCP server can potentially impact the host machine\nthrough Docker.\n\nFor safety reasons, this MCP server doesn\'t support sensitive Docker options\nlike --privileged or --cap-add/--cap-drop. If these features are of interest\nto you, file an issue on this repository with your use-case.\n\n🛠️ Configuration\n\nThis server uses the Python Docker SDK\'s from_env method. For configuration\ndetails, see\nthe documentation.\n\nConnect to Docker over SSH\n\nThis MCP server can connect to a remote Docker daemon over SSH.\n\nSimply set a ssh:// host URL in the MCP server definition:\n\n \n\n💻 Development\n\nPrefer using Devbox to configure your development environment. The server uses\nMCP Python SDK v2\'s high-level MCPServer API and can be inspected directly:\n\n \n\nRun the hermetic test and lint suite without a Docker daemon:\n\n \n\nSee the devbox.json for helpful development commands.\n\nAfter setting up devbox you can configure your Claude MCP config to use it:', 'readme_raw_len': 4511, 'one_line_zh': '管理 Docker 容器、镜像、数据卷和网络', 'one_line_plain': 'Manage Docker containers, images, volumes and networks'}, {'id': 1662, 'key': 'vivekvells-mcp-pandoc', 'kind': 'mcp', 'name': 'vivekvells/mcp-pandoc', 'one_line': 'Document format conversion using Pandoc: Markdown, HTML, PDF, DOCX, CSV and more', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for document format conversion using pandoc.', 'url': 'https://github.com/vivekVells/mcp-pandoc', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/vivekVells/mcp-pandoc', 'downloads': None, 'stars': 583, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/vivekVells.png?size=64', 'official_desc': 'Document format conversion using Pandoc: Markdown, HTML, PDF, DOCX, CSV and more.', 'stars_own': 583, 'stars_creator': None, 'stars_own_repo': 'vivekVells/mcp-pandoc', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '-->\n \n \n\nmcp-pandoc: A Document Conversion MCP Server\n\n> Officially included in the Model Context Protocol servers open-source project. 🎉\n\nOverview\n\nA Model Context Protocol server for document format conversion using pandoc. This server provides tools to transform content between different document formats while preserving formatting and structure.\n\nPlease note that mcp-pandoc is currently in early development. PDF support is under development, and the functionality and available tools are subject to change and expansion as we continue to improve the server.\n\nCredit: This project uses the Pandoc Python package for document conversion, forming the foundation for this project.\n\n📋 Quick Reference\n\n**New to mcp-pandoc?** Check out **📖 CHEATSHEET.md** for\n\n- ⚡ Copy-paste examples for all formats\n- 🔄 Bidirectional conversion matrix\n- 🎯 Common workflows and pro tips\n- 🌟 Reference document styling guide\n\n_Perfect for quick lookups and getting started fast!_\n\nDemo\n\n \n\n> 🎥 Watch on YouTube\n\n \n Screenshots \n\n \n \n \n \n \n \n\nMore to come...\n\nTools\n\n1. convert-contents\n - Transforms content between supported formats\n - Inputs:\n - contents (string): Source content to convert (required if input_file not provided)\n - input_file (string): Complete path to input file (required if contents not provided)\n - input_format (string): Source format of the content (defaults to markdown)\n - output_format (string): Target format (defaults to markdown)\n - output_file (string): Complete path for output file (required for pdf, docx, rst, latex, epub, odt, pptx formats)\n - reference_doc (string): Path to a reference document to use for styling (supported for docx, odt and pptx output; the file must match the output format)\n - defaults_file (string): Path to a Pandoc defaults file (YAML) containing conversion options\n - filters (array): List of Pandoc filter paths to apply during conversion\n - Supported formats, by direction:\n\n | Format | Read | Write |\n | -------- | :--: | :---: |\n | markdown | ✅ | ✅ |\n | html | ✅ | ✅ |\n | docx | ✅ | ✅ |\n | odt | ✅ | ✅ |\n | rst | ✅ | ✅ |\n | latex | ✅ | ✅ |\n | epub | ✅ | ✅ |\n | ipynb | ✅ | ✅ |\n | txt | ✅ | ✅ |\n | **pdf** | ❌ | ✅ |\n | **pptx** | ❌ | ✅ |\n\n - Note: For advanced formats (pdf, docx, rst, latex, epub, odt, pptx), an output_file path is required\n\n🔧 Advanced Features\n\nDefaults Files (YAML Configuration)\n\nUse defaults files to create reusable conversion templates with consistent formatting:\n\n \n\nExample usage: "Convert paper.md to PDF using defaults academic-paper.yaml and save as paper.pdf"\n\nPandoc Filters\n\nApply custom filters for enhanced processing:\n\nExample usage: "Convert docs.md to HTML with filters [\'/path/to/mermaid-filter.py\'] and save as docs.html"\n\n> 💡 **For comprehensive examples and workflows**, see **CHEATSHEET.md**\n\n📊 Supported Formats & Conversions\n\nBidirectional Conversion Matrix\n\nRows are source formats, columns are targets. PDF and PPTX have no rows because pandoc cannot read them.\n\n| From\\To | MD | HTML | TXT | DOCX | ODT | RST | LaTeX | EPUB | IPYNB | PDF | PPTX |\n| ------------ | --- | ---- | --- | ---- | --- | --- | ----- | ---- | ----- | --- | ---- |\n| **Markdown** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **HTML** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **TXT** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **DOCX** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **ODT** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **RST** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **LaTeX** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **EPUB** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **IPYNB** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n\nA Note on Write-Only Formats\n\n**PDF** and **PPTX** can be produced but not read.\n\nPandoc has never shipped a PDF reader. Pandoc gained a PowerPoint reader in 3.8.3 (released 2025-12-01), but this project declares no minimum pandoc version, and Ubuntu 24.04 still ships pandoc 3.1.3, so pptx input is not offered. Tracked in #54 and #49.\n\nPPTX **output** needs no particular pandoc version. The PowerPoint writer has existed since pandoc 2.0.5.\n\nFormat Categories\n\n| Category | Formats | Requirements |\n| ------------ | ---------------------------------- | ------------------------------- |\n| **Basic** | MD, HTML, TXT, IPYNB | None, returned inline |\n| **Advanced** | DOCX, ODT, PDF, PPTX, RST, LaTeX, EPUB | Must specify output_file path |\n| **Styled** | DOCX, ODT, PPTX with reference doc | Custom template support ⭐ |\n\nRequirements by Format\n\n- **PDF (.pdf)** - requires TeX Live installation\n- **DOCX (.docx), ODT (.odt), PPTX (.pptx)** - support custom styling via reference documents, and the reference must be the same format as the output\n- **All others** - no additional requirements\n\nNote: For advanced formats:\n\n1. Complete file paths with filename and extension are required\n2. **PDF conversion requires TeX Live installation** (see Critical Requirements section -> For macOS: brew install texlive)\n3. When no output path is specified:\n - Basic formats: Displays converted content in the chat\n - Advanced formats: May save in system temp directory (/tmp/ on Unix systems)\n\nUsage & configuration\n\n**NOTE: Ensure to complete installing required packages mentioned below under "Critical Requirements".**\n\nTo use the published one\n\n \n\n**💡 Quick Start**: See **CHEATSHEET.md** for copy-paste examples and common workflows.\n\n⚠️ Important Notes\n\nCritical Requirements\n\n1. **Pandoc Installation**\n\n- **Required**: Install pandoc - the core document conversion engine\n- Installation:\n\n \n\n- **Verify**: pandoc --version\n\n2. **UV package installation**\n\n- **Required**: Install uv package (includes uvx command)\n- Installation:\n\n \n\n- **Verify**: uvx --version\n\n3. **PDF Conversion Prerequisites:** Only needed if you need to convert & save pdf\n\n- TeX Live must be installed before attempting PDF conversion\n- Installation commands:\n\n \n\n4. **File Path Requirements**\n\n- When saving or converting files, you MUST provide complete file paths including filename and extension\n- The tool does not automatically generate filenames or extensions\n\nExamples\n\n✅ Correct Usage:\n\n \n\n❌ Incorrect Usage:\n\n \n\nCommon Issues and Solutions\n\n1. **PDF Conversion Fails**\n\n - Error: "xelatex not found"\n - Solution: Install TeX Live first (see installation commands above)\n\n2. **File Conversion Fails**\n\n - Error: "Invalid file path"\n - Solution: Provide complete path including filename and extension\n - Example: /path/to/document.pdf instead of just /path/to/\n\n3. **Format Conversion Fails**\n\n - Error: "Unsupported format"\n - Solution: Use only supported formats:\n - Basic: txt, html, markdown\n - Advanced: pdf, docx, rst, latex, epub\n\n4. **Reference Document Issues**\n - Error: "Reference document not found"\n - Solution: Ensure the reference document path exists and is accessible\n - Error: "reference_doc must be a \'.odt\' file when output_format is \'odt\'"\n - Solution: The reference document must be the same format as the output. Pandoc does not check this itself: a mismatched reference is silently ignored for DOCX output, and produces an ODT file that cannot be opened\n - Note: Reference documents work with DOCX, ODT and PPTX output formats\n - How to create: pandoc -o reference.docx --print-default-data-file reference.docx, and likewise with reference.odt or reference.pptx\n\nQuickstart\n\n \n\nInstalling manually via claude_desktop_config.json config file\n\n- On MacOS: open ~/Library/Application\\ Support/Claude/claude_desktop_config.json\n- On Windows: %APPDATA%/Claude/claude_desktop_config.json\n\na) Only for local development & contribution to this repo\n\n \n Development/Unpublished Servers Configuration \n\nℹ️ Replace with your locally cloned project path\n\n \n\n \n\nb) Published Servers Configuration - Consumers should use this config\n\n \n\n \n\n- If you face any issue, use the "Published Servers Configuration" above directly instead of this cli.\n\n**Note**: To use locally configured mcp-pandoc, follow "Development/Unpublished Servers Configuration" step ab', 'readme_raw_len': 9320, 'one_line_zh': '使用 Pandoc 转换文档格式：Markdown、HTML、PDF、DOCX、CSV 等', 'one_line_plain': 'Convert document formats between Markdown, HTML, PDF, DOCX, CSV and more using Pandoc'}, {'id': 1597, 'key': 'quantgeekdev-docker-mcp', 'kind': 'mcp', 'name': 'QuantGeekDev/docker-mcp', 'one_line': '🏎️ 🏠 - Docker container management and operations', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A docker MCP Server (modelcontextprotocol)', 'url': 'https://github.com/QuantGeekDev/docker-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/QuantGeekDev/docker-mcp', 'downloads': None, 'stars': 505, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/QuantGeekDev.png?size=64', 'official_desc': '— A docker MCP Server (modelcontextprotocol) ☆`505`', 'stars_own': 505, 'stars_creator': None, 'stars_own_repo': 'QuantGeekDev/docker-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '🐳 docker-mcp\n\n \n \n \n \n\nA powerful Model Context Protocol (MCP) server for Docker operations, enabling seamless container and compose stack management through Claude AI.\n\n✨ Features\n\n- 🚀 Container creation and instantiation\n- 📦 Docker Compose stack deployment\n- 🔍 Container logs retrieval\n- 📊 Container listing and status monitoring\n\n🎬 Demos\nDeploying a Docker Compose Stack\n\nhttps://github.com/user-attachments/assets/b5f6e40a-542b-4a39-ba12-7fdf803ee278\n\nAnalyzing Container Logs\n\nhttps://github.com/user-attachments/assets/da386eea-2fab-4835-82ae-896de955d934\n\n🚀 Quickstart\n\nTo try this in Claude Desktop app, add this to your claude config files:\n \n\nInstalling via Smithery\n\nTo install Docker MCP for Claude Desktop automatically via Smithery:\n\n \n\nPrerequisites\n\n- UV (package manager)\n- Python 3.12+\n- Docker Desktop or Docker Engine\n- Claude Desktop\n\nInstallation\n\nClaude Desktop Configuration\n\nAdd the server configuration to your Claude Desktop config file:\n\n**MacOS**: ~/Library/Application\\ Support/Claude/claude_desktop_config.json \n**Windows**: %APPDATA%/Claude/claude_desktop_config.json\n\n \n 💻 Development Configuration \n\n \n \n\n \n 🚀 Production Configuration \n\n \n \n\n🛠️ Development\n\nLocal Setup\n\n1. Clone the repository:\n \n\n2. Create and activate a virtual environment:\n \n\n3. Install dependencies:\n \n\n🔍 Debugging\n\nLaunch the MCP Inspector for debugging:\n\n \n\nThe Inspector will provide a URL to access the debugging interface.\n\n📝 Available Tools\n\nThe server provides the following tools:\n\ncreate-container\nCreates a standalone Docker container\n \n\ndeploy-compose\nDeploys a Docker Compose stack\n \n\nget-logs\nRetrieves logs from a specific container\n \n\nlist-containers\nLists all Docker containers\n \n\n🚧 Current Limitations\n\n- No built-in environment variable support for containers\n- No volume management\n- No network management\n- No container health checks\n- No container restart policies\n- No container resource limits\n\n🤝 Contributing\n\n1. Fork the repository from docker-mcp\n2. Create your feature branch\n3. Commit your changes\n4. Push to the branch\n5. Open a Pull Request\n\n📜 License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n✨ Authors\n\n- **Alex Andru** - *Initial work | Core contributor* - @QuantGeekDev\n- **Ali Sadykov** - *Initial work | Core contributor* - @md-archive\n\n---\nMade with ❤️', 'readme_raw_len': 2339, 'one_line_zh': '🏎️ 🏠 - Docker 容器管理与运维操作', 'one_line_plain': 'Docker lets you create, start, and stop containers for running apps locally'}, {'id': 2736, 'key': 'shinpr-mcp-local-rag', 'kind': 'mcp', 'name': 'shinpr/mcp-local-rag', 'one_line': '📇 🏠 - Local document search: semantic search over PDF, DOCX, TXT and Markdown files with LanceDB vector…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Local-first RAG server for developers. Semantic + keyword search for code and technical docs. Works with MCP or CLI. Fully private, zero setup.', 'url': 'https://github.com/shinpr/mcp-local-rag', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/shinpr/mcp-local-rag', 'downloads': None, 'stars': 415, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/shinpr.png?size=64', 'official_desc': '📇 🏠 - Local document search: semantic search over PDF, DOCX, TXT and Markdown files with LanceDB vector storage and local embeddings.', 'stars_own': 415, 'stars_creator': None, 'stars_own_repo': 'shinpr/mcp-local-rag', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'MCP Local RAG\n\n \n \n \n \n\n \n English |\n 简体中文 |\n Deutsch |\n Español |\n Português (Brasil) |\n Français \n \n\nSearch private documents from an MCP client or the terminal without sending them to an\nembedding API.\n\nmcp-local-rag indexes PDF, DOCX, Markdown, and text files on your machine. Search combines\nsemantic similarity with keyword matching, so queries can match both intent and exact technical\nterms such as API names, class names, and error codes. Results include source passages and,\nwhere available, headings, line numbers, or page numbers so you can check and cite the original\ndocument.\n\nNo API key, Docker, Python, or external database is required. After the initial model download,\ntext ingestion and search work offline.\n\nQuick Start\n\nRequirements\n\n- Node.js 22 or later\n- Internet access on first use to download the npm package and embedding model\n- A directory containing the documents you want to search\n\nSet BASE_DIR to that directory. It is also the security boundary for file operations. Replace\n/absolute/path/to/your/documents below with the directory\'s absolute path.\n\nUse one of the examples below, or register npx -y mcp-local-rag and set BASE_DIR using your\nclient\'s MCP configuration format.\n\nSet DB_PATH and CACHE_DIR to absolute paths as well. Relative paths resolve from the\nserver\'s working directory, so starting the server from different projects creates a separate\nindex and model cache in each.\n\n \n Claude Code \n\nRun this command:\n\n \n\n \n\n \n Codex \n\nAdd to ~/.codex/config.toml:\n\n \n\n \n\n \n OpenCode \n\nAdd to ~/.config/opencode/opencode.json (or opencode.jsonc):\n\n \n\n \n\n \n Cursor \n\nAdd to ~/.cursor/mcp.json:\n\n \n\n \n\nRestart the client, then ask it to build the index:\n\n \n\nThe first sync downloads the default embedding model (about 90 MB) and may take 1–2 minutes\nbefore ingestion starts. Later runs use the local cache.\n\nOnce the sync completes:\n\n \n\nCLI Quick Start\n\nTo use the CLI without an MCP client:\n\n \n\nThe CLI uses the current directory as its document root by default. Run both commands from the\nsame directory so they use the same default index, or set BASE_DIR and DB_PATH explicitly.\n\nSupported Content\n\n| Input | How to ingest |\n|---|---|\n| PDF, DOCX, TXT, Markdown | File ingestion or directory sync |\n| HTML already fetched by the client | ingest_data |\n| Plain text or Markdown held in memory | ingest_data with a stable source identifier |\n\nHTML fetching is not built into the server. An MCP client can fetch a page and pass its HTML to\ningest_data.\n\nExcel, PowerPoint, standalone images, and source-code file extensions are not supported by file\ningestion. PDFs can optionally use a local vision model to describe figures, but this is not\nOCR or image search.\n\nUsing the Index\n\nSync after adding, editing, or removing documents. For searches and follow-up reading, ask your\nMCP client:\n\n \n\nYou can also ingest a single file or HTML already fetched by the client. To refresh an existing\nentry, ingest the document again using the same path or source identifier. sync skips unchanged\nfiles. MCP file paths must be absolute and inside a configured document root.\n\nPDF section headings may be inaccurate. Check the original page when you need the exact heading.\n\n \n MCP Tools \n\n| Tool | Purpose |\n|---|---|\n| sync_start | Reconcile the index with all configured roots or one path |\n| sync_status | Poll a running sync job |\n| ingest_file | Ingest or replace one file |\n| ingest_data | Ingest text, Markdown, or HTML already held by the client |\n| query_documents | Search with semantic matching and keyword boost |\n| read_chunk_neighbors | Read surrounding chunks from a search result |\n| list_files | Show supported files and their ingestion state |\n| delete_file | Delete an indexed file or an ingest_data item |\n| status | Show index and search status |\n\n \n\nCLI\n\nUse the CLI to update the index, narrow searches, or remove indexed content:\n\n \n\ningest imports the selected files; sync also removes entries for deleted files and skips\nunchanged files. Use --scope to restrict search results to a path prefix, repeating it to\ninclude multiple prefixes.\n\nGlobal options such as --db-path, --cache-dir, and --model-name go before the subcommand.\nSubcommand options go after it:\n\n \n\nRun npx mcp-local-rag --help for the complete command reference.\n\nquery writes its results to stdout as JSON, best match first, so it can be piped into another\ntool. The field-by-field contract is in\ndocs/schema/query-output.schema.json.\n\nIf you move a project and want to keep its index, stop the MCP server and other database\nwriters, move the files and database together without changing the directory layout, then run:\n\n \n\nUpdate BASE_DIR/BASE_DIRS, DB_PATH, and any other moved paths in your MCP configuration,\nthen restart the client.\n\nAgent Skills\n\nAgent Skills provide query and ingestion guidance for AI assistants:\n\n \n\nInstalled skills cover query formulation, result refinement, and HTML ingestion. Ask the\nassistant to use the mcp-local-rag skill explicitly if it does not activate automatically.\n\nAdvanced Options\n\nStart with the defaults. Open the sections below when you need different document roots, better\nresults for your corpus, or searchable PDF figures.\n\n \n Storage and Document Roots \n\nThe MCP server reads environment variables. The CLI accepts the listed variables and flags.\nKeep the same DB_PATH when commands should use the same index.\n\n| Environment Variable | CLI Flag | Default | Description |\n|---------------------|----------|---------|-------------|\n| BASE_DIR | --base-dir | Current directory | One document root; the CLI flag is repeatable on ingest, list, and sync |\n| BASE_DIRS | N/A | (unset) | JSON array of document roots; takes precedence over BASE_DIR |\n| DB_PATH | --db-path | ./lancedb/ | Vector database location |\n| CACHE_DIR | --cache-dir | ./models/ | Model cache directory |\n| HF_ENDPOINT | N/A | https://huggingface.co | Hugging Face model download endpoint; use a mirror URL when direct downloads are blocked |\n| MAX_FILE_SIZE | --max-file-size | 104857600 (100MB) | Maximum file size in bytes |\n\nFile operations stay within configured roots. For multiple directories, set\nBASE_DIRS=\'["/absolute/docs","/absolute/specs"]\' or repeat CLI --base-dir. Precedence: CLI\nroots, BASE_DIRS, BASE_DIR, then the current directory. Only the highest-priority source is\nused; roots from different sources are not merged. Invalid BASE_DIRS is an error. Relative\nDB_PATH and CACHE_DIR are resolved from the working directory.\n\n \n\n \n Models and Search Tuning \n\nChoose an embedding model for your documents’ language and subject. Compare settings using\nquestions you actually ask and check which source passages are returned. The model must support\nmean pooling and L2 normalization, which this tool uses to produce embeddings.\n\n| Environment Variable | CLI Flag | Default | Description |\n|---------------------|----------|---------|-------------|\n| MODEL_NAME | --model-name | Xenova/all-MiniLM-L6-v2 | Hugging Face embedding model |\n| CHUNK_MIN_LENGTH | --chunk-min-length | 50 | Minimum length in characters (1–10000) for ordinary chunks; a fragment of content split to fit the model\'s token limit can be shorter |\n| EMBED_TITLE_PREFIX | N/A | false | Add the document title to each chunk\'s embedding input |\n| EMBED_HEADING_PREFIX | N/A | false | Add the heading hierarchy to each chunk\'s embedding input when it fits |\n| RAG_DEVICE | N/A | cpu | ONNX Runtime execution device |\n| RAG_DTYPE | N/A | fp32 | Embedding dtype passed to the selected model |\n\nBoth prefix options default to false and work independently. Try EMBED_TITLE_PREFIX when a\npassage needs the document’s overall topic, or EMBED_HEADING_PREFIX when it needs its\nsection’s topic. Enabling both is not always better. They affect embeddings, not the returned\ntext or keyword index; heading context is omitted when it would exceed the input budget.\n\nWhen changing embedding models, build a fresh index at a new DB_PATH. Vectors from different\nmodels are not comparable, even wh', 'readme_raw_len': 13904, 'one_line_zh': '本地文档搜索：借助 LanceDB 向量库对 PDF、DOCX、TXT 与 Markdown 文件做语义搜索…', 'one_line_plain': 'Search local documents semantically over PDF, DOCX, TXT and Markdown files with LanceDB vector…'}, {'id': 5614, 'key': 'razorpay-slashdocs', 'kind': 'repo', 'name': 'razorpay/slashdocs', 'one_line': 'Give your agents the power to publish reviewable HTML documents', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Give your agents the power to publish reviewable HTML documents. Self-hosted server + CLI + remote MCP; agents push, humans review with comments and versions.', 'url': 'https://github.com/razorpay/slashdocs', 'source': 'github', 'source_url': 'https://github.com/razorpay/slashdocs', 'downloads': None, 'stars': 388, 'installs': None, 'mentions': 1, 'score': 103.29, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/razorpay.png?size=64', 'official_desc': 'Give your agents the power to publish reviewable HTML documents. Self-hosted server + CLI + remote MCP; agents push, humans review with comments and versions.', 'stars_own': 388, 'stars_creator': None, 'stars_own_repo': 'razorpay/slashdocs', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "slashdocs\n\n \n \n \n \n \n\nThe collaborative workspace for agentic organizations.\n\nslashdocs is where agents and people work on the same documents. Agents publish rich, self-contained HTML: reports, specs, dashboards, diagrams, and decision docs. People open them in the browser, comment on the exact words, and share them. Agents read the comments and publish the next version. Every version is kept.\n\nInstead of pasting giant reports into chat, give the work a shared home where your team and its agents improve it together.\n\nWhat you can build with it\n\n- Agent-generated architecture reviews\n- Incident reports and postmortems\n- Product specs and launch plans\n- Data analysis reports\n- Design proposals with Mermaid/SVG diagrams\n- QA findings and browser-test reports\n- Internal dashboards captured as portable HTML\n\nFeatures\n\n- One self-contained HTML file per document\n- Browser review UI with sandboxed rendering\n- Anchored comments and resolve/reopen workflow\n- Slack notifications, personal mute controls, and comment/heading links\n- Immutable versions with optimistic concurrency\n- OpenID Connect for human users (Google, Okta, Entra ID, Keycloak, Dex, ...)\n- Native service accounts for headless/agent workflows\n- Remote MCP endpoint with browser OAuth—no local MCP process or CLI required\n- Go CLI with compact default output and JSON mode\n- Generated OpenAPI/Swagger docs\n- Self-hostable Go server with embedded React frontend\n- Dockerfile for container deployments\n\nWhy one HTML file?\n\nA single HTML artifact is easy for agents to generate, easy to version, easy to archive, and easy to move through a CLI. Images can be embedded with base64 data URLs, and diagrams can be Mermaid, inline SVG, or rendered HTML/CSS.\n\n \n\nNo separate image hosting is required for v1.\n\nQuickstart (docker compose)\n\nThe fastest way to try slashdocs is the bundled compose stack, which brings up\nthe server with Postgres, versitygw (S3),\nand a Dex identity provider — no cloud accounts or OAuth\nsetup needed:\n\n \n\nThis pulls the latest published image, so nothing is compiled on your machine.\nTo run your local changes instead, use docker compose up --build. Building\nneeds Docker with Compose v2 and the\nBuildx plugin, because\nthe Dockerfile uses BuildKit features. Docker Desktop and Docker's official\nEngine packages include both. Distro packages may not: on Ubuntu or Debian, run\nsudo apt install docker-buildx. Check with docker buildx version.\n\nOpen and sign in as admin@example.com with password\npassword.\n\nDex is the **development** login only. Real deployments configure Google or\nany other OIDC provider — see Self-hosting.\n\nmake e2e boots the same stack and runs the full non-interactive smoke test\n(login, documents, comments, sharing, folders, sandboxed rendering, SSE).\nmake e2e-browser runs the advisory Chromium browser suite. It reuses a healthy\nslashdocs + Dex stack already listening on ports 8080 and 5556; otherwise it boots\nthe isolated slashdocs-e2e compose project and leaves it running for inspection.\nBrowser E2E is deliberately separate from make ci.\n\nTear down the isolated E2E project, including data volumes, with:\n\n \n\nIf you started the normal quickstart project with docker compose up, stop that\nproject with docker compose down -v instead.\n\nInstall from source\n\n \n\nBinaries are written to:\n\n \n\nRun with Docker\n\nEvery tagged release publishes a multi-arch (amd64, arm64) image to Docker Hub\nat razorpay/slashdocs:\n\n \n\nCopy .env.example to .env, fill it in, and run. Pin an exact\nversion (razorpay/slashdocs:0.1.0) or digest in production rather than :latest:\n\n \n\nEach GitHub Release also carries\nstandalone CLI archives and the packaged Helm chart. See\ndocs/releasing.md for how releases are cut.\n\nSee Self-hosting for production setup and all environment variables. A Kubernetes Helm chart is available in charts/slashdocs.\n\nConnect an MCP client\n\nThe application server exposes Streamable HTTP directly at:\n\n \n\nAdd that URL to a remote MCP client. Standards-based clients discover the\nserver's OAuth metadata, open the normal slashdocs sign-in and consent flow, and\nreceive a short-lived, audience-bound token using authorization code + PKCE.\nNo slashdocs CLI or local MCP bridge is involved. Unattended agents can instead\nsend a slashdocs service-account key as a bearer token.\n\nThe MCP surface covers document, version, comment, sharing, and folder\nworkflows, plus management of existing service accounts. Creating service\naccounts or bearer keys is intentionally not exposed over MCP; mint those\ncredentials in the web application or CLI. Client-local operations such as\ninstalling or updating the CLI, switching local contexts, or opening a browser\nare also not MCP tools.\n\nMCP mirrors the CLI's authoring conventions: get_authoring_guidelines\nserves the same sectioned guidelines as slashdocs guidelines (index first,\nthen per-section detail). Default sharing of new documents is\nserver-controlled: org deployments can set SLASHDOCS_DEFAULT_DOC_PERMISSION\nto viewer, commenter, or editor (default private), and both MCP and\nCLI creates report the applied default_grant so the share can be revoked.\n\nCLI quickstart\n\n \n\nUse --json for structured output:\n\n \n\nDevelopment\n\n \n\nUseful targets:\n\n \n\nBuild the server through make build-backend rather than a bare go build.\nThe target regenerates the iframe bridge and links its source fingerprint so\nstartup can reject missing, stale, or corrupt embedded assets.\n\nArchitecture\n\nEverything ships as one Go binary, slashdocs-server. It serves the REST API, the embedded React frontend, and the embedded per-platform CLI binaries (/install.sh and downloads). You bring Postgres and an S3-compatible bucket. The same binary answers on two hostnames: the app hostname for the authenticated UI and API, and a separate render hostname where document HTML is served inside a sandboxed frame — the browser's same-origin policy keeps untrusted documents away from your session.\n\n \n\nProject layout\n\n \n\nAPI docs\n\nWhen the server is running:\n\n \n\nSecurity model\n\nUploaded HTML should be treated as untrusted content. slashdocs renders documents in a sandboxed frame and requires app and render URLs on different hostnames. Scheme or port differences on the same hostname do not isolate cookies; the unsafe override is for local development only.\n\nTelemetry\n\nNone. slashdocs collects no telemetry, analytics, or usage data, and phones home to no one. The only outbound calls the server makes are to your Postgres, your blob storage, and your configured OpenID Connect provider during login.\n\nLicense\n\nMIT — see LICENSE.", 'readme_raw_len': 6571, 'one_line_zh': '让你的 Agent 能够发布可评审的 HTML 文档', 'one_line_plain': 'Give agents the power to publish reviewable HTML documents'}, {'id': 268, 'key': 'wegotdocs-open-mcp', 'kind': 'mcp', 'name': 'wegotdocs/open-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Turn a web API into an MCP server and add it to the open-source open-mcp.org registry', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/boltmcp/boltmcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/wegotdocs/open-mcp', 'downloads': None, 'stars': 370, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/wegotdocs.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Turn a web API into an MCP server in 10 seconds and add it to the open source registry: [https://open-mcp.org](https://open-mcp.org)', 'stars_own': 370, 'stars_creator': None, 'stars_own_repo': 'boltmcp/boltmcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'BoltMCP Installation\n\nBoltMCP installs on any Kubernetes cluster with a single Helm chart.\n\nInstall with Claude\n\n1. 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'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/storybookjs/storybook/tree/next/code/addons/mcp', 'downloads': None, 'stars': 91338, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/storybookjs.png?size=64', 'official_desc': '📇 🏠 - Helps agents write and test Storybook stories for UI components.', 'stars_own': 91338, 'stars_creator': None, 'stars_own_repo': 'storybookjs/storybook', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Build bulletproof UI components faster \n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \nStorybook is a frontend workshop for building UI components and pages in isolation. Thousands of teams use it for UI development, testing, and documentation. Find out more at storybook.js.org !\n \n\n \n \n \n\n \n View README for: \n \n \n \n\nTable of contents\n\n- 🚀 Getting Started\n- 📒 Projects\n - 🛠 Supported Frameworks & Examples\n - 🔗Addons\n- 🏅 Badges & Presentation materials\n- 👥 Community\n- 👏 Contributing\n - 👨\u200d💻 Development scripts\n - 💸 Sponsors\n - 💵 Backers\n- :memo: License\n\nGetting Started\n\nVisit Storybook's website to learn more about Storybook and to get started.\n\nDocumentation\n\nDocumentation can be found on Storybook's docs site.\n\nExamples\n\nView Component Encyclopedia to see how leading teams use Storybook.\n\nUse storybook.new to quickly create an example project in Stackblitz.\n\nStorybook comes with a lot of addons for component design, documentation, testing, interactivity, and so on. Storybook's API makes it possible to configure and extend in various ways. It has even been extended to support React Native, Android, iOS, and Flutter development for mobile.\n\nCommunity\n\nFor additional help, share your issue in the repo's GitHub Discussions.\n\nProjects\n\nSupported Frameworks\n\n| Renderer | Demo | |\n| -------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |\n| React | | |\n| Angular | | |\n| Vue 3 | | |\n| Web components | | |\n| React Native | | |\n| HTML | | |\n| Ember | | |\n| Svelte | | |\n| Preact | | |\n| Qwik | | |\n| SolidJS | | |\n| Android, iOS, Flutter | | |\n\nAddons\n\n| Addons | |\n| ------------------------------------------------------------------------- | -------------------------------------------------------------------------- |\n| a11y | Test components for user accessibility in Storybook |\n| actions | Log actions as users interact with components in the Storybook UI |\n| backgrounds | Let users choose backgrounds in the Storybook UI |\n| cssresources | Dynamically add/remove CSS resources to the component iframe |\n| design assets | View images, videos, and weblinks alongside your story |\n| docs | Add high quality documentation to your components |\n| events | Interactively fire events to components that respond to EventEmitter |\n| google-analytics | Reports google analytics on stories |\n| graphql | Query a GraphQL server within Storybook stories |\n| jest | View the results of components' unit tests in Storybook |\n| links | Create links between stories |\n| measure | Visually inspect the layout and box model within the Storybook UI |\n| outline | Visually debug the CSS layout and alignment within the Storybook UI |\n| query params | Mock query params |\n| viewport | Change display sizes and layouts for responsive components using Storybook |\n\nSee Addon / Framework Support Table\n\nTo continue improving your experience, we have to eventually deprecate or remove certain addons in favor of new and better tools.\n\nIf you're using info/notes, we highly recommend you migrate to docs instead, and here is a guide to help you.\n\nIf you're using contexts, we highly recommend you migrate to toolbars and here is a guide to help you.\n\nIf you're using addon-storyshots, we highly recommend you migrate to the Storybook test-runner and here is a guide to help you.\n\nBadges & Presentation materials\n\nWe have a badge! Link it to your live Storybook example.\n\n \n\n \n\nIf you're looking for material to use in your Storybook presentation, such as logos, video material, and the colors we use, you can find it all on our brand repo.\n\nCommunity\n\n- Tweeting via @storybookjs\n- Blogging at storybook.js.org and Medium\n- Chatting on Discord\n- Videos and streams at YouTube\n\nContributing\n\nContributions to Storybook are always welcome!\n\n- 📥 Pull requests and 🌟 Stars are always welcome.\n- Read our contributing guide to get started,\n or find us on Discord, we will take the time to guide you.\n\nLooking for a first issue to tackle?\n\n- We tag issues with when we think they are well suited for people who are new to the codebase or OSS in general.\n- Talk to us, we'll find something that suits your skills and learning interests.\n\nDevelopment scripts\n\nStorybook is organized as a monorepo. Useful scripts include:\n\nyarn start\n\n> Runs a sandbox template storybook with test stories\n\nyarn task\n\n> As above, but gives you options to customize the sandbox (e.g. selecting other frameworks)\n\nyarn lint\n\n> boolean check if code conforms to linting rules - uses remark & eslint\n\n- yarn lint:js - will check js\n- yarn lint:md - will check markdown + code samples\n- yarn lint:js --fix - will automatically fix js\n\nyarn test\n\n> boolean check if unit tests all pass - uses jest\n\n- yarn run test --core --watch - will run core tests in watch-mode\n\nSponsors\n\nBecome a sponsor to have your logo and website URL on our README on Github. \\[Become a sponsor]\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\nBackers\n\nBy making a recurring donation, you can support us and our work. \\[Become a backer]\n\n \n\nLicense\n\nMIT\n\n-the end-", 'readme_raw_len': 5365, 'one_line_zh': '📇 🏠 - 帮助 Agent 为 UI 组件编写并测试 Storybook 故事', 'one_line_plain': 'MCP helps agents write and test Storybook stories for UI components'}, {'id': 8124, 'key': 'skillsmp-com-creators-outline-outline-claude-skills-verify-outline', 'kind': 'skill', 'name': 'verify-outline', 'one_line': 'Launch an isolated Outline instance (web UI + API, production build, own Postgres/Redis containers) from the…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/outline/outline/claude-skills-verify-outline', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/outline/outline/claude-skills-verify-outline', 'downloads': None, 'stars': 40915, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/outline.png?size=64', 'official_desc': 'Launch an isolated Outline instance (web UI + API, production build, own Postgres/Redis containers) from the current checkout and drive it in a headless browser to prove a user-facing change works, with screenshots, Playwright traces and database checks as evidence. Use when asked to verify, demo, r', 'stars_own': None, 'stars_creator': 40975, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'outline/outline', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '从…启动隔离的 Outline 实例（Web UI + API、生产构建、自有 Postgres/Redis 容器）', 'one_line_plain': 'Launch an isolated Outline instance with a web UI and API, using your own Postgres or Redis containers'}, {'id': 532, 'key': 'lightpanda-io-browser', 'kind': 'mcp', 'name': 'lightpanda-io/browser', 'one_line': '🏠 🍎 🐧 - Headless browser for AI and web automation with a built-in MCP server', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Lightpanda: the headless browser designed for AI and automation', 'url': 'https://github.com/lightpanda-io/browser', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/lightpanda-io/browser', 'downloads': None, 'stars': 36741, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/lightpanda-io.png?size=64', 'official_desc': '🏠 🍎 🐧 - Headless browser for AI and web automation with a built-in MCP server.', 'stars_own': 36741, 'stars_creator': None, 'stars_own_repo': 'lightpanda-io/browser', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Lightpanda Browser \n \n The headless browser built from scratch for AI agents and automation. \nNot a Chromium fork. Not a WebKit patch. A new browser, written in Zig. \n16x lighter and 9x faster than Chromium.\n \n\n \n \n\n \n \n \n \n\n \n \n\n \n\n \n \n\n \n\nBenchmarks\n\nRequesting 933 real web pages over the network on a AWS EC2 m5.large instance.\nSee benchmark details.\n\n| Metric | Lightpanda | Headless Chrome | Difference |\n| :---- | :---- | :---- | :---- |\n| Memory (peak, 100 pages) | 123MB | 2GB | ~16x less |\n| Execution time (100 pages) | 5s | 46s | ~9x faster |\n\nQuick start\n\nInstall\n\n**Package Managers**\n\nLatest nightly from Homebrew:\n \n\nLatest nightly from Arch Linux User Repository:\n \n\n**Download from the nightly builds**\n\nYou can download the last binary from the nightly\nbuilds for\nLinux and MacOS for both x86_64 and aarch64.\n\n*For Linux*\n \n\nVerify the binary before running anything:\n \n\nLinux aarch64 is also available\n\n> **Note:** The Linux release binaries are linked against glibc. On musl-based Linux distributions (Alpine, etc.) the binary fails with cannot execute: required file not found because the glibc dynamic linker is missing. Use a glibc-based base image (e.g., FROM debian:bookworm-slim or FROM ubuntu:24.04) or build from sources.\n>\n> **Android / Termux:** there is no native Android build. The Linux aarch64 binary needs the glibc loader (/lib/ld-linux-aarch64.so.1), which Android's Bionic libc does not provide, so it fails with the same cannot execute: required file not found error.\n\n*For MacOS*\n \n\nMacOS x86_64 is also available\n\n*For Windows + WSL2*\n\nLightpanda has no native Windows binary. Install it inside WSL following the Linux steps above.\n\nWSL not installed? Run wsl --install from an administrator shell, restart, then open wsl.\nSee Microsoft's WSL install guide for details.\n\nYour automation client (Puppeteer, Playwright, etc.) can run either inside WSL or on the Windows host. WSL forwards localhost:9222 automatically.\n\n**Install from Docker**\n\nLightpanda provides official Docker\nimages for both Linux amd64 and\narm64 architectures.\nThe following command fetches the Docker image and starts a new container exposing Lightpanda's CDP server on port 9222.\n \n\nDump a URL\n\n \n\nYou can use --dump markdown to convert directly into markdown, or\n--dump png > page.png or --dump pdf > page.pdf for a text-only rendering\nof the page.\n--wait-until, --wait-ms, --wait-selector and --wait-script are\navailable to adjust waiting time before dump.\n\nStart a CDP server\n\n \nOnce the CDP server started, you can run a Puppeteer script by configuring the\nbrowserWSEndpoint.\n\n \n Example Puppeteer script \n\n \n \n\nStart a webdriver Bidi server\n\nUse --protocol webdriver to enable Bidi support.\nYou can start both, CDP and Bidi, with --protocol webdriver --protocol cdp\n\n \n\nAgent mode\n\nlightpanda agent lets you drive the browser with a native agent. Describe what\nyou want in plain English or with slash commands, and it controls the browser:\nnavigating pages, clicking through flows, filling forms, extracting structured\ndata. Think of it as a robot you're directing to use the web, more than a\nchatbot you're having a conversation with.\n\nBecause the agent runs inside the same process as the browser, every tool call\nis a direct operation and you retain Lightpanda's speed and memory advantage.\n\nThe output of an agent session is a\nPandaScript: vanilla JavaScript\nwith a small set of native browser primitives built directly into Lightpanda.\nRun /save to export one from your current session, then replay it with\nlightpanda run .js. Scripts are deterministic and token-free, so\nyou can prototype with the LLM and ship the output to production without a\nmodel at runtime.\n\nIt supports Anthropic, OpenAI, Gemini, Google Vertex AI, Mistral, Hugging\nFace, the multi-lab gateways Vercel AI Gateway\nand OpenRouter (one key for hundreds of models), any OpenAI-compatible endpoint via\nOPENAI_BASE_URL, and local models via Ollama or llama.cpp. You can also run\nwithout an LLM using --no-llm, which drops you into the REPL. See the\nagent documentation for the full\nreference.\n\n \n\nNative MCP and skill\n\nThe MCP server communicates via MCP JSON-RPC 2.0 over stdio.\n\nAdd to your MCP configuration:\n \n\nHTTP transport and independent sessions\n\nFor serving several agents from one process, start the MCP server over HTTP\ninstead of stdio by giving it a port (add --host x.x.x.x to specify the\ninterface to listen on):\n\n \n\nClients POST JSON-RPC to http://host:9223/mcp. Each connection is routed to\nits own **browsing session** — its own page, cookies and memory — so agents no\nlonger clobber each other's page:\n\n- A client that initializes without an Mcp-Session-Id header is assigned a\n fresh session; the id comes back in the response's Mcp-Session-Id header.\n Send it on subsequent requests to stay on that session (**isolation**).\n- Two agents that send the **same** Mcp-Session-Id share one browsing context\n (**sharing** — e.g. a workflow where several agents work the same page).\n- The session_new, session_list and session_close tools manage sessions\n explicitly. Sending DELETE /mcp with an Mcp-Session-Id closes that session.\n\nRead full documentation\n\nA skill is available in lightpanda-io/agent-skill.\n\nShell completion\n\nlightpanda completion prints a completion script for commands, flags and their values:\n\n \n\nTelemetry\n\nBy default, Lightpanda collects and sends usage telemetry. This can be disabled by setting an environment variable LIGHTPANDA_DISABLE_TELEMETRY=true. You can read Lightpanda's privacy policy at: https://lightpanda.io/privacy-policy.\n\nCore dumps\n\nSet LIGHTPANDA_DISABLE_CORE_DUMP (to any value) to suppress crash core dumps by zeroing the soft RLIMIT_CORE at startup.\n\nStatus\n\nHere are the key features we have implemented.\nFor full details, see our Web Platform Tests results.\n\n- [x] CORS\n- [x] HTTP loader (Libcurl)\n- [x] HTML parser (html5ever)\n- [x] DOM tree\n- [x] Javascript support (v8)\n- [x] DOM APIs\n- [x] Ajax\n - [x] XHR API\n - [x] Fetch API\n- [x] DOM and Markdown dump\n- [x] CDP/websockets server\n- [x] Click\n- [x] Input form\n- [x] Cookies\n- [x] Custom HTTP headers\n- [x] Proxy support\n- [x] Network interception\n- [x] Respect robots.txt with option --obey-robots\n- [x] CDP and Webdriver Bidi\n- [x] Adblocker\n\nBuild from sources\n\nPrerequisites\n\nLightpanda is written with Zig 0.15.2. You have to\ninstall it with the right version in order to build the project.\n\nLightpanda also depends on\nv8,\nLibcurl and html5ever.\n\nTo be able to build the v8 engine, you have to install some libs:\n\nFor **Debian/Ubuntu based Linux**:\n\n \nYou also need to install Rust.\n\nFor systems with **Nix**, you can use the devShell:\n \n\nFor **MacOS**, you need cmake and Rust.\n \n\nBuild and run\n\nYou can build the entire browser with make build or make build-dev for debug\nenv.\n\nBut you can directly use the zig command: zig build run.\n\nEmbed v8 snapshot\n\nLighpanda uses v8 snapshot. By default, it is created on startup but you can\nembed it by using the following commands:\n\nGenerate the snapshot.\n \n\nBuild using the snapshot binary.\n \n\nSee #1279 for more details.\n\nTest\n\nUnit Tests\n\nYou can test Lightpanda by running make test.\n\n \n\nEnd to end tests\n\nTo run end to end tests, you need to clone the demo\nrepository into ../demo dir.\n\nYou have to install the demo's node\nrequirements\n\nYou also need to install Go > v1.24.\n\n \n\nWeb Platform Tests\n\nLightpanda is tested against the standardized Web Platform\nTests.\n\nWe use a fork including a custom\ntestharnessreport.js. Results are published daily.\n\nFor reference, you can execute a WPT test case with your browser via\nwpt.live.\n\nConfigure WPT HTTP server\n\nTo run the test, you must clone the repository, configure the custom hosts and generate the\nMANIFEST.json file.\n\nClone the repository with the fork branch.\n \n\nEnter into the wpt/ dir.\n\nInstall custom domains in your /etc/hosts\n \n\nGenerate MANIFEST.json\n \nUse the WPT's setup\nguide for\ndetails.\n\nRun WPT test suite\n\nAn external Go runner is provided by\ngithub.com/lightpand", 'readme_raw_len': 9748, 'one_line_zh': '🏠 🍎 🐧 - 面向 AI 与网页自动化的无头浏览器，内置 MCP 服务器', 'one_line_plain': 'Run a headless browser for AI and web automation with a built-in MCP server'}, {'id': 4348, 'key': 'jlowin-fastmcp', 'kind': 'mcp', 'name': 'FastMCP', 'one_line': '🐍 - High-level framework for building MCP servers in Python', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/PrefectHQ/fastmcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/jlowin/fastmcp', 'downloads': None, 'stars': 28137, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/jlowin.png?size=64', 'official_desc': 'ef="trendshift.io target="_blank"> The Model Context Protocol (MCP) connects LLMs to tools and data.', 'stars_own': 28137, 'stars_creator': None, 'stars_own_repo': 'PrefectHQ/fastmcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "FastMCP 🚀\n\n Move fast and make things. \n\n*Made with 💙 by Prefect*\n\n \n \n \n \n \n \n\n \n \n\n---\n\nThe Model Context Protocol (MCP) connects LLMs to tools and data. FastMCP is a full MCP application framework for servers, clients, and interactive apps. A server starts with ordinary Python:\n\n \n\nWhy FastMCP\n\nBuilding an effective MCP application is harder than it looks. FastMCP handles all of it. Declare a tool with a Python function, and the schema, validation, and documentation are generated automatically. Connect to a server with a URL, and transport negotiation, authentication, and protocol lifecycle are managed for you. You focus on your logic, and the MCP part just works: **with FastMCP, best practices are built in.**\n\n**That's why FastMCP is the standard framework for working with MCP.** FastMCP 1.0 was incorporated into the official MCP Python SDK in 2024. Today, the actively maintained standalone project is downloaded a million times a day, and some version of FastMCP powers 70% of MCP servers across all languages.\n\nFastMCP has three pillars:\n\n \n \n \n \n \n Servers \n \n Expose tools, resources, and prompts to LLMs.\n \n \n \n \n Apps \n \n Give your tools interactive UIs rendered directly in the conversation.\n \n \n \n \n Clients \n \n Connect to any MCP server — local or remote, programmatic or CLI.\n \n \n \n\n**Servers** wrap your Python functions into MCP-compliant tools, resources, and prompts. **Clients** connect to any server with full protocol support. And **Apps** give your tools interactive UIs rendered directly in the conversation.\n\n**Building in TypeScript?** FastMCP for TypeScript is the official counterpart, built and maintained by the same team. Same pillars, same ideas, npm install @prefecthq/fastmcp-ts.\n\nReady to build? Start with the installation guide or jump straight to the quickstart.\n\nScale MCP with Horizon\n\nFastMCP handles the MCP application layer. **Prefect Horizon** is the enterprise MCP gateway for scaling servers and tools across teams, with centralized governance over how they are deployed, discovered, secured, and used.\n\nFastMCP and Horizon are built by the same team at Prefect.\n\nDeploy FastMCP servers from GitHub with branch previews and instant rollback. Create a private registry of every MCP your company uses. Secure access with SSO and tool-level RBAC. Get audit logs, observability, and governance across your MCP stack. Remix approved tools into purpose-built endpoints for teams and agents.\n\nStart with FastMCP. Scale with Horizon →\n\nInstallation\n\nWe recommend adding FastMCP to your project with uv:\n\n \n\nFor full installation instructions, including verification and upgrading, see the **Installation Guide**.\n\n**Upgrading?** We have guides for:\n- Upgrading from FastMCP 3\n- Upgrading from FastMCP 2\n- Upgrading from MCP SDK v1 or v2\n- Upgrading from the low-level SDK v1 or v2\n\n📚 Documentation\n\nFastMCP's complete documentation is available at **gofastmcp.com**, including detailed guides, API references, and advanced patterns.\n\nDocumentation is also available in llms.txt format, which is a simple markdown standard that LLMs can consume easily:\n\n- llms.txt is essentially a sitemap, listing all the pages in the documentation.\n- llms-full.txt contains the entire documentation. Note this may exceed the context window of your LLM.\n\n**Community:** Join our Discord server to connect with other FastMCP developers and share what you're building.\n\nContributing\n\nStart with CONTRIBUTING.md for accepted contributions and the issue-assignment workflow, then follow the Contributing Guide for setup and validation. Agents should read AGENTS.md.", 'readme_raw_len': 3596, 'one_line_zh': '用于在 Python 中构建 MCP 服务器的高层框架', 'one_line_plain': 'Build MCP servers using a Python framework'}, {'id': 8081, 'key': 'skillsmp-com-creators-pydantic-pydantic-ai-pydantic-ai-slim-pydantic-ai-agents-skills-buil', 'kind': 'skill', 'name': 'building-pydantic-ai-agents', 'one_line': 'Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), workspaces, structured…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/pydantic/pydantic-ai/pydantic-ai-slim-pydantic-ai-agents-skills-building-pydantic-ai-agents', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/pydantic/pydantic-ai/pydantic-ai-slim-pydantic-ai-agents-skills-building-pydantic-ai-agents', 'downloads': None, 'stars': 20650, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/pydantic.png?size=64', 'official_desc': 'Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), workspaces, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, attach a workspace, de', 'stars_own': None, 'stars_creator': 20734, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'pydantic/pydantic-ai', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '用 Pydantic AI 构建 AI Agent：工具、能力（含按需加载）、工作区与结构化…', 'one_line_plain': 'Build AI agents with Pydantic AI — tools, capabilities, workspaces, and structured output'}, {'id': 1431, 'key': 'glips-figma-context-mcp', 'kind': 'mcp', 'name': 'GLips/Figma-Context-MCP', 'one_line': '📇 🏠 - Gives coding agents direct access to Figma data to implement designs in one shot', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server to provide Figma layout information to AI coding agents like Cursor', 'url': 'https://github.com/GLips/Figma-Context-MCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/GLips/Figma-Context-MCP', 'downloads': None, 'stars': 16012, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/GLips.png?size=64', 'official_desc': '📇 🏠 - Gives coding agents direct access to Figma data to implement designs in one shot.', 'stars_own': 16012, 'stars_creator': None, 'stars_own_repo': 'GLips/Figma-Context-MCP', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Framelink MCP for Figma \n Give your coding agent access to your Figma data. Implement designs in any framework in one-shot. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n\nGive Cursor and other AI-powered coding tools access to your Figma files with this Model Context Protocol server.\n\nWhen Cursor has access to Figma design data, it's **way** better at one-shotting designs accurately than alternative approaches like pasting screenshots.\n\n See quickstart instructions → \n\nDemo\n\nWatch a demo of building a UI in Cursor with Figma design data\n\n \n\nHow it works\n\n1. Open your IDE's chat (e.g. agent mode in Cursor).\n2. Paste a link to a Figma file, frame, or group.\n3. Ask Cursor to do something with the Figma file—e.g. implement the design.\n4. Cursor will fetch the relevant metadata from Figma and use it to write your code.\n\nThis MCP server is specifically designed for use with Cursor. Before responding with context from the Figma API, it simplifies and translates the response so only the most relevant layout and styling information is provided to the model.\n\nReducing the amount of context provided to the model helps make the AI more accurate and the responses more relevant.\n\nGetting Started\n\nMany code editors and other AI clients use a configuration file to manage MCP servers.\n\nThe figma-developer-mcp server can be configured by adding the following to your configuration file.\n\n> NOTE: You will need to create a Figma access token to use this server. Instructions on how to create a Figma API access token can be found here.\n\nMacOS / Linux\n\n \n\nWindows\n\n \n\nOr you can set FIGMA_API_KEY and PORT in the env field.\n\nIf you need more information on how to configure the Framelink MCP for Figma, see the Framelink docs.\n\nStar History\n\n \n\nLearn More\n\nThe Framelink MCP for Figma is simple but powerful. Get the most out of it by learning more at the Framelink site.", 'readme_raw_len': 1859, 'one_line_zh': '让编码 Agent 直接访问 Figma 数据，一步到位实现设计还原', 'one_line_plain': 'Give coding agents direct access to Figma data to implement designs instantly'}, {'id': 4178, 'key': 'apify-actors-mcp-server', 'kind': 'mcp', 'name': 'apify/actors-mcp-server', 'one_line': '📇 ☁️ - Use Apify Actors, pre-built cloud tools, to extract data from websites, e-commerce, social media,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/apify/apify-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/apify/actors-mcp-server', 'downloads': None, 'stars': 12050, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/apify.png?size=64', 'official_desc': 'smithery badge"> The Apify Model Context Protocol (MCP) server at **mcp.apify.com** enables your AI agents to extract data from social media, search engines, maps, e-commerce sites, and any other website using thousands of ready-made scrapers, crawlers, and automation tools from Apify Store.', 'stars_own': 12050, 'stars_creator': None, 'stars_own_repo': 'apify/apify-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "mcp.apify.com \n \n\n \n \n \n \n \n \n\nThe Apify Model Context Protocol (MCP) server at **mcp.apify.com** enables your AI agents to extract data from social media, search engines, maps, e-commerce sites, and any other website using thousands of ready-made scrapers, crawlers, and automation tools from Apify Store. It supports OAuth, allowing you to connect from clients like Claude.ai or Visual Studio Code using just the URL.\n\n> **🚀 Use the hosted Apify MCP Server!**\n>\n> For the best experience, connect your AI assistant to our hosted server at **https://mcp.apify.com**. The hosted server supports the latest features - including output schema inference for structured Actor results - that are not available when running locally via stdio.\n\n> ⚠️ **Legacy SSE transport removed.** The https://mcp.apify.com/sse endpoint has been removed in favor of Streamable HTTP. Migrate your client to **https://mcp.apify.com** — drop the /sse suffix from your configuration.\n\n💰 The server also supports agentic payments: buy a token from AGI to run any Actor, or pay per-request via direct x402 (Pay Per Event Actors only) or Skyfire.\n\nApify MCP Server is compatible with Claude Code, Claude.ai, Cursor, VS Code and any client that adheres to the Model Context Protocol.\nCheck out the MCP clients section for more details or visit the MCP configuration page.\n\n \n\nTable of Contents\n- 🌐 Introducing Apify MCP Server\n- 🚀 Quickstart\n- 🤖 MCP clients\n- 🪄 Try Apify MCP instantly\n- 💰 Agentic payments\n - How agentic payments work\n - 🪙 AGI (recommended)\n - 💸 Direct x402\n - 🔥 Skyfire\n- 🛠️ Tools, resources, and prompts\n- 📊 Telemetry\n- 💬 Usage examples\n- 🐛 Troubleshooting\n- ⚙️ Development\n- 🔒 Privacy policy\n- 🤝 Contributing\n- 📚 Learn more\n\n🌐 Introducing Apify MCP Server\n\nThe Apify MCP Server allows an AI assistant to use any Apify Actor as a tool to perform a specific task.\nFor example, it can:\n- Use Facebook Posts Scraper to extract data from Facebook posts from multiple pages/profiles.\n- Use Google Maps Email Extractor to extract contact details from Google Maps.\n- Use Google Search Results Scraper to scrape Google Search Engine Results Pages (SERPs).\n- Use Instagram Scraper to scrape Instagram posts, profiles, places, photos, and comments.\n- Use RAG Web Browser to search the web, scrape the top N URLs, and return their content.\n- Use Web Fetch to fetch any URL and return its content as Markdown, plain text, HTML, or links — with JavaScript rendering and anti-bot protection.\n\n**Video tutorial: Integrate 8,000+ Apify Actors and Agents with Claude**\n\n \n\n🚀 Quickstart\n\nYou can use the Apify MCP Server in two ways:\n\n**HTTPS Endpoint (mcp.apify.com)**: Connect from your MCP client via OAuth or by including the Authorization: Bearer header in your requests. This is the recommended method for most use cases. Because it supports OAuth, you can connect from clients like Claude.ai or Visual Studio Code using just the URL: https://mcp.apify.com.\n- https://mcp.apify.com streamable transport\n\n**Standard Input/Output (stdio)**: Ideal for local integrations and command-line tools like the Claude for Desktop client.\n- Set the MCP client server command to npx @apify/actors-mcp-server and the APIFY_TOKEN environment variable to your Apify API token.\n- See npx @apify/actors-mcp-server --help for more options.\n\nYou can find detailed instructions for setting up the MCP server in the Apify documentation.\n\n🤖 MCP clients\n\nApify MCP Server is compatible with any MCP client that adheres to the Model Context Protocol, but the level of support for dynamic tool discovery and other features may vary between clients.\n \n\nTo interact with the Apify MCP Server, you can use clients such as Claude Desktop, Visual Studio Code, or Apify Tester MCP Client.\n\nVisit mcp.apify.com to configure the server for your preferred client.\n\n \n\nTested clients\n\n- Claude Desktop\n- Claude.ai (web)\n- ChatGPT\n- VS Code (Genie)\n- Cursor\n- OpenCode\n- Kiro\n- Apify Tester MCP Client — designed for testing Apify MCP servers\n\n🪄 Try Apify MCP instantly\n\nWant to try Apify MCP without any setup?\n\nCheck out Apify Tester MCP Client\n\nThis interactive, chat-like interface provides an easy way to explore the capabilities of Apify MCP without any local setup.\nSign in with your Apify account and start experimenting with web scraping, data extraction, and automation tools!\n\nOr use the MCP bundle file (formerly known as Anthropic Desktop extension file, or DXT) for one-click installation: Apify MCP Server MCPB file\n\n💰 Agentic payments\n\nYou can pay for Actor runs without an Apify API token using **AGI**, **direct x402**, or **Skyfire**.\n\n- **AGI** (agi.apify.com) mints a prepaid Apify API token in exchange for an x402 or MPP payment. Use the token like a normal API token against mcp.apify.com and api.apify.com — works for any Actor, not just Pay Per Event ones. **Recommended** for new integrations; see AGI (recommended) below.\n- **Direct x402** pays with USDC on Base per request and does not require a separate platform account. It is fully supported by mcpc (brew install apify/tap/mcpc or npm install -g @apify/mcpc). We use mcpc because it is one of the few MCP clients that supports the latest features and the x402 protocol natively.\n- **Skyfire** pays with PAY tokens and requires a Skyfire account with a funded wallet. It does not require a special MCP client; the entire payment flow is handled directly through the MCP tool call parameters.\n\n> ℹ️ **Scope:** Both direct x402 and Skyfire are limited to Pay Per Event Actors, don't support Standby Actors, and settle per run instead of minting a token.\n\nHow agentic payments work\n\nActor run costs vary, so both payment methods use a prepaid balance model. The payment flow happens in four steps:\n\n1. **Discovery**: The agent discovers Actors with search-actors or fetch-actor-details. Those calls are free.\n2. **Prepayment**: Before running a paid Actor tool, the agent funds a prepaid balance.\n - **Direct x402**: mcpc automatically signs a $1.00 USDC transaction.\n - **Skyfire**: The agent creates a PAY token (minimum $5.00) using Skyfire's create-pay-token tool.\n3. **Execution**: The agent calls the Actor tool.\n - **Direct x402**: Handled automatically by mcpc using the prepaid balance.\n - **Skyfire**: The agent explicitly passes the PAY token in the skyfire-pay-id input property.\n4. **Resolution**: The tool returns the Actor results. Unused funds stay available for later runs.\n - **Direct x402**: After 60 minutes of inactivity, the server refunds any unused balance to the wallet on Base.\n - **Skyfire**: Skyfire returns unused funds when the token expires.\n\n🪙 AGI (recommended)\n\nAGI (Apify Agent General Interface) is the recommended way for autonomous agents to pay for Apify usage without an account. Pay once via x402 or MPP, receive a prepaid, spend-capped Apify API token, and use it directly against mcp.apify.com and api.apify.com (Authorization: Bearer ) — for any Actor.\n\nFull protocol, supported payment methods, and current terms (minimum amount, token lifetime, refund policy) are documented at **agi.apify.com/AGENTS.md** — treat it as the single source of truth.\n\n💸 Direct x402\n\nThe x402 protocol enables direct, machine-to-machine payments. Your MCP client can use it to pay for Actor runs with USDC on the Base blockchain, completely bypassing the need for an Apify API token.\n\nPrerequisites\n\n- A wallet with USDC on Base mainnet.\n\nSetup\n\nCreate or import a wallet:\n\n \n\nConnect to the server with x402 enabled:\n\n \n\nYou can now call a paid tool:\n\n \n\n🔥 Skyfire\n\nSkyfire provides managed payment infrastructure for AI agents. Instead of authenticating with an Apify API token, your agent passes a Skyfire payment token to cover the cost of each tool call using PAY tokens.\n\nPrerequisites\n\n- A Skyfire account with a funded wallet.\n- An MCP client that supports multiple servers, such as Claude Desktop, OpenCode, or VS Code.\n\nSetup\n\nConfigure the Skyfire MCP server and the Apify MCP Server in your client. Add payment=skyfire to the Apify server UR", 'readme_raw_len': 28910, 'one_line_zh': '使用平台上预置的云端工具，从网站、电商与社交媒体中提取数据。', 'one_line_plain': 'Use Apify Actors: extract data from websites, e-commerce, and social media using pre-built cloud tools'}, {'id': 20, 'key': 'skillsmp-com-creators-dart-lang-sdk-agents-skills-dart-aot-development-path-7fe32fc61bf5d6', 'kind': 'skill', 'name': 'dart-aot-development', 'one_line': 'Instructions on building and testing changes using Dart AOT mode', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/dart-lang/sdk/agents-skills-dart-aot-development--path-7fe32fc61bf5d601a137e70cd0c5d745994c371a4f79746b064b849aa2807e99', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/dart-lang/sdk/agents-skills-dart-aot-development--path-7fe32fc61bf5d601a137e70cd0c5d745994c371a4f79746b064b849aa2807e99', 'downloads': None, 'stars': 11303, 'installs': None, 'mentions': 2, 'score': 203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 24, 'icon': 'https://github.com/dart-lang.png?size=64', 'official_desc': 'Instructions on building and testing changes using Dart AOT mode. Use when working on Dart runtime, Dart AOT compiler or other related components.', 'stars_own': None, 'stars_creator': 11312, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'dart-lang/sdk', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '使用 Dart AOT 模式构建与测试变更的说明', 'one_line_plain': 'Guide Dart AOT mode builds and tests for code changes'}, {'id': 602, 'key': 'awslabs-mcp', 'kind': 'mcp', 'name': 'awslabs/mcp', 'one_line': '🎖️ ☁️ - Suite of servers for integrating with AWS services and resources', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Open source MCP Servers for AWS', 'url': 'https://github.com/awslabs/mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/awslabs/mcp', 'downloads': None, 'stars': 9794, 'installs': None, 'mentions': 22, 'score': 2203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/awslabs.png?size=64', 'official_desc': '🎖️ 🐍 ☁️ - Official AWS MCP server for interacting with AWS services using the Model Context Protocol. Enables AI assistants to manage AWS resources through natural language and programmatic interfaces', 'stars_own': 9794, 'stars_creator': None, 'stars_own_repo': 'awslabs/mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Open source MCP servers for AWS\n\nA suite of specialized MCP servers that help you get the most out of AWS, wherever you use MCP.\n\n \n \n \n \n\n> [!TIP]\n> The Agent Toolkit for AWS is now live! The Agent Toolkit for AWS is the successor to the MCP servers, plugins, and skills available on AWS Labs, and was informed by feedback from customers like you. If you're building production software using coding agents or building agents for your own customers, we recommend Agent Toolkit for AWS. It includes IAM condition keys to distinguish agent actions from human ones, CloudWatch and CloudTrail visibility, and skills that have been evaluated for accuracy and effectiveness. This repo continues to work and accept contributions. Over time, the most useful projects here will move into Agent Toolkit for AWS.\n\nTable of Contents\n\n- Open source MCP servers for AWS\n - Table of Contents\n - What is the Model Context Protocol (MCP) and how does it work with MCP Servers for AWS?\n - Open source MCP servers for AWS Transport Mechanisms\n - Supported transport mechanisms\n - Server Sent Events Support Removal\n - Why MCP Servers for AWS?\n - Available MCP Servers: Quick Installation\n - 🚀 Getting Started with AWS\n - Browse by What You're Building\n - 📚 Real-time access to official AWS documentation\n - 🏗️ Infrastructure \\& Deployment\n - Container Platforms\n - Serverless \\& Functions\n - Support\n - 🤖 AI \\& Machine Learning\n - 📊 Data \\& Analytics\n - SQL \\& NoSQL Databases\n - Search \\& Analytics\n - Backend API Providers\n - Caching \\& Performance\n - 🛠️ Developer Tools \\& Support\n - 📡 Integration \\& Messaging\n - 💰 Cost \\& Operations\n - 🧬 Healthcare \\& Lifesciences\n - Browse by How You're Working\n - 👨\u200d💻 Vibe Coding \\& Development\n - Core Development Workflow\n - Infrastructure as Code\n - Application Development\n - Container \\& Serverless Development\n - Testing \\& Data\n - Lifesciences Workflow Development\n - Healthcare Data Management\n - 💬 Conversational Assistants\n - Knowledge \\& Search\n - Content Processing \\& Generation\n - Business Services\n - 🤖 Autonomous Background Agents\n - Data Operations \\& ETL\n - Caching \\& Performance\n - Workflow \\& Integration\n - Operations \\& Monitoring\n - MCP AWS Lambda Handler Module\n - When to use Local vs Remote MCP Servers?\n - Local MCP Servers\n - Remote MCP Servers\n - Use Cases for the Servers\n - Installation and Setup\n - For macOS/Linux\n - For Windows\n - Running MCP servers in containers\n - Getting Started with Kiro\n - ~/.kiro/settings/mcp.json\n - Getting Started with Cline and Amazon Bedrock\n - cline_mcp_settings.json\n - Getting Started with Cursor\n - .cursor/mcp.json\n - Getting Started with Windsurf\n - ~/.codeium/windsurf/mcp_config.json\n - Getting Started with VS Code\n - .vscode/mcp.json\n - Getting Started with Claude Code\n - .mcp.json\n - Getting Started with fx\n - ~/.fx/mcp.json\n - Samples\n - Vibe coding\n - Additional Resources\n - Security\n - Contributing\n - Developer guide\n - License\n - Disclaimer\n\nWhat is the Model Context Protocol (MCP) and how does it work with MCP Servers for AWS?\n\n> The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.\n>\n> — Model Context Protocol README\n\nAn MCP Server is a lightweight program that exposes specific capabilities through the standardized Model Context Protocol. Host applications (such as chatbots, IDEs, and other AI tools) have MCP clients that maintain 1:1 connections with MCP servers. Common MCP clients include agentic AI coding assistants (like Kiro, Cline, Cursor, Windsurf) as well as chatbot applications like Claude Desktop, with more clients coming soon. MCP servers can access local data sources and remote services to provide additional context that improves the generated outputs from the models.\n\nMCP Servers for AWS use this protocol to provide AI applications access to AWS documentation, contextual guidance, and best practices. Through the standardized MCP client-server architecture, AWS capabilities become an intelligent extension of your development environment or AI application.\n\nMCP Servers for AWS enable enhanced cloud-native development, infrastructure management, and development workflows—making AI-assisted cloud computing more accessible and efficient.\n\nThe Model Context Protocol is an open source project run by Anthropic, PBC. and open to contributions from the entire community. For more information on MCP, you can find further documentation here\n\nOpen source MCP servers for AWS Transport Mechanisms\n\nSupported transport mechanisms\n\nThe MCP protocol currently defines two standard transport mechanisms for client-server communication:\n- stdio, communication over standard in and standard out\n- streamable HTTP\n\nThe MCP servers in this repository are designed to support stdio only.\n\nYou are responsible for ensuring that your use of these servers comply with the terms governing them, and any laws, rules, regulations, policies, or standards that apply to you.\n\nServer Sent Events Support Removal\n\n**Important Notice:** On May 26th, 2025, Server Sent Events (SSE) support was removed from all MCP servers in their latest major versions. This change aligns with the Model Context Protocol specification's backwards compatibility guidelines.\n\nWe are actively working towards supporting Streamable HTTP, which will provide improved transport capabilities for future versions.\n\nFor applications still requiring SSE support, please use the previous major version of the respective MCP server until you can migrate to alternative transport methods.\n\nWhy MCP Servers for AWS?\n\nMCP servers enhance the capabilities of foundation models (FMs) in several key ways:\n\n- **Improved Output Quality**: By providing relevant information directly in the model's context, MCP servers significantly improve model responses for specialized domains like AWS services. This approach reduces hallucinations, provides more accurate technical details, enables more precise code generation, and ensures recommendations align with current AWS best practices and service capabilities.\n\n- **Access to Latest Documentation**: FMs may not have knowledge of recent releases, APIs, or SDKs. MCP servers bridge this gap by pulling in up-to-date documentation, ensuring your AI assistant always works with the latest AWS capabilities.\n\n- **Workflow Automation**: MCP servers convert common workflows into tools that foundation models can use directly. Whether it's CDK, Terraform, or other AWS-specific workflows, these tools enable AI assistants to perform complex tasks with greater accuracy and efficiency.\n\n- **Specialized Domain Knowledge**: MCP servers provide deep, contextual knowledge about AWS services that might not be fully represented in foundation models' training data, enabling more accurate and helpful responses for cloud development tasks.\n\nAvailable MCP Servers: Quick Installation\n\nGet started quickly with one-click installation buttons for popular MCP clients. Click the buttons below to install servers directly in Cursor or VS Code:\n\n🚀 Getting Started with AWS\n\nFor AWS interactions, we recommend starting with:\n\n| Server Name | Description | Install |\n|-------------------------------------------------------------------------------------------------------------|-------------|---------|\n| AWS MCP Server (in preview) | Start here for secure, auditable AWS interactions! This remote, managed MCP server is hosted by AWS and combines comprehensive AWS API support with access to the latest AWS documentation, API references, What's New posts, and Getting Started information. Features pre-built Agent SOPs that follow AWS best practices, helping agents complete complex multi-step AWS tasks reliably. Built with safety and control in mind: syntactically validated API calls, IAM-based permissions", 'readme_raw_len': 38934, 'one_line_zh': '用于集成 AWS 服务与资源的服务器套件', 'one_line_plain': 'Integrate with AWS services and resources using a suite of servers'}, {'id': 5735, 'key': 'skillsmp-com-creators-kyegomez-swarms-skill', 'kind': 'skill', 'name': 'swarms', 'one_line': 'Build agents and multi-agent systems with the Swarms framework — the Agent class, tools, autonomous loops,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/kyegomez/swarms/skill', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/kyegomez/swarms/skill', 'downloads': None, 'stars': 7245, 'installs': None, 'mentions': 8, 'score': 803.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/kyegomez.png?size=64', 'official_desc': 'Build agents and multi-agent systems with the Swarms framework — the Agent class, tools, autonomous loops, memory, and the 15+ multi-agent architectures (SequentialWorkflow, ConcurrentWorkflow, GraphWorkflow, HierarchicalSwarm, SwarmRouter, and more). Use whenever writing, reviewing, or debugging co', 'stars_own': None, 'stars_creator': 7280, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'kyegomez/swarms', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '用该框架构建 Agent 与多 Agent 系统，含工具与自主循环等能力。', 'one_line_plain': 'Build agents and multi-agent systems using the Swarms framework, including the Agent class, tools and autonomous loops'}, {'id': 1314, 'key': '21st-dev-magic-mcp', 'kind': 'mcp', 'name': '21st-dev/Magic-MCP', 'one_line': 'Create UI components inspired by 21st.dev design engineers', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': "It's like v0, but in your Cursor / Claude Code / Windsurf: search 10,000+ React/Tailwind components, generate new UI with AI, and publish your own — right from your editor. Magic MCP is now the 21st MCP; this package keeps old configs working. Setup: 21st.dev/mcp", 'url': 'https://github.com/21st-dev/magic-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/21st-dev/magic-mcp', 'downloads': None, 'stars': 6119, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/21st-dev.png?size=64', 'official_desc': 'Create UI components inspired by 21st.dev design engineers.', 'stars_own': 6119, 'stars_creator': None, 'stars_own_repo': '21st-dev/magic-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Magic MCP → 21st MCP\n\n> **Magic MCP is now the 21st MCP.** The Magic MCP server (@21st-dev/magic) has been replaced by the unified **21st MCP**, installed via the **21st CLI** — setup at 21st.dev/mcp. This package remains published as a thin compatibility proxy so old configs keep working.\n\nInstall the 21st MCP (recommended)\n\n \n\nOr add it manually — it is a plain HTTP MCP server:\n\n \n\nGet an API key at 21st.dev/mcp.\n\n> **Old Magic API keys were reset.** Keys issued by the old Magic console no longer work anywhere. Generate a fresh key at 21st.dev/mcp.\n\nWhat this package does now (v0.2.0+)\n\nnpx -y @21st-dev/magic@latest API_KEY="..." still works: since v0.2.0 it is a small stdio proxy that forwards every MCP message to the 21st MCP server. Existing mcp.json entries that reference @21st-dev/magic keep functioning — but they now speak to the same server as the 21st CLI, with the tools available to the authenticated account.\n\nThe API key is accepted in all the historical forms: positional API_KEY="...", --API_KEY=..., /API_KEY:..., -API_KEY ..., or the TWENTY_FIRST_API_KEY / API_KEY_21ST environment variables.\n\nAI access\n\nBuilder component access does not enable hosted 21st AI. The server lists\ngenerate and iterate_generation only when AI access is enabled. Check\nget_usage.aiGenerationEnabled; this reports access, not the remaining AI\ncredit balance. With AI off, use search and get_component, then adapt the\ncode with your own coding agent. Existing drafts remain readable.\n\nA cached or legacy generation call can return ai_subscription_required. Do\nnot retry until AI is enabled. Refresh the client tool list or reconnect after\nenabling AI. The proxy forwards discovery to the server, so existing proxy\nversions receive this behavior without an npm update.\n\nserver.json mirrors the official dev.21st/mcp registry listing. The registry\nversion is independent of this compatibility package version.\n\nOld tool names → new tool names\n\nThe 21st MCP still accepts the legacy Magic tool names and translates them, so agents that remember the old names keep working. Prefer the new names:\n\n| Legacy Magic tool | 21st MCP tool |\n| --- | --- |\n| 21st_magic_component_builder | generate |\n| 21st_magic_component_inspiration | get_inspiration |\n| 21st_magic_component_refiner | generate (new generation from the refinement prompt) |\n| logo_search | search_logo (one query per call) |\n\nThe current server exposes much more than the old four tools: catalog search across components/themes/templates, paid code retrieval, bookmarks, team libraries, UI generation with variants, profile management, and more. Connect and call tools/list to see the tools available to your account.\n\nInstall as a plugin\n\nThis repository is also packaged as an agent plugin (MCP server + a UI skill). You don\'t need to wait for any store — this repo IS a marketplace, add it directly:\n\n \n\nSet your API key in the API_KEY_21ST variable (free key at 21st.dev/mcp).\n\nStore listings:\n\n- **Cursor / Grok Bot** — install "21st" from the Cursor Marketplace, then set your API_KEY_21ST under **Plugins → Configure**. Get a key at 21st.dev/mcp.\n- **Grok Build** — grok plugin install 21st --trust (from the official xAI marketplace), then export API_KEY_21ST.\n- **Claude Code** — the repo carries a .claude-plugin/ manifest: /plugin install it from any marketplace that lists this repo, or load locally with claude --plugin-dir . and export API_KEY_21ST.\n\nThe plugin config expects the API key in the API_KEY_21ST variable in every client.\n\nLinks\n\n- 21st MCP setup & API keys: https://21st.dev/mcp\n- 21st CLI on npm: https://www.npmjs.com/package/@21st-dev/cli\n- 21st.dev — discover, publish, and generate UI components: https://21st.dev\n\nFAQ\n\n**Why did my old Magic MCP stop working?**\nThe Magic backend (magic.21st.dev) was superseded by the unified 21st MCP, and all old API keys were reset for security. Update to a fresh key from 21st.dev/mcp — your existing @21st-dev/magic config will then work again through this compatibility proxy, though we recommend switching to npx @21st-dev/cli@latest init.\n\n**Is /ui still a thing?**\nUse natural language: ask your agent to search 21st for components (search), or use hosted UI generation (generate) when AI is enabled. The old /ui, /21 trigger phrases were a convention of the legacy tools\' descriptions, not the protocol.', 'readme_raw_len': 4347, 'one_line_zh': '参考 21st.dev 设计工程师的作品创建 UI 组件', 'one_line_plain': 'Create UI components inspired by 21st.dev design engineers'}, {'id': 5251, 'key': 'louis-cfm-coucou', 'kind': 'repo', 'name': 'Louis-CFM/coucou', 'one_line': "A tiny friend in your Mac's notch and on your iPhone that keeps an eye on your AI coding agents: Claude Code, Codex, Cursor, Gemini CLI, Antigravity and more. Approve from the notch or your Lock Screen.", 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': "A tiny friend in your Mac's notch and on your iPhone that keeps an eye on your AI coding agents: Claude Code, Codex, Cursor, Gemini CLI, Antigravity and more. Approve from the notch or your Lock Screen.", 'url': 'https://github.com/Louis-CFM/coucou', 'source': 'github', 'source_url': 'https://github.com/Louis-CFM/coucou', 'downloads': None, 'stars': 5579, 'installs': None, 'mentions': 1, 'score': 112.37, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-10', 'times_seen': 1, 'icon': 'https://github.com/Louis-CFM.png?size=64', 'official_desc': "A tiny friend in your Mac's notch and on your iPhone that keeps an eye on your AI coding agents: Claude Code, Codex, Cursor, Gemini CLI, Antigravity and more. Approve from the notch or your Lock Screen.", 'stars_own': 5579, 'stars_creator': None, 'stars_own_repo': 'Louis-CFM/coucou', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Coucou\n\n**A tiny friend that lives in your Mac\'s notch — or at the top of your screen on Windows and Linux — and keeps an eye on your AI coding agent sessions. And now on your phone too: iPhone and Android.**\n\nApprove permissions, watch your agents work, drop a file, chat with Claude — all without leaving what you\'re doing. Walk away from your Mac and Mochi follows you to your iPhone: Lock Screen, Dynamic Island, widgets, Siri.\n\n**New in 0.4: Coucou is a real Mac app.** Hold a key anywhere, speak, let go: your words are pasted where you type, like Wispr Flow — free, open source, and offline with Parakeet. Record your calls without a bot in the meeting and get the transcript, the summary and the to-dos.\n\n \n \n \n \n \n \n \n \n \n \n \n\n \n\n \n\n---\n\nWhy\n\nSome studios showed off gorgeous notch companions… and never let anyone use them.\n**Coucou is the open version.** Every line of code is open source under the MIT License: read it, fork it, learn from it. The Coucou name, Mochi and the sounds stay © Louis Raillé (see License).\n\nMeet **Mochi**: a soft little squircle with big eyes that pops out of your notch, waves hello, follows your cursor with its eyes, gets annoyed when you poke it (and dizzy if you insist), and tells you the moment Claude Code needs you.\n\nCoucou on the Mac: dictation, notes, one window\n\n \n \n \n\nSince 0.4, Coucou has its own window and sits in the Dock. On the left, **Flow** — everything about your voice — and **Notch**, all the settings of the island. *(macOS, GitHub build)*\n\n- 🎙️ **Flow dictation** — hold a key anywhere on the Mac (right ⌥ by default, or fn, right ⌘ or right ⌃), speak, let go: your words are pasted where you type, in any app. Two quick taps for hands-free, Esc to cancel, ⌃⌘V to paste the last one again. Your clipboard gets back what it held.\n- 💊 **The pill** — a small see-through pill at the bottom of the screen (or on its left or right edge). Hover it for a toolbar: dictate, record a call, open Coucou. While you speak, Mochi listens and nine dots follow your voice.\n- 🦜 **Parakeet, offline** — run NVIDIA\'s Parakeet on your Mac\'s Neural Engine through FluidAudio: more accurate, 25 languages, nothing leaves your Mac. One click to download (about 650 MB), Apple silicon only. Until then the Mac\'s own recognition is used.\n- ✍️ **Your words, your way** — a dictionary of names Coucou always spells right, replacements, snippets (say a short phrase, paste a whole text), and an optional clean-up by Claude with your own key: as said, natural or polished, per app.\n- 📝 **Notetaker** — click Record a call: Coucou records your microphone and what your Mac plays, no bot joins the call. Then you get the transcript person by person, a summary, the key points and the to-dos, written by Claude with your key or by Apple Intelligence on your Mac. The audio is deleted once the transcript is written. Tell the people in the call that you record.\n- 📊 **Statistics** — words dictated, time saved, words per minute, days in a row and the apps you dictate in. Counted on your Mac, never sent anywhere.\n\n \n \n \n \n \n \n \n\nFlow dictation and the Notetaker are on the Mac for now; Windows and Linux are next.\n\nFeatures\n\n- 🤖 **Claude Code, Cursor, Codex, Gemini CLI, Antigravity, Copilot CLI, Muse Code, OpenCode, Amp, Hermes, Claude Desktop and other agents, live** — see every session in your notch: what it reads, edits and runs, step by step. Tag a hook payload with coucou_agent to give any agent its own pill (see docs/AGENTS.md). Finished? Mochi does a happy little jump.\n- See what Claude is editing, live in the notch: each file modification shows the file name and +N −M counts in the ticker, tap to read the full diff\n- ✅ **Approve and answer from the notch** — Claude Code permission requests show up with **Allow / Deny / Always**; AskUserQuestion prompts show the choices right in the notch (single or multi-select, up to 4 questions). One click, or "Reply in terminal" to fall back to the CLI. Codex also gets Allow / Deny.\n- 🧑\u200d💻 **Jump to the right terminal** — open the exact terminal window of a session *(macOS)*.\n- 💬 **Chat with Claude, Gemini, OpenAI, DeepSeek, Qwen, Moonshot, or a local model (Ollama / LM Studio)** — click the model name above the chat box to switch provider and pick a model. Cloud providers use your own API key; local providers connect to a server running on your Mac. *(Gemini, OpenAI, DeepSeek, Qwen, Moonshot and local models: macOS)*\n- 📈 **Portfolio pill** *(macOS)* — track your crypto across 16 networks (Bitcoin, Ethereum, Solana, Polygon, BNB, Avalanche, Arbitrum, Optimism, Base, Dogecoin, Litecoin, TRON, XRP, Cardano, Cosmos, NEAR) and 5 exchanges (Binance, OKX, Bybit, Kraken, Coinbase) in the notch. Live balances, 7-day sparklines and 24-hour price change per holding. Read-only API keys for exchanges, public addresses for wallets — nothing leaves your Mac.\n- 📊 **Claude plan usage** *(macOS, GitHub build)* — a small pill in the notch header shows your 5-hour and weekly Claude plan limits. Enable it from Settings → Agents → Plan usage. Pro and Max plans only.\n- 📊 **Codex plan usage** *(macOS, GitHub build)* — a Codex pill next to it shows your Codex limits and how many free resets you have left, read from the Codex CLI. Turn it on in Settings → Agents → Plan usage.\n- 📋 **Declare the tools you use** — open Settings → Active pills and pick your main workspace tool (VS Code, Cursor, Codex or Antigravity), then toggle up to 4 more: Gemini CLI, Anthropic, Google AI, OpenAI, Ollama, LM Studio and service integrations *(macOS)*.\n- 📎 **Drop a file on the notch** — Mochi turns into a box and swallows it, then ask a question about it or send it by email *(email: macOS, Mail.app)*.\n- 🖥️ **Mochi on the desktop** — drag Mochi out of the island to set him loose on your desktop: he floats as a 120 pt companion, follows your cursor, wears his outfit, reacts to alerts by flying home and flying back, and comes back where you left him on next launch.\n- 🪟 **Drag Mochi onto any window** — attach that window as context for Claude *(macOS)*.\n- 🔌 **Integrations** — Stripe payments, n8n workflows, GitHub (open PRs, reviews requested, CI status), Vercel deployments, Resend emails, Notion, Cal.com. Each one gets its own little colored Mochi.\n- 🎵 **Apple Music pill** *(macOS, GitHub build)* — add the Apple Music pill in Settings → Active pills to see what\'s playing and control playback from the notch; Mochi dances while it plays.\n- 🎧 **Spotify pill** *(macOS, GitHub build)* — add the Spotify pill in Settings → Active pills: album cover, title and artist, a progress bar you drag to seek, play/pause, previous/next, shuffle, repeat and volume, right from the notch; Mochi dances while it plays.\n- 👗 **Dress Mochi up** — right-click him for the wardrobe. He also dresses up for the seasons on his own.\n- ⌨️ **Keyboard shortcuts** — open the chat, jump to an alert or a terminal, switch pills, mute, send Mochi to the desktop or open the wardrobe from anywhere; all customizable in Settings → Shortcuts.\n- 🎭 **A real character** — idle breathing, blinks, eyes on a sphere that follow your mouse, emotes, 28 handcrafted sounds, a greeting on launch.\n- 🔊 **Your own sounds** *(macOS)* — drop a file named like one of Mochi\'s sounds (finish.wav, approval.mp3…) in Settings → General → Sound → **Open sounds folder** to replace it.\n- 🗣️ **«\u202fOK Coucou\u202f»** *(macOS, GitHub build)* — talk to Mochi like Jarvis. Say «\u202fOK Coucou\u202f», then ask: «\u202fhow much did I make on Stripe today?\u202f», «\u202fswap GitHub for Stripe\u202f», «\u202fplay some Daft Punk\u202f», «\u202fwho won the game last night?\u202f», «\u202fsend an email to Enzo to tell him the image is ready\u202f». Claude is the brain (your own Anthropic API key, set in Settings → Chat); speech recognition runs on your Mac, and only your words and the data Coucou reads to answer go to Anthropic, never audio. Mochi listens, thinks and answers out loud; emails open in the notch and leave only when you click Send. Settings → Voice.\n- 🎙️ **Dictate in the chat** *(macOS, GitHub build)* — clic', 'readme_raw_len': 30717, 'one_line_zh': '住在 Mac 刘海与 iPhone 上、帮你盯住 AI 编程 Agent 的小助手，可在刘海或锁屏上批准操作', 'one_line_plain': "Watch your AI coding agents from your Mac's menu bar or iPhone lock screen and approve their actions"}, {'id': 5317, 'key': 'evalstate-fast-agent', 'kind': 'repo', 'name': 'evalstate/fast-agent', 'one_line': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'url': 'https://github.com/evalstate/fast-agent', 'source': 'github', 'source_url': 'https://github.com/evalstate/fast-agent', 'downloads': None, 'stars': 3943, 'installs': None, 'mentions': 1, 'score': 110.86, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-19', 'times_seen': 18, 'icon': 'https://github.com/evalstate.png?size=64', 'official_desc': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'stars_own': 3943, 'stars_creator': None, 'stars_own_repo': 'evalstate/fast-agent', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'uvx fast-agent-mcp@latest', 'install_src': 'HEAD/README.md', 'install_conf': 'medium', 'desc_src': 'github-api', 'readme_body': 'Start Here\n\n> [!TIP]\n> Please see https://fast-agent.ai for latest documentation.\n\n**fast-agent** is a flexible way to interact with LLMs, excellent for use as a Coding Agent, Development Toolkit, Evaluation or Workflow platform.\n\nTo start an interactive session with shell support, install uv and run\n\n \n\nTo start coding with Hugging Face inference providers or use your OpenAI Codex plan:\n\n \n\nEnter a shell with !, or run shell commands e.g. ! cd web && npm run build.\n\nManage skills with the /skills command, and connect to MCP Servers with /connect. The default **fast-agent** registry contains skills to let you set up LSP, Agent and Tool Hooks, Compaction strategies, Automation and more.\n\n \n\nIt\'s recommended to install **fast-agent** to set up the shell aliases and other tooling.\n\n \n\nUse local models with the generic provider, or automatically create the correct configuration for llama.cpp:\n\n \n\nAny **fast-agent** setup or program can be used with any ACP client - the simplest way is to use fast-agent-acp:\n\n \n\n**fast-agent** enables you to create and interact with sophisticated multimodal Agents and Workflows in minutes. It is the first framework with complete, end-to-end tested MCP Feature support including Sampling and Elicitations.\n\nfast-agent is CLI-first, with an optional prompt_toolkit-powered interactive terminal prompt (TUI-style input, completions, and in-terminal menus); responses can stream live to the terminal via rich without relying on full-screen curses UIs or external GUI overlays.\n\n \n\nThe simple declarative syntax lets you concentrate on composing your Prompts and MCP Servers to build effective agents.\n\nModel support is comprehensive with native support for Anthropic, OpenAI and Google providers as well as Azure, Ollama, Deepseek and dozens of others via TensorZero. Structured Outputs, PDF and Vision support is simple to use and well tested. Passthrough and Playback LLMs enable rapid development and test of Python glue-code for your applications.\n\nRecent features include:\n\n- Agent Skills (SKILL.md)\n- MCP Apps\n- OpenAI Apps SDK\n- Shell Mode\n- Advanced MCP Transport Diagnsotics\n- MCP Elicitations\n\n \n\nfast-agent is the only tool that allows you to inspect Streamable HTTP Transport usage - a critical feature for ensuring reliable, compliant deployments. OAuth is supported with KeyRing storage for secrets. Use the fast-agent auth command to manage.\n\n> [!IMPORTANT]\n>\n> Documentation is included in this repository under docs/. Use the docs helper script from the\n> repository root to install, generate, build, serve, screenshot, and assess the site.\n\nAgent Application Development\n\nPrompts and configurations that define your Agent Applications are stored in simple files, with minimal boilerplate, enabling simple management and version control.\n\nChat with individual Agents and Components before, during and after workflow execution to tune and diagnose your application. Agents can request human input to get additional context for task completion.\n\nSimple model selection makes testing Model MCP Server interaction painless. You can read more about the motivation behind this project here\n\n \n\nGet started:\n\nStart by installing the uv package manager for Python. Then:\n\n \n\nWith no configured model, an interactive run opens the model picker.\nAutomation and server runs must pass --model, set FAST_AGENT_MODEL, configure\ndefault_model, or declare a model on the agent.\n\nFor packaged starter agents, use fast-agent go --pack --model .\nThis installs the pack into the selected fast-agent home if needed, then\nstarts go normally. --model is a fallback for cards without an explicit\nmodel setting; a model declared directly in an AgentCard still wins.\n\nOther quickstart examples include a Researcher Agent (with Evaluator-Optimizer workflow) and Data Analysis Agent (similar to the ChatGPT experience), demonstrating MCP Roots support.\n\n> [!TIP]\n> Windows Users - there are a couple of configuration changes needed for the Filesystem and Docker MCP Servers - necessary changes are detailed within the configuration files.\n\nBasic Agents\n\nDefining an agent is as simple as:\n\n \n\nWe can then send messages to the Agent:\n\n \n\nOr start an interactive chat with the Agent:\n\n \n\nHere is the complete sizer.py Agent application, with boilerplate code:\n\n \n\nThe Agent can then be run with uv run sizer.py.\n\nSpecify a model with the --model switch - for example uv run sizer.py --model sonnet.\n\nModel strings also accept query overrides. For example:\n\n- uv run sizer.py --model "gpt-5?reasoning=low"\n- uv run sizer.py --model "claude-sonnet-4-6?web_search=on"\n- uv run sizer.py --model "claude-sonnet-4-5?context=1m"\n- uv run sizer.py --model "gpt-5?poll_period=240"\n\nFor Anthropic models, ?context=1m is only needed for earlier Sonnet 4 / Sonnet 4.5\nmodels that still require the explicit 1M context opt-in. Claude Sonnet 4.6 and\nClaude Opus 4.6 already use their long context window by default, so ?context=1m\nis accepted for backward compatibility but is unnecessary there.\n\nCombining Agents and using MCP Servers\n\n_To generate examples use fast-agent quickstart workflow. This example can be run with uv run workflow/chaining.py. Place fast-agent.yaml in the active fast-agent home, or pass an explicit config path when needed._\n\nAgents can be chained to build a workflow, using MCP Servers defined in the fast-agent.yaml file:\n\n \n\nAll Agents and Workflows respond to .send("message") or .prompt() to begin a chat session.\n\nSaved as social.py we can now run this workflow from the command line with:\n\n \n\nAdd the --quiet switch to disable progress and message display and return only the final response - useful for simple automations.\n\nMAKER\n\nMAKER (“Massively decomposed Agentic processes with K-voting Error Reduction”) wraps a worker agent and samples it repeatedly until a response achieves a k-vote margin over all alternatives (“first-to-ahead-by-k” voting). This is useful for long chains of simple steps where rare errors would otherwise compound.\n\n- Reference: Solving a Million-Step LLM Task with Zero Errors\n- Credit: Lucid Programmer (PR author)\n\n \n\nAgents As Tools\n\nThe Agents As Tools workflow takes a complex task, breaks it into subtasks, and calls other agents as tools based on the main agent instruction.\n\nThis pattern is inspired by the OpenAI Agents SDK Agents as tools feature.\n\nWith child agents exposed as tools, you can implement routing, parallelization, and orchestrator-workers decomposition directly in the instruction (and combine them). Multiple tool calls per turn are supported and executed in parallel.\n\nCommon usage patterns may combine:\n\n- Routing: choose the right specialist tool(s) based on the user prompt.\n- Parallelization: fan out over independent items/projects, then aggregate.\n- Orchestrator-workers: break a task into scoped subtasks (often via a simple JSON plan), then coordinate execution.\n\n \n\nExtended example and all params sample is available in the repository as\nexamples/workflows/agents_as_tools_extended.py.\n\nMCP OAuth (v2.1)\n\nFor SSE and HTTP MCP servers, OAuth is enabled by default with minimal configuration. A local callback server is used to capture the authorization code, with a paste-URL fallback if the port is unavailable.\n\n- Minimal per-server settings in fast-agent.yaml:\n\n \n\n- The OAuth client uses PKCE and in-memory token storage (no tokens written to disk).\n- Token persistence: by default, tokens are stored securely in your OS keychain via keyring. If a keychain is unavailable (e.g., headless container), in-memory storage is used for the session.\n- To force in-memory only per server, set:\n\n \n\n- To disable OAuth for a specific server , set auth.oauth: false for that server.\n\nMCP Ping (optional)\n\nThe MCP ping utility can be enabled by either peer (client or server). See the Ping overview.\n\nClient-side pinging is configured per server (default: 30s interval, 3 missed pings):\n\n \n\n**Global tools (@fast.tool)** — available to all agents that don\'t declare their own tools:\n\n \n\nAgen', 'readme_raw_len': 10194, 'one_line_zh': '编写、构建与评估 Agent，出色的 Model 与 Skills/MCP/ACP/A2A 支持', 'one_line_plain': 'Create, run and test code agents with support for Model, Skills, MCP, ACP and A2A protocols'}, {'id': 5498, 'key': 'davepoon-buildwithclaude', 'kind': 'repo', 'name': 'davepoon/buildwithclaude', 'one_line': 'A single hub to find Claude Skills, Agents, Commands, Hooks, Plugins, and Marketplace collections to extend Claude Code, Claude Desktop, Agent SDK and OpenClaw', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'A single hub to find Claude Skills, Agents, Commands, Hooks, Plugins, and Marketplace collections to extend Claude Code, Claude Desktop, Agent SDK and OpenClaw', 'url': 'https://github.com/davepoon/buildwithclaude', 'source': 'github', 'source_url': 'https://github.com/davepoon/buildwithclaude', 'downloads': None, 'stars': 3654, 'installs': None, 'mentions': 1, 'score': 110.24, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-12', 'times_seen': 1, 'icon': 'https://github.com/davepoon.png?size=64', 'official_desc': 'A single hub to find Claude Skills, Agents, Commands, Hooks, Plugins, and Marketplace collections to extend Claude Code, Claude Desktop, Agent SDK and OpenClaw', 'stars_own': 3654, 'stars_creator': None, 'stars_own_repo': 'davepoon/buildwithclaude', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Build with Claude\n\n**Claude Skills, Agents, Commands, Hooks, Plugins, Marketplaces collections for and extend Claude Code**\n\n \n \n \n \n\n**A plugin marketplace and discovery platform for Claude Code. Browse curated plugins, discover community contributions, and extend your Claude Code workflows.**\n\nQuick Start\n\n \n\nWhat's Included\n\nBuild with Claude Plugins\n\nCurated collections maintained in this repository:\n\n| Type | Count | Description |\n|------|-------|-------------|\n| **Agents** | 117 | Specialized AI experts (Python, Go, DevOps, Security, etc.) |\n| **Commands** | 175 | Slash commands for automation (/commit, /docs, /tdd) |\n| **Hooks** | 28 | Event-driven automation (notifications, git, formatting) |\n| **Skills** | 26 | Reusable capabilities from plugins |\n| **Plugins** | 51 | Bundled plugin packages by category |\n\nCommunity Discovery\n\nThe platform indexes plugins from the broader Claude Code ecosystem:\n\n- **20k+ Community Plugins** from external marketplaces\n- **4,500+ MCP Servers** for database, API, and tool connections\n- **1,100+ Plugin Marketplaces** from the community\n\nWeb UI\n\nBrowse, search, and explore everything at **buildwithclaude.com**\n\n \n\n \n\n \n\n \n\n \n\nFeatures\n\n- Browse all plugin types with filtering\n- Search across plugins, agents, commands, hooks, skills\n- Copy install commands with one click\n- View full documentation and usage examples\n- Discover MCP servers and community plugins\n\nInstallation Options\n\nOption 1: Plugin Marketplace (Recommended)\n\n \n\nOption 2: Manual Installation\n\n \n\nAvailable Plugin Categories\n\nAgents (11 categories)\n\n- **Development & Architecture** - Backend, frontend, mobile, GraphQL experts\n- **Language Specialists** - Python, Go, Rust, TypeScript, C/C++ experts\n- **Quality & Security** - Code review, security audit, debugging\n- **Infrastructure & Operations** - DevOps, cloud, database optimization\n- **Data & AI** - ML engineering, data pipelines, AI development\n- **Crypto & Blockchain** - Trading systems, DeFi, Web3 development\n\nBrowse all agents →\n\n \n\nCommands (22 categories)\n\n- **Version Control** - Commit, PR creation, branch management\n- **Code Analysis** - Testing, review, optimization\n- **Documentation** - Docs generation, changelogs, API specs\n- **Project Management** - Todos, PRDs, task tracking\n\nBrowse all commands →\n\n \n\nHooks (8 categories)\n\n- **Notifications** - Slack, Discord, Telegram alerts\n- **Git** - Auto-staging, smart commits\n- **Development** - Lint on save, auto-format\n- **Security** - File protection, vulnerability scanning\n\nBrowse all hooks →\n\n \n\nUsage Examples\n\nUsing Agents\n\nAgents are automatically invoked based on context, or explicitly called:\n\n \n\nUsing Commands\n\nCommands use the / prefix:\n\n \n\nUsing Hooks\n\nHooks run automatically on events like tool calls or session start.\n\nContributing\n\nWe welcome contributions!\n\nAdding Plugins\n\n1. Create a new directory in plugins/ following the naming convention\n2. Add your plugin files (agents, commands, hooks)\n3. Run npm test to validate\n4. Submit a pull request\n\nPlugin Format\n\n**Agent** (plugins/agents-*/agents/*.md):\n \n\n**Command** (plugins/commands-*/commands/*.md):\n \n\n**Hook** (plugins/hooks-*/hooks/*.md):\n \n\nSee CONTRIBUTING.md for detailed guidelines.\n\nLinks\n\n- **Web UI**: buildwithclaude.com\n- **Documentation**: Claude Code Docs\n- **Plugin Marketplaces**: Plugin Docs\n- **Issues**: GitHub Issues\n- **Visual Index**: Vexilo · A field guide to Claude Code — Interactive index of 31 agents · 99 commands · 123 skills · 13 rules, organized around the 5-step workflow. (companion repo)\n\nLicense\n\nMIT License - see LICENSE for details.\n\n---\n\nMade with ❤️ by Dave Poon", 'readme_raw_len': 3628, 'one_line_zh': '一站式查找 Claude 技能、Agent、命令、钩子、插件与市场合集，用于扩展 Claude Code、Claude Desktop、Agent SDK 与 OpenClaw', 'one_line_plain': 'Browse and install Claude Skills, Agents, Commands, Hooks, Plugins and Marketplace collections'}, {'id': 8089, 'key': 'skillsmp-com-creators-aieradev-clypra-agents-skills-bugfix-regression', 'kind': 'skill', 'name': 'bugfix-regression', 'one_line': 'Guides AI agents through diagnosing, reproducing, fixing, and permanently testing defects in Clypra', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/aieradev/clypra/agents-skills-bugfix-regression', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/aieradev/clypra/agents-skills-bugfix-regression', 'downloads': None, 'stars': 3322, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/AIEraDev.png?size=64', 'official_desc': 'Guides AI agents through diagnosing, reproducing, fixing, and permanently testing defects in Clypra. Activate when investigating crashes, rendering freezes, audio/video drift, playhead glitches, timeline corruption, or unexpected behavior. Enforces evidence-based diagnosis and mandatory regression t', 'stars_own': None, 'stars_creator': 3329, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'aieradev/clypra', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '引导 AI Agent 诊断、复现、修复并永久测试 Clypra 中的缺陷', 'one_line_plain': 'Guide AI agents to diagnose, reproduce, fix, and permanently test defects in Clypra'}, {'id': 4350, 'key': 'punkpeye-fastmcp', 'kind': 'mcp', 'name': 'FastMCP', 'one_line': '📇 - High-level framework for building MCP servers in TypeScript', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A TypeScript framework for building MCP servers.', 'url': 'https://github.com/punkpeye/fastmcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/punkpeye/fastmcp', 'downloads': None, 'stars': 3275, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/punkpeye.png?size=64', 'official_desc': '📇 - A high-level framework for building MCP servers in TypeScript', 'stars_own': 3275, 'stars_creator': None, 'stars_own_repo': 'punkpeye/fastmcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'FastMCP\n\nA TypeScript framework for building MCP servers capable of handling client sessions.\n\n> [!IMPORTANT]\n>\n> FastMCP implements the legacy, handshake-based MCP revisions (2025-11-25 and earlier). It does not support the current specification, 2026-07-28, which made the protocol stateless — no initialize handshake and no Mcp-Session-Id. For a framework targeting the current spec, use ViteMCP.\n\nFeatures\n\n- Simple Tool, Resource, Prompt definition\n- OpenAPI to MCP conversion\n- Authentication\n- Passing headers through context\n- Session ID and Request ID tracking\n- Sessions\n- Image content\n- Audio content\n- Embedded\n- Logging\n- Error handling\n- HTTP Streaming (with SSE compatibility)\n- HTTPS Support for secure connections\n- Custom HTTP routes for REST APIs, webhooks, and admin interfaces\n- Edge Runtime Support for Cloudflare Workers, Deno Deploy, and more\n- Stateless mode for serverless deployments\n- CORS (enabled by default)\n- Progress notifications\n- Streaming output\n- Typed server events\n- Prompt argument auto-completion\n- Sampling\n- Elicitation\n- Configurable ping behavior\n- Health-check endpoint\n- Roots\n- In-memory transport for unit testing without binding a port\n- CLI for testing and debugging\n\nWhen to use FastMCP over the official SDK?\n\nFastMCP is built on top of the official SDK.\n\nThe official SDK provides foundational blocks for building MCPs, but leaves many implementation details to you:\n\n- Initiating and configuring all the server components\n- Handling of connections\n- Handling of tools\n- Handling of responses\n- Handling of resources\n- Adding prompts, resources, resource templates\n- Embedding resources, image and audio content blocks\n\nFastMCP eliminates this complexity by providing an opinionated framework that:\n\n- Handles all the boilerplate automatically\n- Provides simple, intuitive APIs for common tasks\n- Includes built-in best practices and error handling\n- Lets you focus on your MCP\'s core functionality\n\n**When to choose FastMCP:** You want to build MCP servers quickly without dealing with low-level implementation details.\n\n**When to use the official SDK:** You need maximum control or have specific architectural requirements. In this case, we encourage referencing FastMCP\'s implementation to avoid common pitfalls.\n\nInstallation\n\n \n\nQuickstart\n\n> [!NOTE]\n>\n> There are many real-world examples of using FastMCP in the wild. See the Showcase for examples.\n\n \n\n_That\'s it!_ You have a working MCP server.\n\nYou can test the server in terminal with:\n\n \n\nIf you are looking for a boilerplate repository to build your own MCP server, check out fastmcp-boilerplate.\n\nRemote Server Options\n\nFastMCP supports multiple transport options for remote communication, allowing an MCP hosted on a remote machine to be accessed over the network.\n\nHTTP Streaming\n\nHTTP streaming provides a more efficient alternative to SSE in environments that support it, with potentially better performance for larger payloads.\n\nYou can run the server with HTTP streaming support:\n\n \n\nThis will start the server and listen for HTTP streaming connections on http://localhost:8080/mcp.\n\n> **Note:** You can also customize the endpoint path using the httpStream.endpoint option (default is /mcp).\n\n> **Note:** To serve HTTP streaming and built-in OAuth routes under an issuer path, set httpStream.basePath (for example, /issuer1). This exposes authorization server metadata at /.well-known/oauth-authorization-server/issuer1 per RFC 8414.\n\n> **Note:** This also starts an SSE server on http://localhost:8080/sse.\n\nYou can connect to these servers using the appropriate client transport.\n\nFor HTTP streaming connections:\n\n \n\nFor SSE connections:\n\n \n\nHTTPS Support\n\nFastMCP supports HTTPS for secure connections by providing SSL certificate options:\n\n \n\nThis will start the server with HTTPS on https://localhost:8443/mcp.\n\n**SSL Options:**\n\n- sslCert - Path to SSL certificate file\n- sslKey - Path to SSL private key file\n- sslCa - (Optional) Path to CA certificate for mutual TLS authentication\n\n**For testing**, you can generate self-signed certificates:\n\n \n\n**For production**, obtain certificates from a trusted CA like Let\'s Encrypt.\n\nSee the https-server example for a complete demonstration.\n\nCORS Configuration\n\nBy default, FastMCP enables CORS with a standard set of allowed headers. You can customize the CORS behavior by passing a cors option:\n\n \n\nThe cors option accepts:\n\n- true (default) - enable CORS with default settings\n- false - disable CORS entirely\n- An object with these fields:\n - origin - a string, array of strings, or a function (origin: string) => boolean\n - allowedHeaders - a string or array of strings\n - methods - array of allowed HTTP methods\n - exposedHeaders - array of headers to expose\n - credentials - boolean to allow credentials\n - maxAge - preflight cache duration in seconds\n\nThe CorsOptions type is exported from fastmcp for convenience.\n\nCustom HTTP Routes\n\nFastMCP allows you to add custom HTTP routes alongside MCP endpoints, enabling you to build comprehensive HTTP services that include REST APIs, webhooks, admin interfaces, and more - all within the same server process.\n\n \n\nCustom routes use the underlying Hono app returned by server.getApp() and support:\n\n- Hono\'s HTTP methods: get, post, put, delete, patch, options, and more\n- Path parameters (:param) and wildcards (*)\n- Query string parsing\n- JSON, text, form, and other body helpers from c.req\n- Custom status codes and headers\n- Middleware and route groups through Hono\n\nRoutes are matched in the order they are registered, allowing you to define specific routes before catch-all patterns.\n\nPublic and Protected Routes\n\nCustom Hono routes are public unless you add your own route middleware or authentication checks. For protected custom routes, put your auth logic in a reusable helper and call it from both FastMCP\'s authenticate option and your Hono route handlers:\n\n \n\nPublic routes are perfect for:\n\n- OAuth discovery endpoints (.well-known/*)\n- Health checks and status pages\n- Static assets and documentation\n- Webhook endpoints from external services\n- Public APIs that don\'t require user authentication\n\nSee the custom-routes example for a complete demonstration.\n\nEdge Runtime Support\n\nFastMCP supports edge runtimes like Cloudflare Workers, enabling deployment of MCP servers to the edge with minimal latency worldwide.\n\nChoosing Between FastMCP and EdgeFastMCP\n\n| Use Case | Class | Import |\n| ------------------------------- | ------------- | -------------------------------------------- |\n| Node.js, Express, Bun | FastMCP | import { FastMCP } from "fastmcp" |\n| Cloudflare Workers, Deno Deploy | EdgeFastMCP | import { EdgeFastMCP } from "fastmcp/edge" |\n\n| Feature | FastMCP | EdgeFastMCP |\n| -------------------- | ------------------------------ | -------------------------------------- |\n| Runtime | Node.js | Edge (V8 isolates) |\n| Start method | server.start({ port }) | export default server |\n| Transport | stdio, httpStream, SSE | HTTP Streamable only |\n| Sessions | Stateful or stateless | Stateless only |\n| File system | Yes | No |\n| OAuth/Authentication | Built-in authenticate option | Use Hono middleware (built-in planned) |\n| Custom routes | server.getApp() | server.getApp() |\n\n> **Note:** Built-in authentication for EdgeFastMCP is planned for a future release. Both FastMCP and EdgeFastMCP use Hono internally, so there\'s no technical barrier—EdgeFastMCP was simply written before OAuth was added to FastMCP. PRs are welcome to add an authenticate option that accepts web Request instead of Node.js http.IncomingMessage.\n>\n> In the meantime, use Hono middleware:\n>\n> \n\nCloudflare Workers\n\nTo deploy FastMCP to Cloudflare Workers, use the EdgeFastMCP class from the /edge subpath:\n\n \n\nEdge Runtime Differences\n\nWhen running on edge runtimes:\n\n- **Stateless by default**: Each request is handled independently\n- **No filesystem access**: Use fetch APIs for external data\n- **V8 Isolates**: Fast cold starts and efficient resource usage\n- ', 'readme_raw_len': 40996, 'one_line_zh': '用于在 TypeScript 中构建 MCP 服务器的高层框架', 'one_line_plain': 'Build MCP servers using a TypeScript framework'}, {'id': 1488, 'key': 'jpisnice-shadcn-ui-mcp-server', 'kind': 'mcp', 'name': 'Jpisnice/shadcn-ui-mcp-server', 'one_line': '📇 🏠 - Access shadcn/ui v4 components, blocks, demos and metadata', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation,compaitable with react,svelte 5,vue & React Native', 'url': 'https://github.com/Jpisnice/shadcn-ui-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Jpisnice/shadcn-ui-mcp-server', 'downloads': None, 'stars': 3062, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Jpisnice.png?size=64', 'official_desc': '📇 🏠 - Access shadcn/ui v4 components, blocks, demos and metadata.', 'stars_own': 3062, 'stars_creator': None, 'stars_own_repo': 'Jpisnice/shadcn-ui-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Shadcn UI v4 MCP Server\n\n \n \n \n\n \n\n> **🚀 The fastest way to integrate shadcn/ui components into your AI workflow**\n\nA Model Context Protocol (MCP) server that provides AI assistants with comprehensive access to shadcn/ui v4 components, blocks, demos, and metadata. Seamlessly retrieve React, Svelte, Vue, and React Native implementations for your AI-powered development workflow.\n\n✨ Key Features\n\n- **🎯 Multi-Framework Support** - React, Svelte, Vue, and React Native implementations\n- **📦 Component Source Code** - Latest shadcn/ui v4 TypeScript source\n- **🎨 Component Demos** - Example implementations and usage patterns \n- **🏗️ Blocks Support** - Complete block implementations (dashboards, calendars, forms)\n- **📋 Metadata Access** - Dependencies, descriptions, and configuration details\n- **🔍 Directory Browsing** - Explore repository structures\n- **⚡ Smart Caching** - Efficient GitHub API integration with rate limit handling, plus protocol cache hints (ttlMs/cacheScope) on list and read results\n- **⌨️ Argument Completion** - Autocomplete for prompt and resource-template arguments (component names, package managers, build tools, and more)\n- **🌐 Streamable HTTP Transport** - Stateless /mcp endpoint for multi-client deployments (MCP spec 2026-07-28)\n- **🐳 Docker Ready** - Production-ready containerization with Docker Compose\n\n🚀 Quick Start\n\n \n\n**🎯 Get your GitHub token in 2 minutes**: docs/getting-started/github-token.md\n\n📦 One-Click Installation (Claude Desktop)\n\nDownload and double-click the .mcpb file for instant installation:\n\n1. Download shadcn-ui-mcp-server.mcpb from Releases\n2. Double-click the file - Claude Desktop opens automatically\n3. Enter your GitHub token (optional, for higher rate limits)\n4. Click Install - tools are available immediately\n\n**Manual install:** Claude Desktop → Settings → MCP → Add Server → Browse → Select .mcpb file\n\n> **References:** Anthropic Desktop Extensions | Building MCPB\n\n🌐 HTTP Transport & Docker Deployment\n\nThe server implements the **2026-07-28 MCP specification** (via MCP TypeScript SDK v2) and stays compatible with 2025-era clients. To make your client use the modern protocol, or to require it, see the Modern Protocol Guide. Besides stdio, it can serve **Streamable HTTP** at a single stateless /mcp endpoint for multi-client and production deployments:\n\nQuick Start with HTTP\n \n\nTransport Modes\n- **stdio** (default) - Standard input/output for CLI usage\n- **http** - Streamable HTTP at /mcp\n- **dual** - Both stdio and HTTP simultaneously\n\n> The legacy SSE transport (/sse + /message) was removed. --mode sse is still accepted as a deprecated alias for http.\n\nDocker Examples\n \n\nEnvironment Variables\n- MCP_TRANSPORT_MODE - Transport mode (stdio|http|dual)\n- MCP_PORT - HTTP server port (default: 7423 - SHADCN on keypad!)\n- MCP_HOST - Host binding (default: 0.0.0.0)\n- MCP_CORS_ORIGINS - CORS origins (comma-separated)\n- MCP_PROTOCOL - Protocol eras to accept: any (default; 2026-07-28 plus 2025-era fallback) or modern (2026-07-28 only). CLI: --protocol\n- GITHUB_PERSONAL_ACCESS_TOKEN - GitHub API token\n- UI_LIBRARY - UI primitive library: radix (default) or base (React only)\n\n📚 Documentation\n\n| Section | Description |\n|---------|-------------|\n| 🚀 Getting Started | Installation, setup, and first steps |\n| ⚙️ Configuration | Framework selection, tokens, and options |\n| 🔌 Integration | Editor and tool integrations |\n| 📖 Usage | Examples, tutorials, and use cases |\n| 🎨 Frameworks | Framework-specific documentation |\n| 🐛 Troubleshooting | Common issues and solutions |\n| 🔧 API Reference | Tool reference and technical details |\n\n🎨 Framework Support\n\nThis MCP server supports four popular shadcn implementations:\n\n| Framework | Repository | Maintainer | Description |\n|-----------|------------|------------|-------------|\n| **React** (default) | shadcn/ui | shadcn | React components from shadcn/ui v4 |\n| **Svelte** | shadcn-svelte | huntabyte | Svelte components from shadcn-svelte |\n| **Vue** | shadcn-vue | unovue | Vue components from shadcn-vue |\n| **React Native** | react-native-reusables | Founded Labs | React Native components from react-native-reusables |\n\nUI Library (React only)\n\nshadcn/ui v4 supports two primitive libraries for React: **Radix UI** (default) and **Base UI**.\n\n \n\nClaude Desktop config example:\n \n\n🛠️ Essential Setup\n\n1. Get GitHub Token (Recommended)\n \n\n2. Run Server\n \n\n3. Integrate with Your Editor\n- **Autohand Code**: See Autohand Code Integration below\n- **Claude Code**: See Claude Code Integration below\n- **VS Code**: docs/integration/vscode.md\n- **Cursor**: docs/integration/cursor.md\n- **Claude Desktop**: docs/integration/claude-desktop.md\n- **Continue.dev**: docs/integration/continue.md\n\n💻 Autohand Code Integration\n\nUse the Autohand Code CLI to add the shadcn/ui MCP server:\n\n \n\nAdd --scope project before shadcn to save the server in the current project's .autohand configuration instead of your user configuration.\n\n💻 Claude Code Integration\n\nQuick Add (CLI)\n\n \n\nHTTP Transport\n\nFor production deployments with the Streamable HTTP transport:\n\n \n\nFramework-Specific Commands\n\nSee Claude Code Integration Guide for framework-specific commands (React, Svelte, Vue, React Native).\n\n> **Reference:** Claude Code MCP Documentation\n\n🎯 Use Cases\n\n- **AI-Powered Development** - Let AI assistants build UIs with shadcn/ui\n- **Multi-Client Deployments** - Stateless HTTP transport scales horizontally without sticky sessions\n- **Production Environments** - Docker Compose ready with health checks and monitoring\n- **Component Discovery** - Explore available components and their usage\n- **Multi-Framework Learning** - Compare React, Svelte, Vue, and React Native implementations\n- **Rapid Prototyping** - Get complete block implementations for dashboards, forms, etc.\n- **Code Generation** - Generate component code with proper dependencies\n\n📦 Installation\n\n \n\n🔨 Building from Source\n\nPrerequisites\n\n- Node.js >= 20.0.0\n- npm or pnpm\n\nBuild Steps\n\n \n\nRun Locally\n\n \n\nBuilding MCPB Package\n\nThe project includes a manifest.json following the MCPB specification. The .mcpb file is a ZIP archive containing the server, dependencies, and configuration.\n\nSee CONTRIBUTING.md for detailed packaging instructions.\n\n> **Reference:** Building Desktop Extensions with MCPB\n\n🔗 Quick Links\n\n- 📖 Full Documentation\n- 🚀 Getting Started Guide\n- 🌐 HTTP Transport & Docker Guide\n- 🧭 Modern Protocol Guide (2026-07-28)\n- 📝 Changelog\n- 🎨 Framework Comparison\n- 🔧 API Reference\n- 🐛 Troubleshooting\n- 💬 Issues & Discussions\n\n📄 License\n\nMIT License - see LICENSE for details.\n\n🙏 Acknowledgments\n\n- **shadcn** - For the amazing React UI component library\n- **huntabyte** - For the excellent Svelte implementation\n- **unovue** - For the comprehensive Vue implementation\n- **Founded Labs** - For the React Native implementation\n- **Anthropic** - For the Model Context Protocol specification\n\n---\n\n**Made with ❤️ by Janardhan Polle**\n\n**Star ⭐ this repo if you find it helpful!**", 'readme_raw_len': 6973, 'one_line_zh': '📇 🏠 - 访问 shadcn/ui v4 的组件、区块、演示与元数据', 'one_line_plain': 'shadcn/ui lets users access v4 components, blocks, demos, and metadata for UI building'}, {'id': 4499, 'key': 'vibheksoni-stealth-browser-mcp', 'kind': 'repo', 'name': 'vibheksoni/stealth-browser-mcp', 'one_line': 'Stealth browser automation for AI agents over MCP and the Chrome DevTools Protocol: navigation, network hooks, DOM extraction, and pixel-accurate UI cloning.', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Stealth browser automation for AI agents over MCP and the Chrome DevTools Protocol: navigation, network hooks, DOM extraction, and pixel-accurate UI cloning.', 'url': 'https://github.com/vibheksoni/stealth-browser-mcp', 'source': 'github', 'source_url': 'https://github.com/vibheksoni/stealth-browser-mcp', 'downloads': None, 'stars': 3054, 'installs': None, 'mentions': 1, 'score': 107.44, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-10', 'times_seen': 1, 'icon': 'https://github.com/vibheksoni.png?size=64', 'official_desc': 'Stealth browser automation for AI agents over MCP and the Chrome DevTools Protocol: navigation, network hooks, DOM extraction, and pixel-accurate UI cloning.', 'stars_own': 3054, 'stars_creator': None, 'stars_own_repo': 'vibheksoni/stealth-browser-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'pip install fastmcp', 'install_src': 'HEAD/README.md', 'install_conf': 'low', 'desc_src': 'github-api', 'readme_body': "Stealth Browser MCP\n\n**Stealth browser automation for MCP-compatible AI agents.**\n\nNavigate Cloudflare challenges, anti-bot checks, and login walls with real Chrome-family browser instances powered by nodriver, Chrome DevTools Protocol, and FastMCP.\n\n \n \n \n \n \n \n\n \n\n---\n\nSponsors\n\n \n\nNodeMaven — The most efficient proxy provider for Web Scraping and Automation with the Highest Quality IP on the market.\n\n**Why NodeMaven?**\n\n- ZIP targeting\n- 99.9% uptime\n- IP filtering: all proxies have fraud score Sponsored placement. NodeMaven is a paid sponsor of this project.\n\n---\n\nTable of Contents\n\n- Sponsors\n- Demo\n- Features\n- Quickstart\n- Agent Skill\n- Trust Model\n- Modular Architecture\n- Server Configuration\n- Toolbox\n- Stealth vs Playwright MCP\n- Troubleshooting\n- Examples\n- Showcase\n- Roadmap\n- Contributing\n- Support\n- License\n\n---\n\nDemo\n\n \n \n \n \n \n \n \n\n*Stealth Browser MCP passing a Cloudflare challenge, cloning UI elements, and inspecting network traffic through AI chat commands.*\n\n---\n\nFeatures\n\n- **Anti-bot resistance** - Has passed Cloudflare and Queue-It style challenges in testing; results vary by site, region, browser version, and detector version.\n- **101 tools across 11 sections** - From basic navigation to advanced CDP function execution.\n- **Modular loading** - Run the full 101-tool surface or a minimal 21-tool core; disable sections you do not need.\n- **Pixel-accurate element cloning** - Extract complete elements with CSS, DOM structure, events, animations, and assets via CDP.\n- **Network inspection** - Inspect requests, responses, headers, payloads, and captured bodies through your AI agent.\n- **Dynamic hook system** - Restricted Python hooks can intercept, block, redirect, fulfill, or modify request/response flows.\n- **CDP execution** - Run JavaScript, direct CDP commands, and pre-document scripts through trusted local MCP clients.\n- **Instant text input** - Paste large content via CDP, type with human-like keystrokes, or press real keys with trusted CDP key events.\n- **Cross-platform** - Windows, macOS, Linux, Docker, and CI environments with automatic browser/platform detection.\n- **Browser support** - Chrome, Chromium, and Microsoft Edge with automatic executable discovery.\n- **Clean MCP integration** - Works through standard MCP clients without custom brokers.\n\n---\n\nQuickstart\n\n1. Clone and install\n\nRequires Python 3.10+ and Chrome, Chromium, or Microsoft Edge.\n\n \n\n2. Add to your MCP client\n\n**Claude Code CLI (recommended):**\n\nWindows:\n\n \n\nmacOS / Linux:\n\n \n\nReplace the example path with your real clone path.\n\n \n Manual JSON configuration (Claude Desktop, Cursor, etc.) \n\nWindows (%APPDATA%\\Claude\\claude_desktop_config.json):\n\n \n\nmacOS / Linux (~/Library/Application Support/Claude/claude_desktop_config.json):\n\n \n\n \n\n \n Experimental FastMCP CLI install \n\nThese commands are provided for users who prefer FastMCP's installer. The stdio JSON setup above is the recommended path.\n\n \n\n \n\n3. Test it\n\nRestart your MCP client and ask your agent:\n\n> Use stealth-browser to navigate to https://example.com and take a screenshot.\n\n---\n\nAgent Skill\n\nThis repo includes an agent skill at skills/stealth-browser-mcp for clients that support Codex-style skills. It teaches agents the recommended tool order, state checks, pre-document CDP script usage, network debugging flow, and browser cleanup rules.\n\nUse the skill when an LLM struggles to choose the right MCP tool or forgets to verify browser state after actions. If your client does not load repo-local skills automatically, add or symlink skills/stealth-browser-mcp into your client skills directory.\n\n---\n\nTrust Model\n\nThis server exposes powerful local browser-control primitives: CDP execution, page JavaScript execution, cookie/storage access, network interception, dynamic hooks, header mutation, navigation, and file uploads from allowlisted local paths.\n\nTreat the MCP client or agent as the security principal. The recommended deployment is local stdio transport with a trusted desktop MCP client. If you use HTTP transport, set STEALTH_BROWSER_MCP_AUTH_TOKEN and do not expose unauthenticated HTTP outside trusted local or private networks.\n\n---\n\nModular Architecture\n\nChoose exactly what functionality you need. Run the full 101-tool suite or strip it down to the 21-tool core.\n\n| Mode | Tools | Use Case |\n|------|-------|----------|\n| **Full** (default) | 101 | Complete browser automation and debugging |\n| **Minimal** (--minimal) | 21 | Core browser automation and interaction |\n| **Custom** (--disable-*) | Your choice | Disable specific sections |\n| **Xpool safe** (--xpool-safe) | 84 | Disable CDP function tools that trigger Runtime.enable |\n\n \n\nUse --debug or set STEALTH_BROWSER_DEBUG=1 to enable verbose server diagnostics on stderr. Normal MCP stdio runs stay quiet by default to avoid corrupting JSON-RPC transport output.\n\n**Available sections:**\n\n| Section | Tools | Description |\n|---------|-------|-------------|\n| browser-management | 8 | Core browser operations |\n| element-interaction | 13 | Page interaction and manipulation |\n| element-extraction | 9 | Element cloning and extraction |\n| file-extraction | 9 | File-based extraction tools |\n| network-debugging | 10 | Network monitoring and capture |\n| cdp-functions | 17 | Chrome DevTools Protocol execution |\n| progressive-cloning | 10 | Advanced element cloning |\n| cookies-storage | 3 | Cookie management |\n| tabs | 5 | Tab management |\n| debugging | 7 | Debug and system tools |\n| dynamic-hooks | 10 | AI-assisted network hooks |\n\n---\n\nServer Configuration\n\nThese are environment variables for the MCP server process. Set them in your shell, service manager, Docker environment, or MCP client env block.\n\n| Variable | Default | Meaning |\n|----------|---------|---------|\n| STEALTH_BROWSER_MCP_AUTH_TOKEN | unset | Optional bearer token for HTTP transport. When set, HTTP clients must send Authorization: Bearer . |\n| MCP_AUTH_TOKEN | unset | Backward-compatible alias for STEALTH_BROWSER_MCP_AUTH_TOKEN. |\n| BROWSER_IDLE_TIMEOUT | 600 | Global idle timeout in seconds before an unused browser instance is auto-closed. Set 0 to disable idle reaping globally. |\n| BROWSER_IDLE_REAPER_INTERVAL | 60 | Background reaper check interval in seconds. |\n| BROWSER_TAB_RECYCLE_NAVIGATIONS | 0 | Replace the main tab with a fresh one after this many navigations. 0 keeps the same tab, which preserves history and session storage. |\n| BROWSER_ORPHAN_PROFILE_MAX_AGE | 21600 | Startup cleanup threshold in seconds for stale uc_* temp profiles. Set 0 to disable this startup sweep. |\n| STEALTH_BROWSER_EXECUTABLE | auto-detect | Path to the Chrome, Chromium, or Edge executable to launch instead of the auto-detected one. |\n| BROWSER_FILE_UPLOAD_ALLOWED_DIRS | repo root | Directories that file_upload() may read from. Separate multiple roots with ; on Windows or : on macOS/Linux. |\n| STEALTH_BROWSER_DEBUG | 0 | Enable verbose debug logging to stderr when set to 1. |\n| DEBUG | 0 | Legacy alias for STEALTH_BROWSER_DEBUG. |\n| XPOOL_SAFE_MODE | 0 | Disable the cdp-functions section at startup. |\n| PORT | 8000 | Default HTTP port when --port is not provided. |\n\n**PowerShell example:**\n\n \n\n**MCP client env block:**\n\n \n\n**HTTP client example:**\n\n \n\n**Per-instance idle override:**\n\n- spawn_browser(idle_timeout_seconds=1800) keeps that instance for 30 minutes of inactivity.\n- spawn_browser(idle_timeout_seconds=0) disables idle reaping for that one instance.\n\n---\n\nToolbox\n\n \n Browser Management (8) \n\n| Tool | Description |\n|------|-------------|\n| spawn_browser() | Create a stealth browser instance |\n| list_instances() | List active browser sessions |\n| close_instance() | Clean shutdown of a browser |\n| get_instance_state() | Full browser state information |\n| navigate() | Navigate to URLs |\n| go_back() | Navigate back in history |\n| go_forward() | Navigate forward in history |\n| reload_page() | Reload current page |\n\n \n\n \n Element Interaction (13) \n\n| Tool | Description |\n|------|-------------|\n| query_elem", 'readme_raw_len': 18080, 'one_line_zh': '通过 MCP 与 Chrome DevTools 协议为 AI Agent 提供隐身浏览器自动化：导航、网络钩子、DOM 提取与像素级 UI 克隆', 'one_line_plain': 'Stealth browser automation over MCP and Chrome DevTools Protocol that lets agents navigate, hook networks, extract DOM, and clone UI pixels'}, {'id': 8056, 'key': 'skillsmp-com-creators-aldegad-sprite-gen-skill', 'kind': 'skill', 'name': 'sprite-gen', 'one_line': 'Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults,…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/aldegad/sprite-gen/skill', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/aldegad/sprite-gen/skill', 'downloads': None, 'stars': 2836, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/aldegad.png?size=64', 'official_desc': 'Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults, automatic cleanup and optional curation. Handles sprite requests, ordinary image generation/editing, typography logos (로고, 타이포그래피, 타이틀 글자, 타이틀 로고, wordmark, logo, title lettering: a title scr', 'stars_own': None, 'stars_creator': 2918, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'aldegad/sprite-gen', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '通过 GPT 或 Grok 生成图像与游戏精灵，带引导式供应商选择与分开保存的默认值…', 'one_line_plain': 'Generate images and game sprites using GPT or Grok, with guided provider choices and saved defaults'}, {'id': 235, 'key': 'metatool-ai-metatool-app', 'kind': 'mcp', 'name': 'metatool-ai/metatool-app', 'one_line': '📇 ☁️ 🏠 🍎 🪟 🐧 - MetaMCP: unified middleware MCP server that manages your MCP connections through a GUI', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/metatool-ai/metamcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/metatool-ai/metatool-app', 'downloads': None, 'stars': 2713, 'installs': None, 'mentions': 22, 'score': 2203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/metatool-ai.png?size=64', 'official_desc': 'WNFjHvupy3q8TFn3Hos2IAk4Ju5dCo8B3wP7VPr/FGaKiG+T+v+TQqIrOqMTL1VdWV1DdmcbO8KXBz6esmYWYKPwDL5b5FA1a0hwapHiom0r/cKaoqr+27/XcrS5UwSMbQAAAABJRU5ErkJggg==" alt="DeepWiki: MetaMCP"> Latest Update:** This ai-dev branch will be the forward onging dev branch which contains ai agent changes.', 'stars_own': 2713, 'stars_creator': None, 'stars_own_repo': 'metatool-ai/metamcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': '🚀 MetaMCP (MCP Aggregator, Orchestrator, Middleware, Gateway in one docker) \n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n\n> \n\n**📢 Latest Update:** This ai-dev branch will be the forward onging dev branch which contains ai agent changes. Please test before you build the image based on this branch. There has been many PRs thanks to the community but merging and reviewing them has been a growing effort too. I decided to include ai changes. At least so far the core functionality works. There is also a community maintained fork (ty a lot!): https://github.com/Umbrella-IT-Group/metamcp\n\n**📢 Update:** *From the author: apologize for some recent maintainence delay, but will at least keep merging PRs, more background [here]*\n\n**MetaMCP** is a MCP proxy that lets you dynamically aggregate MCP servers into a unified MCP server, and apply middlewares. MetaMCP itself is a MCP server so it can be easily plugged into **ANY** MCP clients.\n\n \n\n---\n\nFor more details, consider visiting our documentation site: https://docs.metamcp.com\n\nEnglish | 简体中文\n📋 Table of Contents \n\n- 🎯 Use Cases\n- 📖 Concepts\n - 🖥️ **MCP Server**\n - 🔐 **Environment Variables \\& Secrets (STDIO MCP Servers)**\n - 🏷️ **MetaMCP Namespace**\n - 🌐 **MetaMCP Endpoint**\n - ⚙️ **Middleware**\n - 🔍 **Inspector**\n - ✏️ **Tool Overrides \\& Annotations**\n- 🚀 Quick Start\n - 🐳 Run with Docker Compose (Recommended)\n - 📦 Build development environment with Dev Containers (VSCode/Cursor)\n - 💻 Local Development\n- 🔌 MCP Protocol Compatibility\n- 🔗 Connect to MetaMCP\n - 📝 E.g., Cursor via mcp.json\n - 🖥️ Connecting Claude Desktop and Other STDIO-only Clients\n - 🔧 API Key Auth Troubleshooting\n- ❄️ Cold Start Problem and Custom Dockerfile\n- 🧾 Log Levels\n- 🔐 Authentication\n- 🚦 Traffic Management\n - 🚧 **MCP Rate Limit**\n- 🔗 OpenID Connect (OIDC) Provider Support\n - 🛠️ **Configuration**\n - 🏢 **Supported Providers**\n - 🔒 **Security Features**\n - 📱 **Usage**\n- ⚙️ Registration Controls\n - 🎛️ **Available Controls**\n - 🏢 **Enterprise Use Cases**\n - 🛠️ **Configuration**\n- 🌐 Custom Deployment and SSE conf for Nginx\n- 🏗️ Architecture\n - 📊 Sequence Diagram\n- 🗺️ Roadmap\n- 🌐 i18n\n- 🤝 Contributing\n- 📄 License\n- 🙏 Credits\n\n🎯 Use Cases\n- 🏷️ **Group MCP servers into namespaces, host them as meta-MCPs, and assign public endpoints** (SSE or Streamable HTTP), with auth. One-click to switch a namespace for an endpoint.\n- 🎯 **Pick tools you only need when remixing MCP servers.** Apply other **pluggable middleware** around observability, security, etc. (coming soon)\n- 🔍 **Use as enhanced MCP inspector** with saved server configs, and inspect your MetaMCP endpoints in house to see if it works or not.\n- 🔍 **Use as Elasticsearch for MCP tool selection** (coming soon)\n\nGenerally developers can use MetaMCP as **infrastructure** to host dynamically composed MCP servers through a unified endpoint, and build agents on top of it.\n\nQuick demo video: https://youtu.be/Cf6jVd2saAs\n\n \n\n📖 Concepts\n\n🖥️ **MCP Server**\nA MCP server configuration that tells MetaMCP how to start a MCP server.\n\n \n\n🔐 **Environment Variables & Secrets (STDIO MCP Servers)**\n\nFor **STDIO MCP servers**, MetaMCP supports three ways to handle environment variables and secrets:\n\n**1. Raw Values** - Direct string values (not recommended for secrets):\n \n\n**2. Environment Variable References** - Use ${ENV_VAR_NAME} syntax:\n \n\n**3. Auto-matching** - If the expected environment variable name in your tool matches the container\'s environment variable, you can omit it entirely. MetaMCP will automatically pass through matching environment variables.\n\n> **🔒 Security Note**: Environment variable references (${VAR_NAME}) are resolved from the MetaMCP container\'s environment at runtime. This keeps actual secret values out of your configuration and git repository.\n\n> **⚙️ Development Note**: For local development with pnpm run dev:docker, ensure your environment variables are listed in turbo.json under globalEnv to be passed to the development processes. This is not required for production Docker deployments.\n\n🏷️ **MetaMCP Namespace**\n- Group one or more MCP servers into a namespace\n- Enable/disable MCP servers or at tool level\n- Apply middlewares to MCP requests and responses\n- Override tool names/titles/descriptions per namespace and attach custom MCP annotations (e.g. { "annotations": { "readOnlyHint": false } })\n\n🌐 **MetaMCP Endpoint**\n- Create endpoints and assign namespace to endpoints\n- Multiple MCP servers in the namespace will be aggregated and emitted as a MetaMCP endpoint\n- Choose between API-Key Auth (in header or query param) or standard OAuth in MCP Spec 2025-06-18\n- Host through **SSE** or **Streamable HTTP** transports in MCP and **OpenAPI** endpoints for clients like Open WebUI\n\n⚙️ **Middleware**\n- Intercepts and transforms MCP requests and responses at namespace level\n- **Built-in example**: "Filter inactive tools" - optimizes tool context for LLMs\n- **Future ideas**: tool logging, error traces, validation, scanning\n\n🔍 **Inspector**\nSimilar to the official MCP inspector, but with **saved server configs** - MetaMCP automatically creates configurations so you can debug MetaMCP endpoints immediately.\n\n✏️ **Tool Overrides & Annotations**\n- Open a namespace → **Tools** tab to see every tool coming from connected MCP servers.\n- Each saved tool can be expanded and edited inline: update the display **name/title/description** or provide a JSON blob with namespace-specific annotations (for example { "annotations": { "readOnlyHint": false } }).\n- Badges in the table ("Overridden", "Annotations") show which tools currently have custom metadata. Hover them to read a tooltip describing what was overridden.\n- Annotation overrides are merged with whatever the upstream MCP server returns, so you can safely add custom UI hints without losing provider metadata.\n\n🚀 Quick Start\n\n**🐳 Run with Docker Compose (Recommended)**\n\nClone repo, prepare .env, and start with docker compose:\n\n \n\nIf you modify APP_URL env vars, make sure you only access from the APP_URL, because MetaMCP enforces CORS policy on the URL, so no other URL is accessible.\n\nNote that the pg volume name may collide with your other pg dockers, which is global, consider rename it in docker-compose.yml:\n\n \n\n**📦 Build development environment with Dev Containers (VSCode/Cursor)**\n\nYou can use the VSCode/Cursor extension to build the development environment in a container.\n\nIt only requires that you have an environment running Docker or a similar alternative (the docker/docker compose command is required), and no other dependent components need to be installed on your host machine.\n\n1. First, clone the MetaMCP source code, open project in Visual Studio Code.\n \n2. Switch to Dev Containers. Open the VSCode Command Palette, and execute Dev Containers: Reopen in Container.\n\nVSCode will open the Dev Containers project in a new window, where it will build the runtime and install the toolchain according to the Dockerfile before starting the connection and finally installing the MetaMCP dependencies.\n \n\n> **note**\n> This process requires a reliable network connection, and it will access Docker Hub, GitHub, and some other sites. You will need to ensure the network connection yourself, otherwise the container build may fail.\n\nWait some minutes, depending on the internet connection or computer performance, it may take from a few minutes to tens of minutes, you can click on the Progress Bar in the bottom right corner to view a live log where you will be able to check unusual stuck.\n \n\nAfter finished, you can run pnpm dev to start the development server.\n\n**💻 Local Development**\n\nStill recommend running postgres through docker for easy setup:\n\n \n\n🔌 MCP Protocol Compatibility\n\n- ✅ **Tools, Resources, and Prompts** supported\n- ✅ **OAuth-enabled MCP servers** tested for 03-26 version\n\nIf you have questions, feel free to leave **GitHub issues** or **PRs**.\n\n🔗 Connect to MetaMCP\n\n📝 E.g., Cursor via mcp.json\n\nExample mcp.json\n\n \n\n🖥️ Connecting Claude Desktop and Other STDIO-only', 'readme_raw_len': 17689, 'one_line_zh': 'MetaMCP：统一的中间件 MCP 服务器，通过图形界面管理 MCP 连接', 'one_line_plain': 'Manage MCP connections through a unified middleware GUI'}, {'id': 8338, 'key': 'feder-cr-git-ai', 'kind': 'repo', 'name': 'feder-cr/git-ai', 'one_line': 'Git designed for AI agents', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '["开源项目"]', 'desc_raw': '', 'url': 'https://github.com/feder-cr/git-ai', 'source': 'github', 'source_url': 'https://github.com/feder-cr/git-ai', 'downloads': None, 'stars': 2438, 'installs': None, 'mentions': 1, 'score': 107.87, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-19', 'last_seen': '2026-10-19', 'times_seen': 1, 'icon': 'https://github.com/feder-cr.png?size=64', 'official_desc': 'Git designed for AI agents.', 'stars_own': 2438, 'stars_creator': None, 'stars_own_repo': 'feder-cr/git-ai', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': None, 'one_line_plain': 'Git designed for AI agents to manage code changes and version history'}, {'id': 3676, 'key': 'mariocandela-beelzebub', 'kind': 'mcp', 'name': 'mariocandela/beelzebub', 'one_line': '☁️ - Honeypot framework for building decoy MCP tools that an agent would never use in normal work, to detect…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/beelzebub-labs/beelzebub', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/mariocandela/beelzebub', 'downloads': None, 'stars': 2201, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/mariocandela.png?size=64', 'official_desc': '☁️ - Honeypot framework for building decoy MCP tools that an agent would never use in normal work, to detect prompt injection and malicious agent behavior.', 'stars_own': 2201, 'stars_creator': None, 'stars_own_repo': 'beelzebub-labs/beelzebub', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "Beelzebub\n\n**Open-source deception framework.**\n\nDeploy realistic decoys. Observe attacker behavior. Turn interactions into security evidence.\n\n**Explore Beelzebub Platform** · Documentation · Quick start\n\n \n \n \n \n \n\nBeelzebub gives security teams a configurable way to study activity directed at decoy services. Define the environment an attacker encounters, choose how it responds, and capture the interaction for investigation. Run the framework independently or connect it to Beelzebub Platform.\n\nWhy Beelzebub\n\n- **Deploy decoys that fit your environment.** Define services, routes, and response rules in YAML. Use static handlers for predictable behavior or LLM-powered responses for adaptive interactions. Extend the runtime with trusted Go plugins when you need custom behavior.\n- **Observe what happens after contact.** Capture evidence from decoy interactions, including commands, HTTP request details, and session context, depending on the protocol. Give analysts a record of activity directed at the service to support investigation.\n- **Bring evidence into your workflow.** Inspect logs locally, publish structured events to RabbitMQ, or enable Beelzebub Platform reporting. Monitor runtime activity through Prometheus metrics and choose where captured data is sent.\n\nSee it in action\n\nWatch an LLM-powered decoy respond to attacker input. The demo illustrates how generated responses can sustain an interaction beyond a fixed set of command handlers.\n\n \n\nHow it works\n\n \n\nDefine services and rules in YAML, validate the configuration, and start the runtime. Trusted Go plugins are compiled into the runtime to extend services and responses. Each service handles incoming interactions and emits evidence through the configured event output. LLM responses require a configured provider; static handlers can run without one.\n\nThe framework supports **SSH, HTTP, TCP, TELNET, and MCP**. MCP decoys expose bait tools that make suspicious invocations observable during controlled agent testing. They can provide evidence of prompt-injection attempts; they do not guarantee detection of every attempt.\n\nDeployment options include a local Go binary, Docker Compose, and Kubernetes with Helm. See the architecture guide for runtime behavior and extension boundaries.\n\nExplore the decoys\n\nStart with one of the **18 example configurations** in configurations/services. Open a YAML file to inspect its rules and adapt it to your environment.\n\n| Category | Example decoys |\n| --- | --- |\n| Remote access | SSH · SSH (LLM) · TELNET · RDP · VNC |\n| Web | WordPress (LLM fallback) · HTTP 401 · HTTP methods · Apache directory maze |\n| Databases & caches | MySQL · PostgreSQL (LLM) · SQL Server · Redis · Memcached |\n| Network services | SMB · LDAP |\n| IoT | MQTT |\n| AI agents | MCP bait tools |\n\nExamples range from banners and selected responses to interactive exchanges. **LLM** examples require a configured provider; WordPress uses it for its catch-all route. Check your deployment's port mappings when enabling additional examples.\n\nGet started\n\nYou need **Git, a shell, and Docker Engine with Compose v2**. Use an **isolated lab host** with authorization for all configured listeners: the installer starts a bundle of example services, including privileged ports. On Linux it can enable **host networking**; Docker does not provide network isolation in that mode.\n\nUse synthetic credentials and review the production safety guide before exposing services to the internet.\n\n \n\nPlatform reporting is optional. Leave the Platform token blank when prompted to run independently. The runtime validates its configuration before starting listeners.\n\nTest the default HTTP decoy and inspect its logs:\n\n \n\nExpect **401 Unauthorized** from the bundled HTTP example on port 8080. This is the decoy's configured response, confirming that the listener is reachable. If the installer skipped an occupied port, resolve the conflict before testing. Press Ctrl+C to stop following logs.\n\nStop the lab when finished:\n\n \n\nFor a single-service setup or another deployment method, follow the installation guide.\n\nBeelzebub Platform\n\nThe open-source framework is the foundation: deploy, operate, and extend it independently. **Beelzebub Platform** is the managed product for teams that need to coordinate deception across environments and connect runtime evidence to a broader security workflow.\n\nExplore the platform's deployment, investigation, and reporting capabilities, and see how they fit your team's requirements.\n\n**Explore Beelzebub Platform →**\n\nResources and community\n\n- **Documentation:** configuration, protocols, operations, integrations, and recipes.\n- **Plugin authoring:** build extensions with the public Go SDK. Plugins execute in-process; review their source and pin trusted versions.\n- **Contributing:** contribute code, examples, or documentation under the Code of Conduct. Report vulnerabilities privately through SECURITY.md.\n\nFor development, use the Go version declared in go.mod, Git, and Make. Build with make build; run make test.unit, go vet ./..., and make validate-all before submitting runtime changes. The development workflow covers integration tests and their Docker dependencies.\n\nThank you to JetBrains for supporting development with tools through its open-source support program.\n\nLicensed under the GNU General Public License v3.0.", 'readme_raw_len': 5358, 'one_line_zh': '用于构建诱饵 MCP 工具的蜜罐框架，正常工作中 Agent 不会用到这些工具，一旦调用即可发现。', 'one_line_plain': 'Build decoy MCP tools to detect unauthorized agent activity'}, {'id': 1465, 'key': 'joshuayoes-ios-simulator-mcp', 'kind': 'mcp', 'name': 'ios-simulator-mcp', 'one_line': '📇 🏠 🍎 - Interact with iOS simulators: get simulator information, control UI interactions and inspect UI…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for interacting with the iOS simulator', 'url': 'https://github.com/joshuayoes/ios-simulator-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/joshuayoes/ios-simulator-mcp', 'downloads': None, 'stars': 2194, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/joshuayoes.png?size=64', 'official_desc': '📇 🏠 🍎 - Interact with iOS simulators: get simulator information, control UI interactions and inspect UI elements.', 'stars_own': 2194, 'stars_creator': None, 'stars_own_repo': 'joshuayoes/ios-simulator-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'iOS Simulator MCP Server\n\n \n\nA Model Context Protocol (MCP) server for interacting with iOS simulators. This server allows you to interact with iOS simulators by getting information about them, controlling UI interactions, and inspecting UI elements.\n\n> **Security Notice**: Command injection vulnerabilities present in versions ;\n}\n json\n{\n "bundle_id": "com.example.app",\n "terminate_running": true,\n "env": {\n "FOO": "bar",\n "BAZ": "qux"\n }\n}\n typescript\n{\n /**\n * Udid of target, can also be set with the IDB_UDID env var\n * Format: UUID (8-4-4-4-12 hexadecimal characters)\n */\n udid?: string;\n /** Bundle identifier of the app to terminate (e.g., com.apple.mobilesafari) */\n bundle_id: string;\n}\n typescript\n{\n /**\n * Udid of target, can also be set with the IDB_UDID env var\n * Format: UUID (8-4-4-4-12 hexadecimal characters)\n */\n udid?: string;\n /** The URL or deep link to open (e.g., https://example.com or myapp://screen/detail) */\n url: string;\n}\n typescript\n{\n /**\n * Udid of target, can also be set with the IDB_UDID env var\n * Format: UUID (8-4-4-4-12 hexadecimal characters)\n */\n udid?: string;\n}\n \n Verify all accessibility elements on the current screen\n \n Enter "QA Test" into the text input field and confirm the input is correct\n \n Tap on coordinates x=250, y=400 and verify the expected element is triggered\n \n Swipe from x=150, y=600 to x=150, y=100 and confirm correct behavior\n \n Describe the UI element at position x=300, y=350 to ensure proper labeling and functionality\n \n View the current simulator screen\n \n Take a screenshot of the current simulator screen and save it to my_screenshot.png\n \n Start recording a video of the simulator screen (saves to the default output directory, which is ~/Downloads unless overridden by IOS_SIMULATOR_MCP_DEFAULT_OUTPUT_DIR)\n \n Stop the current simulator screen recording\n \n Install the app at path/to/MyApp.app on the simulator\n \n Launch the Safari app (com.apple.mobilesafari) on the simulator\n bash\nnpx uri-scheme open "myapp://products/42" --ios\n bash\nxcrun simctl openurl booted "myapp://products/42"\n \nOpen the deep link myapp://products/42 in the simulator, then verify the product\ndetails screen is shown\n bash\n # Open with your default editor (or use \'code\', \'vim\', etc.)\n open ~/.cursor/mcp.json\n # Or use Cursor\'s command if available\n # cursor ~/.cursor/mcp.json\n json\n {\n "mcpServers": {\n // ... other servers might be listed here ...\n "ios-simulator": {\n "command": "npx",\n "args": ["-y", "ios-simulator-mcp"]\n }\n }\n }\n json\n {\n "mcpServers": {\n "ios-simulator": {\n "command": "pnpm",\n "args": ["dlx", "ios-simulator-mcp"]\n }\n }\n }\n bash\n git clone https://github.com/joshuayoes/ios-simulator-mcp\n cd ios-simulator-mcp\n bash\n npm install\n # or, using pnpm (installs from the committed pnpm-lock.yaml):\n pnpm install\n bash\n npm run build\n # or:\n pnpm run build\n json\n {\n "mcpServers": {\n // ... other servers might be listed here ...\n "ios-simulator": {\n "command": "node",\n "args": ["/full/path/to/your/ios-simulator-mcp/build/index.js"]\n }\n }\n }\n bash\n claude mcp add ios-simulator npx ios-simulator-mcp\n # or, with pnpm:\n claude mcp add ios-simulator -- pnpm dlx ios-simulator-mcp\n bash\n claude mcp add ios-simulator -- node "/full/path/to/your/ios-simulator-mcp/build/index.js"\n json\n{\n "mcpServers": {\n "ios-simulator": {\n "command": "npx",\n "args": ["-y", "ios-simulator-mcp"],\n "env": {\n "IOS_SIMULATOR_MCP_FILTERED_TOOLS": "screenshot,record_video,stop_recording",\n "IOS_SIMULATOR_MCP_DEFAULT_OUTPUT_DIR": "~/Code/awesome-project/tmp",\n "IOS_SIMULATOR_MCP_IDB_PATH": "~/bin/idb"\n }\n }\n }\n}\n`\n\nMCP Registry Server Listings\n\n \n \n \n\n \n\nLicense\n\nMIT', 'readme_raw_len': 3633, 'one_line_zh': '📇 🏠 🍎 - 与 iOS 模拟器交互：获取模拟器信息、控制 UI 操作并检查 UI 界面', 'one_line_plain': 'Interact with iOS simulators to get information, control UI and inspect UI elements'}, {'id': 348, 'key': 'jau123-meigen-ai-design-mcp', 'kind': 'mcp', 'name': 'jau123/MeiGen-AI-Design-MCP', 'one_line': '', 'one_line_ok': 0, 'why_bad': 'ok', 'fields': '["MCP 服务"]', 'desc_raw': 'Discover inspiration and create images and videos with the latest AI models. Works with ChatGPT, Claude, Cursor, and other MCP clients.', 'url': 'https://github.com/jau123/MeiGen-AI-Design-MCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/jau123/MeiGen-AI-Design-MCP', 'downloads': None, 'stars': 1787, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/jau123.png?size=64', 'official_desc': '[](https://glama.ai/mcp/servers/@jau123/mei-gen-ai-design-mcp) 📇 ☁️ 🏠 - Image generation', 'stars_own': 1787, 'stars_creator': None, 'stars_own_repo': 'jau123/MeiGen-AI-Design-MCP', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "MeiGen AI Design MCP \n\n \n Discover inspiration. Create images and videos with the latest AI models. \n Open-source MCP tools for ChatGPT, Claude, Cursor, OpenClaw and other AI assistants.\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n \n English · 中文 \n Quick start · Capabilities · Manual setup · Documentation \n \n\nQuick start\n\n \n \n\nChoose the AI tool you use:\n\n| Client | Get started |\n|---|---|\n| **ChatGPT** | Install from the official ChatGPT store → sign in to MeiGen → start a new chat and use MeiGen. No API key needed. |\n| **Claude** | Connect remote MCP and sign in to MeiGen. Optional creative helpers are available separately. |\n| **Cursor** | Add the remote MCP URL, then sign in. |\n| **OpenClaw** | Install from ClawHub, then sign in with OAuth. |\n| **Other MCP clients** | Connect remote MCP with OAuth. Setup guide → |\n\nTry a search first:\n\n> Use MeiGen to find minimalist product photography inspiration. Show me the reference images and prompts.\n\nWant more options? With remote MCP, ask for another batch of the same search, or describe a new direction.\n\nSearch and model/Skill lookups do not spend generation credits. MeiGen Cloud generation uses **purchased credits** from your account; daily free credits do not apply. Check current prices and confirm the scope before generating.\n\n \n \n\nWhat you can do\n\n| Capability | Try asking |\n|---|---|\n| Inspiration search | “Find cinematic portrait references and show their prompts.” |\n| Image & video generation | “Create one product photo, then animate it into a short video.” |\n| Reference media | “Use this clip or audio as a reference with a compatible video model.” |\n| Saved characters — remote OAuth | “List my MeiGen characters and use this one in the video.” |\n| Personal library — remote OAuth | “Find my saved references, or the prompt from a previous creation.” |\n| Background removal | “Remove this background and give me a transparent PNG.” |\n| Product Detail Images | “Make a hero shot, a close-up and a lifestyle image for this product.” |\n| Marketing Poster | “Design one poster for our weekend coffee tasting.” |\n| AI Backgrounds | “Place this product on a sunlit stone counter.” |\n| Upscale | “Enhance this original photo while preserving its appearance.” |\n\nProvide a source image when the task needs one. Your assistant checks available models, options and prices with list_models / list_skills, prepares inputs, and returns results. See the Skill API guide for input limits and tool-call examples.\n\nVideo references follow each model’s supported frame, content-image, video and audio inputs. Media preparation does not spend generation credits, requires a positive purchased balance, and creates temporary public URLs cleared after seven days. Saved characters can be read and reused through OAuth; creating characters is not supported. See 3.1 release notes.\n\nBring your MeiGen library into the conversation\n\nBrowse and save ideas in MeiGen, then continue with your assistant. With remote OAuth, it can search your favorites, browse boards, find past creations and read a selected original prompt. For questions about your preferred style, it can use relevant favorites as references.\n\nAsk to save a selected inspiration or your own result, optionally to a private board. Saving requires your request or prior permission, does not publish the work, and does not spend generation credits. Reading and saving use separate account permissions; existing connections may need additional consent.\n\n**Available through remote OAuth and the official ChatGPT plugin.** Local npm and API keys cannot access the private library. Refresh your client’s tool list and complete any requested additional consent. See the account-library reference for tool names and prompt pagination.\n\n \n\nManual MCP setup\n\nUse remote OAuth for a new MeiGen connection. Keep one connection per client to avoid duplicate tools. Local npm and the optional client plugins below support existing API-key integrations and OpenAI-compatible image providers.\n\n \n\nRemote MCP\n\nAdd this **Streamable HTTP** URL in your client's MCP settings, then choose **Connect / Authenticate** and sign in to MeiGen:\n\n \n\nOAuth requires support in your client. If unavailable, use a compatible host or the official ChatGPT plugin; a new API key is not a substitute for account sign-in. Reconnect and ask “List MeiGen's available Skills and current prices” to verify the connection without generating.\n\nFor optional Codex CLI / IDE MCP configuration, see the advanced setup guide.\n\nLocal npm — legacy compatibility and BYOK\n\nExisting local integrations and BYOK workflows can keep this stdio configuration. New MeiGen users should use remote OAuth above. Node.js 22+ is recommended:\n\n \n\nExisting local API keys remain compatible with generation, public search and Skills as MEIGEN_API_TOKEN in the server's private environment, but cannot access the private library. New connections use remote OAuth. OpenAI-compatible providers support general image generation; video and the five visual Skills use MeiGen Cloud. See client-specific setup, providers and CLI.\n\n**Upgrading from 2.x?** 3.0.0 is available on npm. It removes ComfyUI and the bundled offline prompt library. Read the migration notes and preserve your existing credentials and settings when updating.\n\n \n\n \n Claude plugin \n\nFor new connections, use remote OAuth. The optional plugin below retains its bundled legacy stdio connection; keep one active MeiGen connection per host when switching. To install only within that supported setup, run inside Claude, then restart:\n\n \n\nUse /meigen:setup for connection guidance or existing legacy/BYOK configuration. The plugin adds /meigen:gen, /meigen:find, /meigen:models and optional creative agents. Plugin guide →\n\n \n\n \n\n \n OpenClaw \n\nInstall MeiGen from ClawHub in current OpenClaw, then connect your account with OAuth:\n\n \n\nThe 4.1 plugin includes creative guides and remote tools, including OAuth-only favorites, boards and history. The standalone creative-toolkit Skill is instructions only and is not needed alongside the plugin. Setup and migration from local 3.x →\n\n \n\n \n\n \n Let your coding assistant set up MCP \n\nPaste this into an assistant that can configure your MCP client:\n\n \n\n \n\n \n\nDocumentation\n\n- Client setup, configuration, privacy and troubleshooting · Upgrading\n- Five Skills HTTP API and MCP examples\n- Compose image/video steps and recover interrupted jobs\n- Claude plugin commands and agents\n- 3.0 migration notes · Maintainer release guide\n\nIf a generation is interrupted, check the original task with check_generation or check_skill before starting another paid request. Keep your original assets and download results you need to retain.\n\nCommunity & license\n\nDiscord · Issues · MeiGen · Privacy policy\n\nFeatured in Awesome MCP Servers and wshobson/agents.\n\nMIT. The open-source server is free to use; cloud generation and third-party providers have their own usage costs.", 'readme_raw_len': 6913, 'one_line_zh': '发现灵感，并用最新 AI 模型创作图像与视频。支持 ChatGPT、Claude、Cursor 及其他 MCP 客户端。', 'one_line_plain': None}]/2026-10-20 https://anclum.com/zh/[{'id': 1932, 'key': 'microsoft-markitdown-tree-main-packages-markitdown-mcp', 'kind': 'mcp', 'name': 'microsoft/markitdown', 'one_line': '🎖️ 🐍 🏠 - Convert many file formats, local or remote, to Markdown for LLMs using MarkItDown', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Python tool for converting files and office documents to Markdown.', 'url': 'https://github.com/microsoft/markitdown/tree/main/packages/markitdown-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/microsoft/markitdown/tree/main/packages/markitdown-mcp', 'downloads': None, 'stars': 191102, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/microsoft.png?size=64', 'official_desc': '🎖️ 🐍 🏠 - Convert many file formats, local or remote, to Markdown for LLMs using MarkItDown.', 'stars_own': 191102, 'stars_creator': None, 'stars_own_repo': 'microsoft/markitdown', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'MarkItDown\n\n \n \n\n> [!IMPORTANT]\n> MarkItDown performs I/O with the privileges of the current process. Like open() or requests.get(), it will access resources that the process itself can access. Sanitize your inputs in untrusted environments, and call the narrowest convert_* function needed for your use case (e.g., convert_stream(), or convert_local()). See the Security Considerations section of the documentation for more information.\n\nMarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to textract, but with a focus on preserving important document structure and content as Markdown (including: headings, lists, tables, links, etc.) While the output is often reasonably presentable and human-friendly, it is meant to be consumed by text analysis tools -- and may not be the best option for high-fidelity document conversions for human consumption.\n\nMarkItDown currently supports the conversion from:\n\n- PDF\n- PowerPoint\n- Word\n- Excel\n- Images (EXIF metadata and OCR)\n- Audio (EXIF metadata and speech transcription)\n- HTML\n- Text-based formats (CSV, JSON, XML)\n- ZIP files (iterates over contents)\n- YouTube URLs\n- EPubs\n- ... and more!\n\nWhy Markdown?\n\nMarkdown is extremely close to plain text, with minimal markup or formatting, but still\nprovides a way to represent important document structure. Mainstream LLMs, such as\nOpenAI\'s GPT-4o, natively "_speak_" Markdown, and often incorporate Markdown into their\nresponses unprompted. This suggests that they have been trained on vast amounts of\nMarkdown-formatted text, and understand it well. As a side benefit, Markdown conventions\nare also highly token-efficient.\n\nPrerequisites\nMarkItDown requires Python 3.10 through 3.14. It is recommended to use a virtual environment to avoid dependency conflicts.\n\nWith the standard Python installation, you can create and activate a virtual environment using the following commands:\n\n \n\nIf using uv, you can create a virtual environment with:\n\n \n\nIf you are using Anaconda, you can create a virtual environment with:\n\n \n\nInstallation\n\nTo install MarkItDown, use pip: pip install \'markitdown[all]\'. Alternatively, you can install it from the source:\n\n \n\nUsage\n\nCommand-Line\n\n \n\nOr use -o to specify the output file:\n\n \n\nYou can also pipe content:\n\n \n\nOptional Dependencies\nMarkItDown has optional dependencies for activating various file formats. Earlier in this document, we installed all optional dependencies with the [all] option. However, you can also install them individually for more control. For example:\n\n \n\nwill install only the dependencies for PDF, DOCX, and PPTX files.\n\nAt the moment, the following optional dependencies are available:\n\n* [all] Installs all optional dependencies\n* [pptx] Installs dependencies for PowerPoint files\n* [docx] Installs dependencies for Word files\n* [xlsx] Installs dependencies for Excel files\n* [xls] Installs dependencies for older Excel files\n* [pdf] Installs dependencies for PDF files\n* [outlook] Installs dependencies for Outlook messages\n* [az-doc-intel] Installs dependencies for Azure Document Intelligence\n* [az-content-understanding] Installs dependencies for Azure Content Understanding\n* [audio-transcription] Installs dependencies for audio transcription of wav and mp3 files\n* [youtube-transcription] Installs dependencies for fetching YouTube video transcription\n\nPlugins\n\nMarkItDown also supports 3rd-party plugins. Plugins are disabled by default. To list installed plugins:\n\n \n\nTo enable plugins use:\n\n \n\nTo find available plugins, search GitHub for the hashtag #markitdown-plugin. To develop a plugin, see packages/markitdown-sample-plugin.\n\nmarkitdown-ocr Plugin\n\nThe markitdown-ocr plugin adds OCR support to PDF, DOCX, PPTX, and XLSX converters, extracting text from embedded images using LLM Vision — the same llm_client / llm_model pattern that MarkItDown already uses for image descriptions. No new ML libraries or binary dependencies required.\n\n**Installation:**\n\n \n\n**Usage:**\n\nPass the same llm_client and llm_model you would use for image descriptions:\n\n \n\nIf no llm_client is provided the plugin still loads, but OCR is silently skipped and the standard built-in converter is used instead.\n\nSee packages/markitdown-ocr/README.md for detailed documentation.\n\nAzure Content Understanding\n\nAzure Content Understanding provides higher-quality conversion with structured field extraction (YAML front matter), multi-modal support (documents, images, audio, video), and configurable analyzers.\n\nInstall: pip install \'markitdown[az-content-understanding]\'\n\nWhen to use Content Understanding\n\nContent Understanding is ideal when you need capabilities beyond what built-in or Document Intelligence converters provide:\n\n- **Audio and video files** — CU is the only option for video, and the higher-quality cloud option for audio. Built-in converters have no video support and only basic audio transcription.\n- **Structured field extraction** — Prebuilt or custom-built analyzers extract domain-specific fields (invoice amounts, receipt dates, contract clauses) serialized as YAML front matter. Neither built-in nor Doc Intel integration exposes fields.\n- **Higher-quality document extraction** — Cloud-based layout analysis and OCR for scanned PDFs, complex tables, and multi-page documents.\n- **Single API for all modalities** — One cu_endpoint handles documents, images, audio, and video with automatic analyzer routing.\n\n| Capability | Built-in converters | Azure Document Intelligence | Azure Content Understanding |\n|------------|---------------------|-----------------------------|-----------------------------|\n| Document conversion | Offline, format-specific extraction | Cloud layout extraction | Cloud multimodal extraction |\n| Structured fields | Not available | Not exposed by this integration | YAML front matter from analyzer fields |\n| Custom analyzers | Not available | Not configurable in this integration | Supported with cu_analyzer_id |\n| Audio and video | Basic audio, no video | Not supported | Audio and video analyzers |\n| Cost | Local compute only | Billable Azure API calls | Billable Azure API calls |\n\n**CLI:**\n\n \n\nThe endpoint can also be set once in the environment, so callers only need --use-cu:\n\n \n\n**Python API:**\n\n \n\n**With a custom analyzer** (for domain-specific field extraction):\n\n \n\nWhen cu_analyzer_id is set, the converter automatically scopes it to compatible file types based on the analyzer\'s modality. Incompatible types (e.g., audio files with a document analyzer) auto-route to default prebuilt analyzers.\n\n**Cost note:** Each convert() call for a CU-routed format is a billable Azure API call. Use cu_file_types to restrict which formats route to CU:\n\n \n\nMore information about Azure Content Understanding can be found here.\n\nAzure Document Intelligence\n\nTo use Microsoft Document Intelligence for conversion:\n\n \n\nThe endpoint can also be set once in the environment, so callers only need -d:\n\n \n\nMore information about how to set up an Azure Document Intelligence Resource can be found here\n\nPython API\n\nBasic usage in Python:\n\n \n\nDocument Intelligence conversion in Python:\n\n \n\nTo use Large Language Models for image descriptions (currently only for pptx and image files), provide llm_client and llm_model:\n\n \n\nmax_retries controls the OpenAI client\'s automatic retries for retryable errors (the default is 2); 5 allows up to six attempts with backoff. See the OpenAI SDK retry documentation.\n\nIf any attempt succeeds, image conversion continues normally. If the client raises an error after exhausting its retries, or encounters a non-retryable error, MarkItDown tries other applicable converters and raises FileConversionException only if none succeeds.\n\nDocker\n\n \n\nContributing\n\nBefore starting significant work, please read What to Contribute, which describes what is in and out of scope for this repository.\n\nThis project welcomes contributions and suggestio', 'readme_raw_len': 12893, 'one_line_zh': '🎖️ 🐍 🏠 - 使用 MarkItDown 把本地或远程的多种文件格式转换为 Markdown，供 LLM 使用。', 'one_line_plain': 'MarkItDown converts many file formats, local or remote, to Markdown for LLMs'}, {'id': 7696, 'key': 'thedotmack-claude-mem', 'kind': 'repo', 'name': 'thedotmack/claude-mem', 'one_line': 'Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions,…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '["开源项目"]', 'desc_raw': '', 'url': 'https://github.com/thedotmack/claude-mem', 'source': 'github', 'source_url': 'https://github.com/thedotmack/claude-mem', 'downloads': None, 'stars': 101789, 'installs': None, 'mentions': 1, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-15', 'last_seen': '2026-10-19', 'times_seen': 5, 'icon': 'https://github.com/thedotmack.png?size=64', 'official_desc': 'Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More', 'stars_own': 101789, 'stars_creator': None, 'stars_own_repo': 'thedotmack/claude-mem', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx claude-mem', 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "   \n \n    \n \n \n\n \n 🇨🇳 中文 •\n 🇹🇼 繁體中文 •\n 🇯🇵 日本語 •\n 🇵🇹 Português •\n 🇧🇷 Português •\n 🇰🇷 한국어 •\n 🇪🇸 Español •\n 🇩🇪 Deutsch •\n 🇫🇷 Français •\n 🇮🇱 עברית •\n 🇸🇦 العربية •\n 🇷🇺 Русский •\n 🇵🇱 Polski •\n 🇨🇿 Čeština •\n 🇳🇱 Nederlands •\n 🇹🇷 Türkçe •\n 🇺🇦 Українська •\n 🇻🇳 Tiếng Việt •\n 🇵🇭 Tagalog •\n 🇮🇩 Indonesia •\n 🇹🇭 ไทย •\n 🇮🇳 हिन्दी •\n 🇧🇩 বাংলা •\n 🇵🇰 اردو •\n 🇷🇴 Română •\n 🇸🇪 Svenska •\n 🇮🇹 Italiano •\n 🇬🇷 Ελληνικά •\n 🇭🇺 Magyar •\n 🇫🇮 Suomi •\n 🇩🇰 Dansk •\n 🇳🇴 Norsk \n \n\n Persistent memory compression system built for Claude Code . \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n Quick Start •\n How It Works •\n Search Tools •\n Documentation •\n Configuration •\n Troubleshooting •\n License \n \n\n \n Claude-Mem seamlessly preserves context across sessions by automatically capturing tool usage observations, generating semantic summaries, and making them available to future sessions. This enables Claude to maintain continuity of knowledge about projects even after sessions end or reconnect.\n \n\n---\n\nQuick Start\n\nInstall claude-mem for Grok Bot:\n\n \n\nGrok Bot has no host hooks, so we watch the chat log files. Default is CMEM Pro, the hosted memory. Local observer is opt-in: --provider host. Installing this plugin does not install Cursor.\n\n**Awareness push pilot (LFG + Orifice):** needle observations (decision, bugfix, security_alert, sensitive) are appended as dated - YYYY-MM-DD [awareness] … lines into that bot's memory/log/YYYY-MM.md. Grok Bot already re-reads the log from disk. This does not write profile.md, user-memory, or project memory. Disable with CLAUDE_MEM_GROK_BOT_AWARENESS_ENABLED=false.\n\nInstall with a single command:\n\n \n\nThe installer sets everything up first, then asks you to sign in to claude-mem in your browser (email magic link — no card required). Signing in provisions a memory key for your account and unlocks the **claude-mem observer** with a **30 Day Free Trial**: memory runs off-plan, so you get up to 100% more usage from your plan. When the free trial ends, memory automatically falls back to your Anthropic plan unless you subscribe. After sign-in you pick your memory provider — the claude-mem observer, your own OpenRouter or Gemini key, or your Anthropic plan.\n\nPrefer to skip the sign-in? Pass an explicit --provider flag, set CLAUDE_MEM_ONLINE_OPTIN=false, or run in CI/non-interactive shells — the installer completes without any account interaction.\n\nOr install for OpenCode:\n\n \n\nThis works with OpenCode 1.3.4 or later, including OpenCode 2. Restart OpenCode after installing.\n\nOr install for **T3 Code** (Codex and Claude Code providers):\n\n \n\nThe installer discovers T3 Code's enabled providers, registers native Claude-Mem plugins in their configured homes, and supports T3-managed Codex. Restart T3 Code, trust the provider's hooks when prompted, and start a new thread. See the T3 Code integration guide for custom server settings, status, and removal.\n\nOr install for Antigravity CLI (setup guide):\n\n \n\nOr install for OMP (Oh My Pi):\n\n \n\nOr install the native Pi extension or DeepSeek Harness plugin:\n\n \n\nPi and DeepSeek Harness use the worker runtime. See native harness setup for capture, recall, and troubleshooting.\n\nOr install from the plugin marketplace inside Claude Code:\n\n \n\nRestart Claude Code. Context from previous sessions will automatically appear in new sessions.\n\n> **Note:** Claude-Mem is also published on npm, but npm install -g claude-mem installs the **SDK/library only** — it does not register the plugin hooks or set up the worker service. Always install via npx claude-mem install or the /plugin commands above.\n\n🦞 OpenClaw Gateway\n\nInstall claude-mem as a persistent memory plugin on OpenClaw gateways with a single command:\n\n \n\nThe installer handles dependencies, plugin setup, AI provider configuration, worker startup, and optional real-time observation feeds to Telegram, Discord, Slack, and more. See the OpenClaw Integration Guide for details.\n\n**Key Features:**\n\n- 🧠 **Persistent Memory** - Context survives across sessions\n- 📊 **Progressive Disclosure** - Layered memory retrieval with token cost visibility\n- 🔍 **Skill-Based Search** - Query your project history with mem-search skill\n- 🖥️ **Web Viewer UI** - Real-time memory stream at the worker URL printed on startup\n- 💻 **Claude Desktop Skill** - Search memory from Claude Desktop conversations\n- 🔒 **Privacy Control** - Use tags to exclude sensitive content from storage\n- ⚙️ **Context Configuration** - Fine-grained control over what context gets injected\n- 🤖 **Automatic Operation** - No manual intervention required\n- 🔗 **Citations** - Reference past observations with IDs through the worker API or view all in the web viewer\n\n---\n\nDocumentation\n\n📚 **View Full Documentation** - Browse on official website\n\nGetting Started\n\n- **Installation Guide** - Quick start & advanced installation\n- **Usage Guide** - How Claude-Mem works automatically\n- **Search Tools** - Query your project history with natural language\n- **Cloud Sync** - Back up your memories to cmem.ai — no daemon, the worker syncs on write\n\nBest Practices\n\n- **Context Engineering** - AI agent context optimization principles\n- **Progressive Disclosure** - Philosophy behind Claude-Mem's context priming strategy\n\nArchitecture\n\n- **Overview** - System components & data flow\n- **Architecture Evolution** - The journey from v3 to v5\n- **Hooks Architecture** - How Claude-Mem uses lifecycle hooks\n- **Hooks Reference** - Every hook entry explained\n- **Worker Service** - HTTP API & Bun management\n- **Database** - SQLite schema & FTS5 search\n- **Search Architecture** - Hybrid search with Chroma vector database\n\nConfiguration & Development\n\n- **Configuration** - Environment variables & settings\n- **Development** - Building, testing, contributing\n- **Troubleshooting** - Common issues & solutions\n\n---\n\nHow It Works\n\n**Core Components:**\n\n1. **Lifecycle Hooks** - SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Stop, SessionEnd. Each is one shell-free command, claude-mem hook claude-code , run by a small launcher that the installer puts on PATH\n2. **Smart Install** - Cached dependency checker (Setup hook on claude --init / --maintenance, plus npx claude-mem install / repair)\n3. **Worker Service** - Local HTTP API with web viewer UI and search endpoints, managed by Bun\n4. **SQLite Database** - Stores sessions, observations, summaries\n5. **mem-search Skill** - Natural language queries with progressive disclosure\n6. **Chroma Vector Database** - Hybrid semantic + keyword search for intelligent context retrieval\n\nSee Architecture Overview for details.\n\n---\n\nMCP Search Tools\n\nClaude-Mem provides intelligent memory search through **4 MCP tools** following a token-efficient **3-layer workflow pattern**:\n\n**The 3-Layer Workflow:**\n\n1. **search** - Get compact index with IDs (~50-100 tokens/result)\n2. **timeline** - Get chronological context around interesting results\n3. **get_observations** - Fetch full details ONLY for filtered IDs (~500-1,000 tokens/result)\n\n**How It Works:**\n- Claude uses MCP tools to search your memory\n- Start with search to get an index of results\n- Use timeline to see what was happening around specific observations\n- Use get_observations to fetch full details for relevant IDs\n- **~10x token savings** by filtering before fetching details\n\n**Available MCP Tools:**\n\n1. **search** - Search memory index with full-text queries, filters by type/date/project\n2. **timeline** - Get chronological context around a specific observation or query\n3. **get_observations** - Fetch full observation details by IDs (always batch multiple IDs)\n\n**Example Usage:**\n\n \n\nSee Search Tools Guide for detailed examples.\n\n---\n\nSystem Requirements\n\n- **Node.js**: 20.0.0 or higher\n- **Claude Code**: Latest version with plugin support\n- **Bun**: JavaScript runtime and process manager (auto-installed if missing)\n- **uv**: Python package manager for vector search (auto-installed if missing)\n- **SQLite 3**: ", 'readme_raw_len': 12188, 'one_line_zh': '为每个 Agent 提供跨会话持久上下文，捕获 Agent 在会话中的所有操作…', 'one_line_plain': 'Captures everything an agent does during sessions to provide persistent context across sessions'}, {'id': 4262, 'key': 'modelcontextprotocol-servers-tree-main-src-everything', 'kind': 'mcp', 'name': 'modelcontextprotocol/server-everything', 'one_line': '📇 🏠 - Exercises all features of the MCP protocol', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol Servers', 'url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/everything', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/everything', 'downloads': None, 'stars': 91381, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/modelcontextprotocol.png?size=64', 'official_desc': '📇 🏠 - Exercises all features of the MCP protocol.', 'stars_own': 91381, 'stars_creator': None, 'stars_own_repo': 'modelcontextprotocol/servers', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol servers\n\nThis repository is a collection of *reference implementations* for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources.\n\n> [!IMPORTANT]\n> If you are looking for a list of MCP servers, you can browse published servers on the MCP Registry. The repository served by this README is dedicated to housing just the small number of reference servers maintained by the MCP steering group.\n\n> [!WARNING]\n> The servers in this repository are intended as **reference implementations** to demonstrate MCP features and SDK usage. They are meant to serve as educational examples for developers building their own MCP servers, not as production-ready solutions. Developers should evaluate their own security requirements and implement appropriate safeguards based on their specific threat model and use case.\n\nThe servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.\nTypically, each MCP server is implemented with an MCP SDK:\n\n- C# MCP SDK\n- Go MCP SDK\n- Java MCP SDK\n- Kotlin MCP SDK\n- PHP MCP SDK\n- Python MCP SDK\n- Ruby MCP SDK\n- Rust MCP SDK\n- Swift MCP SDK\n- TypeScript MCP SDK\n\n🌟 Reference Servers\n\nThese servers aim to demonstrate MCP features and the official SDKs.\n\n- **Everything** - Reference / test server with prompts, resources, and tools.\n- **Fetch** - Web content fetching and conversion for efficient LLM usage.\n- **Filesystem** - Secure file operations with configurable access controls.\n- **Git** - Tools to read, search, and manipulate Git repositories.\n- **Memory** - Knowledge graph-based persistent memory system.\n- **Sequential Thinking** - Dynamic and reflective problem-solving through thought sequences.\n- **Time** - Time and timezone conversion capabilities.\n\nArchived\n\nThe following reference servers are now archived and can be found at servers-archived.\n\n- **AWS KB Retrieval** - Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.\n- **Brave Search** - Web and local search using Brave\'s Search API. Has been replaced by the official server (@brave/brave-search-mcp-server).\n- **EverArt** - AI image generation using various models.\n- **GitHub** - Repository management, file operations, and GitHub API integration.\n- **GitLab** - GitLab API, enabling project management.\n- **Google Drive** - File access and search capabilities for Google Drive.\n- **Google Maps** - Location services, directions, and place details.\n- **PostgreSQL** - Read-only database access with schema inspection.\n- **Puppeteer** - Browser automation and web scraping.\n- **Redis** - Interact with Redis key-value stores.\n- **Sentry** - Retrieving and analyzing issues from Sentry.io.\n- **Slack** - Channel management and messaging capabilities. Now maintained by Zencoder\n- **SQLite** - Database interaction and business intelligence capabilities.\n\n🚀 Getting Started\n\nUsing MCP Servers in this Repository\nTypeScript-based servers in this repository can be used directly with npx.\n\nFor example, this will start the Memory server:\n \n\nPython-based servers in this repository can be used directly with uvx or pip. uvx is recommended for ease of use and setup.\n\nFor example, this will start the Git server:\n \n\nFollow these instructions to install uv / uvx and these to install pip.\n\nUsing an MCP Client\nHowever, running a server on its own isn\'t very useful, and should instead be configured into an MCP client. For example, here\'s the Claude Desktop configuration to use the above server:\n\n \n\nOn Windows, wrap npx with cmd /c:\n\n \n\nAdditional examples of using the Claude Desktop as an MCP client might look like:\n\n \n\nOn Windows, apply the same wrapper to each npx-based entry above by changing "command" to "cmd" and prepending "/c", "npx" to the existing args. Leave uvx entries unchanged.\n\n🛠️ Creating Your Own Server\n\nInterested in creating your own MCP server? Visit the official documentation at modelcontextprotocol.io for comprehensive guides, best practices, and technical details on implementing MCP servers.\n\n📚 Learn More\n\nSee ADDITIONAL.md for a curated list of frameworks and resources that simplify building MCP servers and clients.\n\n🤝 Contributing\n\nSee CONTRIBUTING.md for information about contributing to this repository.\n\n📦 Releasing\n\nSee RELEASING.md for how packages are published (OIDC trusted publishing from CI — no registry tokens) and how to retry a failed publish.\n\n🔒 Security\n\nSee SECURITY.md for reporting security vulnerabilities.\n\n📜 License\n\nThis project is licensed under the Apache License, Version 2.0 for new contributions, with existing code under MIT - see the LICENSE file for details.\n\n💬 Community\n\n- GitHub Discussions\n\n⭐ Support\n\nIf you find MCP servers useful, please consider starring the repository and contributing new servers or improvements!\n\n---\n\nManaged by Anthropic, but built together with the community. The Model Context Protocol is open source and we encourage everyone to contribute their own servers and improvements!', 'readme_raw_len': 5101, 'one_line_zh': '用于演练 MCP 协议全部特性的官方示例服务。', 'one_line_plain': 'Exercise all features of the MCP protocol'}, {'id': 3452, 'key': 'modelcontextprotocol-servers-tree-main-src-fetch', 'kind': 'mcp', 'name': 'modelcontextprotocol/server-fetch', 'one_line': '🐍 🏠 ☁️ - Web content fetching and processing for AI consumption', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol Servers', 'url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/fetch', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/fetch', 'downloads': None, 'stars': 91381, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/modelcontextprotocol.png?size=64', 'official_desc': '🐍 🏠 ☁️ - Web content fetching and processing for AI consumption.', 'stars_own': 91381, 'stars_creator': None, 'stars_own_repo': 'modelcontextprotocol/servers', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol servers\n\nThis repository is a collection of *reference implementations* for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources.\n\n> [!IMPORTANT]\n> If you are looking for a list of MCP servers, you can browse published servers on the MCP Registry. The repository served by this README is dedicated to housing just the small number of reference servers maintained by the MCP steering group.\n\n> [!WARNING]\n> The servers in this repository are intended as **reference implementations** to demonstrate MCP features and SDK usage. They are meant to serve as educational examples for developers building their own MCP servers, not as production-ready solutions. Developers should evaluate their own security requirements and implement appropriate safeguards based on their specific threat model and use case.\n\nThe servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.\nTypically, each MCP server is implemented with an MCP SDK:\n\n- C# MCP SDK\n- Go MCP SDK\n- Java MCP SDK\n- Kotlin MCP SDK\n- PHP MCP SDK\n- Python MCP SDK\n- Ruby MCP SDK\n- Rust MCP SDK\n- Swift MCP SDK\n- TypeScript MCP SDK\n\n🌟 Reference Servers\n\nThese servers aim to demonstrate MCP features and the official SDKs.\n\n- **Everything** - Reference / test server with prompts, resources, and tools.\n- **Fetch** - Web content fetching and conversion for efficient LLM usage.\n- **Filesystem** - Secure file operations with configurable access controls.\n- **Git** - Tools to read, search, and manipulate Git repositories.\n- **Memory** - Knowledge graph-based persistent memory system.\n- **Sequential Thinking** - Dynamic and reflective problem-solving through thought sequences.\n- **Time** - Time and timezone conversion capabilities.\n\nArchived\n\nThe following reference servers are now archived and can be found at servers-archived.\n\n- **AWS KB Retrieval** - Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.\n- **Brave Search** - Web and local search using Brave\'s Search API. Has been replaced by the official server (@brave/brave-search-mcp-server).\n- **EverArt** - AI image generation using various models.\n- **GitHub** - Repository management, file operations, and GitHub API integration.\n- **GitLab** - GitLab API, enabling project management.\n- **Google Drive** - File access and search capabilities for Google Drive.\n- **Google Maps** - Location services, directions, and place details.\n- **PostgreSQL** - Read-only database access with schema inspection.\n- **Puppeteer** - Browser automation and web scraping.\n- **Redis** - Interact with Redis key-value stores.\n- **Sentry** - Retrieving and analyzing issues from Sentry.io.\n- **Slack** - Channel management and messaging capabilities. Now maintained by Zencoder\n- **SQLite** - Database interaction and business intelligence capabilities.\n\n🚀 Getting Started\n\nUsing MCP Servers in this Repository\nTypeScript-based servers in this repository can be used directly with npx.\n\nFor example, this will start the Memory server:\n \n\nPython-based servers in this repository can be used directly with uvx or pip. uvx is recommended for ease of use and setup.\n\nFor example, this will start the Git server:\n \n\nFollow these instructions to install uv / uvx and these to install pip.\n\nUsing an MCP Client\nHowever, running a server on its own isn\'t very useful, and should instead be configured into an MCP client. For example, here\'s the Claude Desktop configuration to use the above server:\n\n \n\nOn Windows, wrap npx with cmd /c:\n\n \n\nAdditional examples of using the Claude Desktop as an MCP client might look like:\n\n \n\nOn Windows, apply the same wrapper to each npx-based entry above by changing "command" to "cmd" and prepending "/c", "npx" to the existing args. Leave uvx entries unchanged.\n\n🛠️ Creating Your Own Server\n\nInterested in creating your own MCP server? Visit the official documentation at modelcontextprotocol.io for comprehensive guides, best practices, and technical details on implementing MCP servers.\n\n📚 Learn More\n\nSee ADDITIONAL.md for a curated list of frameworks and resources that simplify building MCP servers and clients.\n\n🤝 Contributing\n\nSee CONTRIBUTING.md for information about contributing to this repository.\n\n📦 Releasing\n\nSee RELEASING.md for how packages are published (OIDC trusted publishing from CI — no registry tokens) and how to retry a failed publish.\n\n🔒 Security\n\nSee SECURITY.md for reporting security vulnerabilities.\n\n📜 License\n\nThis project is licensed under the Apache License, Version 2.0 for new contributions, with existing code under MIT - see the LICENSE file for details.\n\n💬 Community\n\n- GitHub Discussions\n\n⭐ Support\n\nIf you find MCP servers useful, please consider starring the repository and contributing new servers or improvements!\n\n---\n\nManaged by Anthropic, but built together with the community. The Model Context Protocol is open source and we encourage everyone to contribute their own servers and improvements!', 'readme_raw_len': 5101, 'one_line_zh': '为 AI 消费而抓取并处理网页内容的官方 MCP 服务。', 'one_line_plain': 'Fetch and process web content for AI consumption'}, {'id': 1933, 'key': 'modelcontextprotocol-servers-tree-main-src-filesystem', 'kind': 'mcp', 'name': 'modelcontextprotocol/server-filesystem', 'one_line': '📇 🏠 - Direct local file system access', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol Servers', 'url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem', 'downloads': None, 'stars': 91381, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/modelcontextprotocol.png?size=64', 'official_desc': '📇 🏠 - Direct local file system access.', 'stars_own': 91381, 'stars_creator': None, 'stars_own_repo': 'modelcontextprotocol/servers', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol servers\n\nThis repository is a collection of *reference implementations* for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources.\n\n> [!IMPORTANT]\n> If you are looking for a list of MCP servers, you can browse published servers on the MCP Registry. The repository served by this README is dedicated to housing just the small number of reference servers maintained by the MCP steering group.\n\n> [!WARNING]\n> The servers in this repository are intended as **reference implementations** to demonstrate MCP features and SDK usage. They are meant to serve as educational examples for developers building their own MCP servers, not as production-ready solutions. Developers should evaluate their own security requirements and implement appropriate safeguards based on their specific threat model and use case.\n\nThe servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.\nTypically, each MCP server is implemented with an MCP SDK:\n\n- C# MCP SDK\n- Go MCP SDK\n- Java MCP SDK\n- Kotlin MCP SDK\n- PHP MCP SDK\n- Python MCP SDK\n- Ruby MCP SDK\n- Rust MCP SDK\n- Swift MCP SDK\n- TypeScript MCP SDK\n\n🌟 Reference Servers\n\nThese servers aim to demonstrate MCP features and the official SDKs.\n\n- **Everything** - Reference / test server with prompts, resources, and tools.\n- **Fetch** - Web content fetching and conversion for efficient LLM usage.\n- **Filesystem** - Secure file operations with configurable access controls.\n- **Git** - Tools to read, search, and manipulate Git repositories.\n- **Memory** - Knowledge graph-based persistent memory system.\n- **Sequential Thinking** - Dynamic and reflective problem-solving through thought sequences.\n- **Time** - Time and timezone conversion capabilities.\n\nArchived\n\nThe following reference servers are now archived and can be found at servers-archived.\n\n- **AWS KB Retrieval** - Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.\n- **Brave Search** - Web and local search using Brave\'s Search API. Has been replaced by the official server (@brave/brave-search-mcp-server).\n- **EverArt** - AI image generation using various models.\n- **GitHub** - Repository management, file operations, and GitHub API integration.\n- **GitLab** - GitLab API, enabling project management.\n- **Google Drive** - File access and search capabilities for Google Drive.\n- **Google Maps** - Location services, directions, and place details.\n- **PostgreSQL** - Read-only database access with schema inspection.\n- **Puppeteer** - Browser automation and web scraping.\n- **Redis** - Interact with Redis key-value stores.\n- **Sentry** - Retrieving and analyzing issues from Sentry.io.\n- **Slack** - Channel management and messaging capabilities. Now maintained by Zencoder\n- **SQLite** - Database interaction and business intelligence capabilities.\n\n🚀 Getting Started\n\nUsing MCP Servers in this Repository\nTypeScript-based servers in this repository can be used directly with npx.\n\nFor example, this will start the Memory server:\n \n\nPython-based servers in this repository can be used directly with uvx or pip. uvx is recommended for ease of use and setup.\n\nFor example, this will start the Git server:\n \n\nFollow these instructions to install uv / uvx and these to install pip.\n\nUsing an MCP Client\nHowever, running a server on its own isn\'t very useful, and should instead be configured into an MCP client. For example, here\'s the Claude Desktop configuration to use the above server:\n\n \n\nOn Windows, wrap npx with cmd /c:\n\n \n\nAdditional examples of using the Claude Desktop as an MCP client might look like:\n\n \n\nOn Windows, apply the same wrapper to each npx-based entry above by changing "command" to "cmd" and prepending "/c", "npx" to the existing args. Leave uvx entries unchanged.\n\n🛠️ Creating Your Own Server\n\nInterested in creating your own MCP server? Visit the official documentation at modelcontextprotocol.io for comprehensive guides, best practices, and technical details on implementing MCP servers.\n\n📚 Learn More\n\nSee ADDITIONAL.md for a curated list of frameworks and resources that simplify building MCP servers and clients.\n\n🤝 Contributing\n\nSee CONTRIBUTING.md for information about contributing to this repository.\n\n📦 Releasing\n\nSee RELEASING.md for how packages are published (OIDC trusted publishing from CI — no registry tokens) and how to retry a failed publish.\n\n🔒 Security\n\nSee SECURITY.md for reporting security vulnerabilities.\n\n📜 License\n\nThis project is licensed under the Apache License, Version 2.0 for new contributions, with existing code under MIT - see the LICENSE file for details.\n\n💬 Community\n\n- GitHub Discussions\n\n⭐ Support\n\nIf you find MCP servers useful, please consider starring the repository and contributing new servers or improvements!\n\n---\n\nManaged by Anthropic, but built together with the community. The Model Context Protocol is open source and we encourage everyone to contribute their own servers and improvements!', 'readme_raw_len': 5101, 'one_line_zh': '📇 🏠 - 直接访问本地文件系统。', 'one_line_plain': 'Direct access to read and write local files'}, {'id': 4016, 'key': 'modelcontextprotocol-servers-tree-main-src-git', 'kind': 'mcp', 'name': 'modelcontextprotocol/server-git', 'one_line': '🐍 🏠 - Read, search and analyze local Git repositories', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol Servers', 'url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/git', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/git', 'downloads': None, 'stars': 91381, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/modelcontextprotocol.png?size=64', 'official_desc': '🐍 🏠 - Read, search and analyze local Git repositories.', 'stars_own': 91381, 'stars_creator': None, 'stars_own_repo': 'modelcontextprotocol/servers', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol servers\n\nThis repository is a collection of *reference implementations* for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources.\n\n> [!IMPORTANT]\n> If you are looking for a list of MCP servers, you can browse published servers on the MCP Registry. The repository served by this README is dedicated to housing just the small number of reference servers maintained by the MCP steering group.\n\n> [!WARNING]\n> The servers in this repository are intended as **reference implementations** to demonstrate MCP features and SDK usage. They are meant to serve as educational examples for developers building their own MCP servers, not as production-ready solutions. Developers should evaluate their own security requirements and implement appropriate safeguards based on their specific threat model and use case.\n\nThe servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.\nTypically, each MCP server is implemented with an MCP SDK:\n\n- C# MCP SDK\n- Go MCP SDK\n- Java MCP SDK\n- Kotlin MCP SDK\n- PHP MCP SDK\n- Python MCP SDK\n- Ruby MCP SDK\n- Rust MCP SDK\n- Swift MCP SDK\n- TypeScript MCP SDK\n\n🌟 Reference Servers\n\nThese servers aim to demonstrate MCP features and the official SDKs.\n\n- **Everything** - Reference / test server with prompts, resources, and tools.\n- **Fetch** - Web content fetching and conversion for efficient LLM usage.\n- **Filesystem** - Secure file operations with configurable access controls.\n- **Git** - Tools to read, search, and manipulate Git repositories.\n- **Memory** - Knowledge graph-based persistent memory system.\n- **Sequential Thinking** - Dynamic and reflective problem-solving through thought sequences.\n- **Time** - Time and timezone conversion capabilities.\n\nArchived\n\nThe following reference servers are now archived and can be found at servers-archived.\n\n- **AWS KB Retrieval** - Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.\n- **Brave Search** - Web and local search using Brave\'s Search API. Has been replaced by the official server (@brave/brave-search-mcp-server).\n- **EverArt** - AI image generation using various models.\n- **GitHub** - Repository management, file operations, and GitHub API integration.\n- **GitLab** - GitLab API, enabling project management.\n- **Google Drive** - File access and search capabilities for Google Drive.\n- **Google Maps** - Location services, directions, and place details.\n- **PostgreSQL** - Read-only database access with schema inspection.\n- **Puppeteer** - Browser automation and web scraping.\n- **Redis** - Interact with Redis key-value stores.\n- **Sentry** - Retrieving and analyzing issues from Sentry.io.\n- **Slack** - Channel management and messaging capabilities. Now maintained by Zencoder\n- **SQLite** - Database interaction and business intelligence capabilities.\n\n🚀 Getting Started\n\nUsing MCP Servers in this Repository\nTypeScript-based servers in this repository can be used directly with npx.\n\nFor example, this will start the Memory server:\n \n\nPython-based servers in this repository can be used directly with uvx or pip. uvx is recommended for ease of use and setup.\n\nFor example, this will start the Git server:\n \n\nFollow these instructions to install uv / uvx and these to install pip.\n\nUsing an MCP Client\nHowever, running a server on its own isn\'t very useful, and should instead be configured into an MCP client. For example, here\'s the Claude Desktop configuration to use the above server:\n\n \n\nOn Windows, wrap npx with cmd /c:\n\n \n\nAdditional examples of using the Claude Desktop as an MCP client might look like:\n\n \n\nOn Windows, apply the same wrapper to each npx-based entry above by changing "command" to "cmd" and prepending "/c", "npx" to the existing args. Leave uvx entries unchanged.\n\n🛠️ Creating Your Own Server\n\nInterested in creating your own MCP server? Visit the official documentation at modelcontextprotocol.io for comprehensive guides, best practices, and technical details on implementing MCP servers.\n\n📚 Learn More\n\nSee ADDITIONAL.md for a curated list of frameworks and resources that simplify building MCP servers and clients.\n\n🤝 Contributing\n\nSee CONTRIBUTING.md for information about contributing to this repository.\n\n📦 Releasing\n\nSee RELEASING.md for how packages are published (OIDC trusted publishing from CI — no registry tokens) and how to retry a failed publish.\n\n🔒 Security\n\nSee SECURITY.md for reporting security vulnerabilities.\n\n📜 License\n\nThis project is licensed under the Apache License, Version 2.0 for new contributions, with existing code under MIT - see the LICENSE file for details.\n\n💬 Community\n\n- GitHub Discussions\n\n⭐ Support\n\nIf you find MCP servers useful, please consider starring the repository and contributing new servers or improvements!\n\n---\n\nManaged by Anthropic, but built together with the community. The Model Context Protocol is open source and we encourage everyone to contribute their own servers and improvements!', 'readme_raw_len': 5101, 'one_line_zh': '读取、检索并分析本地 Git 仓库的官方 MCP 服务。', 'one_line_plain': 'Read, search and analyze local Git repositories'}, {'id': 2682, 'key': 'modelcontextprotocol-servers-tree-main-src-memory', 'kind': 'mcp', 'name': 'modelcontextprotocol/server-memory', 'one_line': '📇 🏠 - Knowledge graph-based persistent memory for maintaining context', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol Servers', 'url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/memory', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/modelcontextprotocol/servers/tree/main/src/memory', 'downloads': None, 'stars': 91381, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/modelcontextprotocol.png?size=64', 'official_desc': '📇 🏠 - Knowledge graph-based persistent memory for maintaining context.', 'stars_own': 91381, 'stars_creator': None, 'stars_own_repo': 'modelcontextprotocol/servers', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol servers\n\nThis repository is a collection of *reference implementations* for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources.\n\n> [!IMPORTANT]\n> If you are looking for a list of MCP servers, you can browse published servers on the MCP Registry. The repository served by this README is dedicated to housing just the small number of reference servers maintained by the MCP steering group.\n\n> [!WARNING]\n> The servers in this repository are intended as **reference implementations** to demonstrate MCP features and SDK usage. They are meant to serve as educational examples for developers building their own MCP servers, not as production-ready solutions. Developers should evaluate their own security requirements and implement appropriate safeguards based on their specific threat model and use case.\n\nThe servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.\nTypically, each MCP server is implemented with an MCP SDK:\n\n- C# MCP SDK\n- Go MCP SDK\n- Java MCP SDK\n- Kotlin MCP SDK\n- PHP MCP SDK\n- Python MCP SDK\n- Ruby MCP SDK\n- Rust MCP SDK\n- Swift MCP SDK\n- TypeScript MCP SDK\n\n🌟 Reference Servers\n\nThese servers aim to demonstrate MCP features and the official SDKs.\n\n- **Everything** - Reference / test server with prompts, resources, and tools.\n- **Fetch** - Web content fetching and conversion for efficient LLM usage.\n- **Filesystem** - Secure file operations with configurable access controls.\n- **Git** - Tools to read, search, and manipulate Git repositories.\n- **Memory** - Knowledge graph-based persistent memory system.\n- **Sequential Thinking** - Dynamic and reflective problem-solving through thought sequences.\n- **Time** - Time and timezone conversion capabilities.\n\nArchived\n\nThe following reference servers are now archived and can be found at servers-archived.\n\n- **AWS KB Retrieval** - Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.\n- **Brave Search** - Web and local search using Brave\'s Search API. Has been replaced by the official server (@brave/brave-search-mcp-server).\n- **EverArt** - AI image generation using various models.\n- **GitHub** - Repository management, file operations, and GitHub API integration.\n- **GitLab** - GitLab API, enabling project management.\n- **Google Drive** - File access and search capabilities for Google Drive.\n- **Google Maps** - Location services, directions, and place details.\n- **PostgreSQL** - Read-only database access with schema inspection.\n- **Puppeteer** - Browser automation and web scraping.\n- **Redis** - Interact with Redis key-value stores.\n- **Sentry** - Retrieving and analyzing issues from Sentry.io.\n- **Slack** - Channel management and messaging capabilities. Now maintained by Zencoder\n- **SQLite** - Database interaction and business intelligence capabilities.\n\n🚀 Getting Started\n\nUsing MCP Servers in this Repository\nTypeScript-based servers in this repository can be used directly with npx.\n\nFor example, this will start the Memory server:\n \n\nPython-based servers in this repository can be used directly with uvx or pip. uvx is recommended for ease of use and setup.\n\nFor example, this will start the Git server:\n \n\nFollow these instructions to install uv / uvx and these to install pip.\n\nUsing an MCP Client\nHowever, running a server on its own isn\'t very useful, and should instead be configured into an MCP client. For example, here\'s the Claude Desktop configuration to use the above server:\n\n \n\nOn Windows, wrap npx with cmd /c:\n\n \n\nAdditional examples of using the Claude Desktop as an MCP client might look like:\n\n \n\nOn Windows, apply the same wrapper to each npx-based entry above by changing "command" to "cmd" and prepending "/c", "npx" to the existing args. Leave uvx entries unchanged.\n\n🛠️ Creating Your Own Server\n\nInterested in creating your own MCP server? Visit the official documentation at modelcontextprotocol.io for comprehensive guides, best practices, and technical details on implementing MCP servers.\n\n📚 Learn More\n\nSee ADDITIONAL.md for a curated list of frameworks and resources that simplify building MCP servers and clients.\n\n🤝 Contributing\n\nSee CONTRIBUTING.md for information about contributing to this repository.\n\n📦 Releasing\n\nSee RELEASING.md for how packages are published (OIDC trusted publishing from CI — no registry tokens) and how to retry a failed publish.\n\n🔒 Security\n\nSee SECURITY.md for reporting security vulnerabilities.\n\n📜 License\n\nThis project is licensed under the Apache License, Version 2.0 for new contributions, with existing code under MIT - see the LICENSE file for details.\n\n💬 Community\n\n- GitHub Discussions\n\n⭐ Support\n\nIf you find MCP servers useful, please consider starring the repository and contributing new servers or improvements!\n\n---\n\nManaged by Anthropic, but built together with the community. The Model Context Protocol is open source and we encourage everyone to contribute their own servers and improvements!', 'readme_raw_len': 5101, 'one_line_zh': '基于知识图谱的持久记忆，用于维持上下文。', 'one_line_plain': 'Knowledge graph-based persistent memory for maintaining context'}, {'id': 1630, 'key': 'storybookjs-storybook-tree-next-code-addons-mcp', 'kind': 'mcp', 'name': 'storybookjs/storybook', 'one_line': '📇 🏠 - Helps agents write and test Storybook stories for UI components', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Storybook is the industry standard workshop for building, documenting, and testing UI components in isolation', 'url': 'https://github.com/storybookjs/storybook/tree/next/code/addons/mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/storybookjs/storybook/tree/next/code/addons/mcp', 'downloads': None, 'stars': 91338, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/storybookjs.png?size=64', 'official_desc': '📇 🏠 - Helps agents write and test Storybook stories for UI components.', 'stars_own': 91338, 'stars_creator': None, 'stars_own_repo': 'storybookjs/storybook', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Build bulletproof UI components faster \n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \nStorybook is a frontend workshop for building UI components and pages in isolation. Thousands of teams use it for UI development, testing, and documentation. Find out more at storybook.js.org !\n \n\n \n \n \n\n \n View README for: \n \n \n \n\nTable of contents\n\n- 🚀 Getting Started\n- 📒 Projects\n - 🛠 Supported Frameworks & Examples\n - 🔗Addons\n- 🏅 Badges & Presentation materials\n- 👥 Community\n- 👏 Contributing\n - 👨\u200d💻 Development scripts\n - 💸 Sponsors\n - 💵 Backers\n- :memo: License\n\nGetting Started\n\nVisit Storybook's website to learn more about Storybook and to get started.\n\nDocumentation\n\nDocumentation can be found on Storybook's docs site.\n\nExamples\n\nView Component Encyclopedia to see how leading teams use Storybook.\n\nUse storybook.new to quickly create an example project in Stackblitz.\n\nStorybook comes with a lot of addons for component design, documentation, testing, interactivity, and so on. Storybook's API makes it possible to configure and extend in various ways. It has even been extended to support React Native, Android, iOS, and Flutter development for mobile.\n\nCommunity\n\nFor additional help, share your issue in the repo's GitHub Discussions.\n\nProjects\n\nSupported Frameworks\n\n| Renderer | Demo | |\n| -------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |\n| React | | |\n| Angular | | |\n| Vue 3 | | |\n| Web components | | |\n| React Native | | |\n| HTML | | |\n| Ember | | |\n| Svelte | | |\n| Preact | | |\n| Qwik | | |\n| SolidJS | | |\n| Android, iOS, Flutter | | |\n\nAddons\n\n| Addons | |\n| ------------------------------------------------------------------------- | -------------------------------------------------------------------------- |\n| a11y | Test components for user accessibility in Storybook |\n| actions | Log actions as users interact with components in the Storybook UI |\n| backgrounds | Let users choose backgrounds in the Storybook UI |\n| cssresources | Dynamically add/remove CSS resources to the component iframe |\n| design assets | View images, videos, and weblinks alongside your story |\n| docs | Add high quality documentation to your components |\n| events | Interactively fire events to components that respond to EventEmitter |\n| google-analytics | Reports google analytics on stories |\n| graphql | Query a GraphQL server within Storybook stories |\n| jest | View the results of components' unit tests in Storybook |\n| links | Create links between stories |\n| measure | Visually inspect the layout and box model within the Storybook UI |\n| outline | Visually debug the CSS layout and alignment within the Storybook UI |\n| query params | Mock query params |\n| viewport | Change display sizes and layouts for responsive components using Storybook |\n\nSee Addon / Framework Support Table\n\nTo continue improving your experience, we have to eventually deprecate or remove certain addons in favor of new and better tools.\n\nIf you're using info/notes, we highly recommend you migrate to docs instead, and here is a guide to help you.\n\nIf you're using contexts, we highly recommend you migrate to toolbars and here is a guide to help you.\n\nIf you're using addon-storyshots, we highly recommend you migrate to the Storybook test-runner and here is a guide to help you.\n\nBadges & Presentation materials\n\nWe have a badge! Link it to your live Storybook example.\n\n \n\n \n\nIf you're looking for material to use in your Storybook presentation, such as logos, video material, and the colors we use, you can find it all on our brand repo.\n\nCommunity\n\n- Tweeting via @storybookjs\n- Blogging at storybook.js.org and Medium\n- Chatting on Discord\n- Videos and streams at YouTube\n\nContributing\n\nContributions to Storybook are always welcome!\n\n- 📥 Pull requests and 🌟 Stars are always welcome.\n- Read our contributing guide to get started,\n or find us on Discord, we will take the time to guide you.\n\nLooking for a first issue to tackle?\n\n- We tag issues with when we think they are well suited for people who are new to the codebase or OSS in general.\n- Talk to us, we'll find something that suits your skills and learning interests.\n\nDevelopment scripts\n\nStorybook is organized as a monorepo. Useful scripts include:\n\nyarn start\n\n> Runs a sandbox template storybook with test stories\n\nyarn task\n\n> As above, but gives you options to customize the sandbox (e.g. selecting other frameworks)\n\nyarn lint\n\n> boolean check if code conforms to linting rules - uses remark & eslint\n\n- yarn lint:js - will check js\n- yarn lint:md - will check markdown + code samples\n- yarn lint:js --fix - will automatically fix js\n\nyarn test\n\n> boolean check if unit tests all pass - uses jest\n\n- yarn run test --core --watch - will run core tests in watch-mode\n\nSponsors\n\nBecome a sponsor to have your logo and website URL on our README on Github. \\[Become a sponsor]\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\nBackers\n\nBy making a recurring donation, you can support us and our work. \\[Become a backer]\n\n \n\nLicense\n\nMIT\n\n-the end-", 'readme_raw_len': 5365, 'one_line_zh': '📇 🏠 - 帮助 Agent 为 UI 组件编写并测试 Storybook 故事', 'one_line_plain': 'MCP helps agents write and test Storybook stories for UI components'}, {'id': 2762, 'key': 'upstash-context7', 'kind': 'mcp', 'name': 'upstash/context7', 'one_line': '📇 ☁️ - Up-to-date code documentation for LLMs and AI code editors', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Context7 Platform Up-to-date docs for AI agents', 'url': 'https://github.com/upstash/context7', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/upstash/context7', 'downloads': None, 'stars': 63321, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/upstash.png?size=64', 'official_desc': '📇 ☁️ - Up-to-date code documentation for LLMs and AI code editors.', 'stars_own': 63321, 'stars_creator': None, 'stars_own_repo': 'upstash/context7', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Context7 - Up-to-date Docs for AI Agents\n\n \n\n \n\n❌ Without Context7\n\nLLMs rely on outdated or generic information about the libraries you use. You get:\n\n- ❌ Code examples are outdated and based on year-old training data\n- ❌ Hallucinated APIs that don\'t even exist\n- ❌ Generic answers for old package versions\n\n✅ With Context7\n\nContext7 pulls up-to-date, version-specific documentation and code examples straight from the source — and gives them to your coding agent.\n\n \n\n \n\n \n\nContext7 fetches up-to-date code examples and documentation right into your coding agent\'s context. No tab-switching, no hallucinated APIs that don\'t exist, no outdated code generation.\n\nWorks in two modes:\n\n- **CLI + Skills** — installs a skill that guides your agent to fetch docs using ctx7 CLI commands (no MCP required)\n- **MCP** — registers a Context7 MCP server so your agent can call documentation tools natively\n\nInstallation\n\n> [!NOTE]\n> **API Key Recommended**: Get a free API key at context7.com/dashboard for higher rate limits.\n\nSet up Context7 for your coding agents with a single command. The ctx7 CLI requires Node.js 22.13 or newer.\n\n \n\nAuthenticates via OAuth, generates an API key, and installs the appropriate skill. You can choose between CLI + Skills or MCP mode. Use --cursor, --claude, or --opencode to target a specific agent.\n\nTo remove the generated setup later, run npx ctx7 remove. If you globally installed the CLI with npm install -g ctx7, remove that package separately with npm uninstall -g ctx7.\n\nTo configure manually, use the Context7 server URL https://mcp.context7.com/mcp with your MCP client and pass your API key via the Authorization: Bearer YOUR_API_KEY header. See the link below for client-specific setup instructions.\n\n**Manual Installation / Other Clients →**\n\nImportant Tips\n\nUse Library Id\n\nIf you already know exactly which library you want to use, add its Context7 ID to your prompt. That way, Context7 can skip the library-matching step and directly retrieve docs.\n\n \n\nThe slash syntax tells Context7 exactly which library to load docs for.\n\nSpecify a Version\n\nTo get documentation for a specific library version, just mention the version in your prompt:\n\n \n\nContext7 will automatically match the appropriate version.\n\nAdd a Rule\n\nIf you installed via ctx7 setup, a skill is configured automatically that triggers Context7 for library-related questions. To set up a rule manually instead, add one to your coding agent:\n\n- **Cursor**: Cursor Settings > Rules\n- **Claude Code**: CLAUDE.md\n- Or the equivalent in your coding agent\n\n**Example rule:**\n\n \n\nAvailable Tools\n\nCLI Commands\n\n- ctx7 library : Searches the Context7 index by library name and returns matching libraries with their IDs.\n- ctx7 docs : Retrieves documentation for a library using a Context7-compatible library ID (e.g., /mongodb/docs, /vercel/next.js).\n\nMCP Tools\n\n- resolve-library-id: Resolves a general library name into a Context7-compatible library ID.\n - query (required): The user\'s question or task (used to rank results by relevance)\n - libraryName (required): The name of the library to search for\n- query-docs: Retrieves documentation for a library using a Context7-compatible library ID.\n - libraryId (required): Exact Context7-compatible library ID (e.g., /mongodb/docs, /vercel/next.js)\n - query (required): The question or task to get relevant documentation for\n - language (optional): The programming language you write in, used to rank code examples in that language first\n\nMore Documentation\n\n- CLI Reference - Full CLI documentation\n- MCP Clients - Manual MCP installation for 30+ clients\n- Adding Libraries - Submit your library to Context7\n- Troubleshooting - Common issues and solutions\n- API Reference - REST API documentation\n- Developer Guide - Run Context7 MCP locally\n\nPackages\n\n- @upstash/context7-mcp - MCP server\n- ctx7 - CLI\n- @upstash/context7-sdk - TypeScript SDK\n- @upstash/context7-tools-ai-sdk - Vercel AI SDK tools\n- @upstash/context7-pi - pi.dev extension\n\nDisclaimer\n\n1- Context7 projects are community-contributed and while we strive to maintain high quality, we cannot guarantee the accuracy, completeness, or security of all library documentation. Projects listed in Context7 are developed and maintained by their respective owners, not by Context7. If you encounter any suspicious, inappropriate, or potentially harmful content, please use the "Report" button on the project page to notify us immediately. We take all reports seriously and will review flagged content promptly to maintain the integrity and safety of our platform. By using Context7, you acknowledge that you do so at your own discretion and risk.\n\n2- This repository hosts the MCP server’s source code. The supporting components — API backend, parsing engine, and crawling engine — are private and not part of this repository.\n\n🤝 Connect with Us\n\nStay updated and join our community:\n\n- 📢 Follow us on X for the latest news and updates\n- 🌐 Visit our Website\n- 💬 Join our Discord Community\n\n📺 Context7 In Media\n\n- Better Stack: "Free Tool Makes Cursor 10x Smarter"\n- Cole Medin: "This is Hands Down the BEST MCP Server for AI Coding Assistants"\n- Income Stream Surfers: "Context7 + SequentialThinking MCPs: Is This AGI?"\n- Julian Goldie SEO: "Context7: New MCP AI Agent Update"\n- JeredBlu: "Context 7 MCP: Get Documentation Instantly + VS Code Setup"\n- Income Stream Surfers: "Context7: The New MCP Server That Will CHANGE AI Coding"\n- AICodeKing: "Context7 + Cline & RooCode: This MCP Server Makes CLINE 100X MORE EFFECTIVE!"\n- Sean Kochel: "5 MCP Servers For Vibe Coding Glory (Just Plug-In & Go)"\n\n📄 License\n\nMIT', 'readme_raw_len': 5621, 'one_line_zh': '向 LLM 和 AI 代码编辑器提供最新代码文档', 'one_line_plain': 'Provide up-to-date code documentation for LLMs and AI code editors'}, {'id': 1431, 'key': 'glips-figma-context-mcp', 'kind': 'mcp', 'name': 'GLips/Figma-Context-MCP', 'one_line': '📇 🏠 - Gives coding agents direct access to Figma data to implement designs in one shot', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server to provide Figma layout information to AI coding agents like Cursor', 'url': 'https://github.com/GLips/Figma-Context-MCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/GLips/Figma-Context-MCP', 'downloads': None, 'stars': 16012, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/GLips.png?size=64', 'official_desc': '📇 🏠 - Gives coding agents direct access to Figma data to implement designs in one shot.', 'stars_own': 16012, 'stars_creator': None, 'stars_own_repo': 'GLips/Figma-Context-MCP', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Framelink MCP for Figma \n Give your coding agent access to your Figma data. Implement designs in any framework in one-shot. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n\nGive Cursor and other AI-powered coding tools access to your Figma files with this Model Context Protocol server.\n\nWhen Cursor has access to Figma design data, it's **way** better at one-shotting designs accurately than alternative approaches like pasting screenshots.\n\n See quickstart instructions → \n\nDemo\n\nWatch a demo of building a UI in Cursor with Figma design data\n\n \n\nHow it works\n\n1. Open your IDE's chat (e.g. agent mode in Cursor).\n2. Paste a link to a Figma file, frame, or group.\n3. Ask Cursor to do something with the Figma file—e.g. implement the design.\n4. Cursor will fetch the relevant metadata from Figma and use it to write your code.\n\nThis MCP server is specifically designed for use with Cursor. Before responding with context from the Figma API, it simplifies and translates the response so only the most relevant layout and styling information is provided to the model.\n\nReducing the amount of context provided to the model helps make the AI more accurate and the responses more relevant.\n\nGetting Started\n\nMany code editors and other AI clients use a configuration file to manage MCP servers.\n\nThe figma-developer-mcp server can be configured by adding the following to your configuration file.\n\n> NOTE: You will need to create a Figma access token to use this server. Instructions on how to create a Figma API access token can be found here.\n\nMacOS / Linux\n\n \n\nWindows\n\n \n\nOr you can set FIGMA_API_KEY and PORT in the env field.\n\nIf you need more information on how to configure the Framelink MCP for Figma, see the Framelink docs.\n\nStar History\n\n \n\nLearn More\n\nThe Framelink MCP for Figma is simple but powerful. Get the most out of it by learning more at the Framelink site.", 'readme_raw_len': 1859, 'one_line_zh': '让编码 Agent 直接访问 Figma 数据，一步到位实现设计还原', 'one_line_plain': 'Give coding agents direct access to Figma data to implement designs instantly'}, {'id': 813, 'key': 'wonderwhy-er-desktopcommandermcp', 'kind': 'mcp', 'name': 'wonderwhy-er/DesktopCommanderMCP', 'one_line': '📇 🏠 🍎 🪟 🐧 - Manage and execute programs, and read, write, search and edit code and text files', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'This is MCP server for Claude that gives it terminal control, file system search and diff file editing capabilities', 'url': 'https://github.com/wonderwhy-er/DesktopCommanderMCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/wonderwhy-er/DesktopCommanderMCP', 'downloads': None, 'stars': 10102, 'installs': None, 'mentions': 22, 'score': 2203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/wonderwhy-er.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - A swiss-army-knife that can manage/execute programs and read/write/search/edit code and text files.', 'stars_own': 10102, 'stars_creator': None, 'stars_own_repo': 'wonderwhy-er/DesktopCommanderMCP', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Desktop Commander MCP\nSearch, update, manage files and run terminal commands with AI\n\n \n \n \n \n \n\n \n\nWork with code and text, run processes, and automate tasks, going far beyond other AI editors - while using host client subscriptions instead of API token costs.\n\n \n \n \n\nTable of Contents\n- Features\n- How to install\n- Getting Started\n- Usage\n- File Preview UI & Markdown Editor\n- Handling Long-Running Commands\n- Work in Progress and TODOs\n- Sponsors and Supporters\n- Website\n- Media\n- Testimonials\n- Frequently Asked Questions\n- Contributing\n- License\n\nAll of your AI development tools in one place.\nDesktop Commander puts all dev tools in one chat.\nExecute long-running terminal commands on your computer and manage processes through Model Context Protocol (MCP). Built on top of MCP Filesystem Server to provide additional search and replace file editing capabilities.\n\nFeatures\n\n- **Remote AI Control** - Use Desktop Commander from ChatGPT, Claude web, and other AI services via Remote MCP\n- **File Preview UI** - Visual file previews in Claude Desktop with rendered markdown, inline images, expandable content, built-in markdown editor, and quick "Open in folder" access\n- **Enhanced terminal commands with interactive process control**\n- **Execute code in memory (Python, Node.js, R) without saving files**\n- **Instant data analysis - just ask to analyze CSV/JSON/Excel files**\n- **Native Excel file support** - Read, write, edit, and search Excel files (.xlsx, .xls, .xlsm) without external tools\n- **PDF support** - Read PDFs with text extraction, create new PDFs from markdown, modify existing PDFs\n- **DOCX support** - Read, create, edit, and search Word documents (.docx) with surgical XML editing and markdown-to-DOCX conversion\n- **Interact with running processes (SSH, databases, development servers)**\n- Execute terminal commands with output streaming\n- Command timeout and background execution support\n- Process management (list and kill processes)\n- Session management for long-running commands\n- **Process output pagination** - Read terminal output with offset/length controls to prevent context overflow\n- Server configuration management:\n - Get/set configuration values\n - Update multiple settings at once\n - Dynamic configuration changes without server restart\n- Full filesystem operations:\n - Read/write files (text, Excel, PDF, DOCX)\n - Create/list directories\n - **Recursive directory listing** with configurable depth and context overflow protection for large folders\n - Move files/directories\n - Search files and content (including Excel content)\n - Get file metadata\n - **Negative offset file reading**: Read from end of files using negative offset values (like Unix tail)\n- Code editing capabilities:\n - Surgical text replacements for small changes\n - Full file rewrites for major changes\n - Multiple file support\n - Pattern-based replacements\n - vscode-ripgrep based recursive code or text search in folders\n- Local tool-call history and audit logs:\n - Tool calls and arguments are recorded locally on the machine running Desktop Commander\n - Recent call history with bounded output previews is available through get_recent_tool_calls\n - Size-based rotation/trimming keeps the active history files bounded\n- Safety guardrails (not a sandbox — see SECURITY.md):\n - Symlink traversal prevention on file operations\n - Command blocklist for accidental execution\n - Docker isolation for complete isolation\n\nHow to install\n\nInstall in Claude Desktop\n\nDesktop Commander offers multiple installation methods for Claude Desktop.\n\n> **📋 Update & Uninstall Information:** Options 1, 2, 3, 4, and 6 have automatic updates. Option 5 requires manual updates. See below for details.\n\n \n Option 1: Install through npx ⭐ Auto-Updates (Requires Node.js) \n\nJust run this in terminal:\n \n\nFor debugging mode (allows Node.js inspector connection):\n \n\n**Command line options during setup:**\n- --debug: Enable debugging mode for Node.js inspector\n- --no-onboarding: Disable onboarding prompts for new users\n\nRestart Claude if running.\n\n**✅ Auto-Updates:** Yes - automatically updates when you restart Claude \n**🔄 Manual Update:** Run the setup command again \n**🗑️ Uninstall:** Run npx @wonderwhy-er/desktop-commander@latest remove\n\n \n\n \n Option 2: Using bash script installer (macOS) ⭐ Auto-Updates (Installs Node.js if needed) \n\n \nThis script handles all dependencies and configuration automatically.\n\n**✅ Auto-Updates:** Yes \n**🔄 Manual Update:** Re-run the bash installer command above \n**🗑️ Uninstall:** Run npx @wonderwhy-er/desktop-commander@latest remove\n\n \n\n \n Option 3: Installing via Smithery ⭐ Auto-Updates (Requires Node.js) \n\n1. **Visit:** https://smithery.ai/server/@wonderwhy-er/desktop-commander\n2. **Login to Smithery** if you haven\'t already\n3. **Select your client** (Claude Desktop) on the right side\n4. **Install with the provided key** that appears after selecting your client\n5. **Restart Claude Desktop**\n\n**✅ Auto-Updates:** Yes - automatically updates when you restart Claude \n**🔄 Manual Update:** Visit the Smithery page and reinstall \n\n \n\n \n Option 4: Add to claude_desktop_config manually ⭐ Auto-Updates (Requires Node.js) \n\nAdd this entry to your claude_desktop_config.json:\n\n- On Mac: ~/Library/Application Support/Claude/claude_desktop_config.json\n- On Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n- On Linux: ~/.config/Claude/claude_desktop_config.json\n\n \nRestart Claude if running.\n\n**✅ Auto-Updates:** Yes - automatically updates when you restart Claude \n**🔄 Manual Update:** Run the setup command again \n**🗑️ Uninstall:** Run npx @wonderwhy-er/desktop-commander@latest remove or remove the entry from your claude_desktop_config.json\n\n \n\n \n Option 5: Checkout locally ❌ Manual Updates (Requires Node.js) \n\n \nRestart Claude if running.\n\nThe setup command will install dependencies, build the server, and configure Claude\'s desktop app.\n\n**❌ Auto-Updates:** No - requires manual git updates \n**🔄 Manual Update:** cd DesktopCommanderMCP && git pull && npm run setup \n**🗑️ Uninstall:** Run npx @wonderwhy-er/desktop-commander@latest remove or remove the cloned directory and MCP server entry from Claude config\n\n \n\n \n Option 6: Docker Installation 🐳 ⭐ Auto-Updates (No Node.js Required) \n\nPerfect for users who want isolation or don\'t have Node.js installed. Runs in a sandboxed Docker container with a persistent work environment.\n\n**Prerequisites:** Docker Desktop installed **and running**, Claude Desktop app installed.\n\n**macOS/Linux:**\n json\n{\n "mcpServers": {\n "desktop-commander-in-docker": {\n "command": "docker",\n "args": ["run", "-i", "--rm", "mcp/desktop-commander:latest"]\n }\n }\n}\n json\n{\n "mcpServers": {\n "desktop-commander-in-docker": {\n "command": "docker",\n "args": [\n "run", "-i", "--rm",\n "-v", "/Users/username/Desktop:/mnt/desktop",\n "-v", "/Users/username/Documents:/mnt/documents",\n "mcp/desktop-commander:latest"\n ]\n }\n }\n}\n json\n{\n "mcpServers": {\n "desktop-commander-in-docker": {\n "command": "docker",\n "args": [\n "run", "-i", "--rm",\n "-v", "dc-system:/usr",\n "-v", "dc-home:/root", \n "-v", "dc-workspace:/workspace",\n "-v", "dc-packages:/var",\n "-v", "/Users/username/Projects:/mnt/Projects",\n "-v", "/Users/username/Downloads:/mnt/Downloads",\n "mcp/desktop-commander:latest"\n ]\n }\n }\n}\n bash\nCheck status\nbash \n\n**✅ Auto-Updates:** Yes - latest tag automatically gets newer versions \n**🔄 Manual Update:** docker pull mcp/desktop-commander:latest then restart Claude \n\n \n\nInstall in Other Clients\n\nDesktop Commander works with any MCP-compatible client. The standard JSON configuration is:\n\n \n\nAdd this to your client\'s MCP configuration file at the locations below:\n\n \n Cursor \n\n \n\nView MCP Server in Directory\n\nOr add manually to ~/.cursor/mcp.json (global) or .cursor/mcp.json in your project folder (project-specific).\n\nSee Cursor MCP docs for more info.\n\n \n\n \n Windsurf \n\nAdd to ~/.codeium/windsurf/mcp_config.json. See Windsurf MCP docs for more info.\n\n \n\n \n VS Code / GitHub Copilot \n\nAdd to .vscode/mcp.json in your pr', 'readme_raw_len': 36608, 'one_line_zh': '📇 🏠 🍎 🪟 🐧 - 管理与执行程序，并读取、写入、搜索和编辑代码与文本文件', 'one_line_plain': 'Manage and execute programs, and read, write, search and edit code and text files'}, {'id': 3921, 'key': 'sooperset-mcp-atlassian', 'kind': 'mcp', 'name': 'sooperset/mcp-atlassian', 'one_line': '🐍 ☁️ - Search, read, create and manage Confluence and Jira content, supporting Confluence Cloud, Jira Cloud…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for Atlassian tools (Confluence, Jira)', 'url': 'https://github.com/sooperset/mcp-atlassian', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/sooperset/mcp-atlassian', 'downloads': None, 'stars': 6019, 'installs': None, 'mentions': 22, 'score': 2203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/sooperset.png?size=64', 'official_desc': '🐍 ☁️ - MCP server for Atlassian products (Confluence and Jira). Supports Confluence Cloud, Jira Cloud, and Jira Server/Data Center. Provides comprehensive tools for searching, reading, creating, and m', 'stars_own': 6019, 'stars_creator': None, 'stars_own_repo': 'sooperset/mcp-atlassian', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'MCP Atlassian\n\n \n \n \n \n \n \n\nModel Context Protocol (MCP) server for Atlassian products (Confluence and Jira). Supports both Cloud and Server/Data Center deployments.\n\nhttps://github.com/user-attachments/assets/35303504-14c6-4ae4-913b-7c25ea511c3e\n\n \n Confluence Demo \n\nhttps://github.com/user-attachments/assets/7fe9c488-ad0c-4876-9b54-120b666bb785\n\n \n\nQuick Start\n\n1. Get Your API Token\n\nGo to https://id.atlassian.com/manage-profile/security/api-tokens and create a token.\n\n> For Server/Data Center, use a Personal Access Token instead. See Authentication.\n\n2. Configure Your IDE\n\nAdd to your Claude Desktop or Cursor MCP configuration:\n\n \n\n> **Server/Data Center users**: Use JIRA_PERSONAL_TOKEN instead of JIRA_USERNAME + JIRA_API_TOKEN. See Authentication for details.\n\nAutohand Code\n\nUse the same uvx server with your Atlassian credentials:\n\n \n\nAdd --scope project after add to keep the configuration in the current\nproject. See Autohand Code for current\ninstallation and CLI details.\n\n3. Start Using\n\nAsk your AI assistant to:\n- **"Find issues assigned to me in PROJ project"**\n- **"Search Confluence for onboarding docs"**\n- **"Create a bug ticket for the login issue"**\n- **"Update the status of PROJ-123 to Done"**\n\nDocumentation\n\nFull documentation is available at **mcp-atlassian.soomiles.com**.\n\nDocumentation is also available in llms.txt format, which LLMs can consume easily:\n- llms.txt — documentation sitemap\n- llms-full.txt — complete documentation\n\n| Topic | Description |\n|-------|-------------|\n| Installation | uvx, Docker, pip, from source |\n| Authentication | API tokens, PAT, OAuth 2.0 |\n| Configuration | IDE setup, environment variables |\n| HTTP Transport | SSE, streamable-http, multi-user |\n| Tools Reference | All Jira & Confluence tools |\n| Troubleshooting | Common issues & debugging |\n\nCompatibility\n\n| Product | Deployment | Support |\n|---------|------------|---------|\n| Confluence | Cloud | Fully supported |\n| Confluence | Server/Data Center | Supported (v6.0+) |\n| Jira | Cloud | Fully supported |\n| Jira | Server/Data Center | Supported (v8.14+) |\n\nKey Tools\n\n| Jira | Confluence |\n|------|------------|\n| jira_search - Search with JQL | confluence_search - Search with CQL |\n| jira_get_issue - Get issue details | confluence_get_page - Get page content |\n| jira_create_issue - Create issues | confluence_create_page - Create pages |\n| jira_update_issue - Update issues | confluence_update_page - Update pages |\n| jira_transition_issue - Change status | confluence_add_comment - Add comments |\n\n**98 tools total** — See Tools Reference for the complete list.\n\nSecurity\n\nNever share API tokens. Keep .env files secure. See SECURITY.md.\n\nContributing\n\nSee CONTRIBUTING.md for development setup.\n\nLicense\n\nMIT - See LICENSE. Not an official Atlassian product.', 'readme_raw_len': 2796, 'one_line_zh': '🐍 ☁️ - 搜索、阅读、创建并管理 Confluence 与 Jira 内容，支持 Confluence Cloud、Jira Cloud…', 'one_line_plain': 'Search, view, add and update content in Confluence and Jira Cloud sites'}, {'id': 797, 'key': 'shashankss1205-codegraphcontext', 'kind': 'mcp', 'name': 'shashankss1205/codegraphcontext', 'one_line': '🐍 🏠 🍎 🪟 🐧 - Index local code into a graph database to give AI assistants context, with graphical code…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/CodeGraphContext/CodeGraphContext', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Shashankss1205/CodeGraphContext', 'downloads': None, 'stars': 4299, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Shashankss1205.png?size=64', 'official_desc': '🐍 🏠 🍎 🪟 🐧 - Index local code into a graph database to give AI assistants context, with graphical code visualizations for humans.', 'stars_own': 4299, 'stars_creator': None, 'stars_own_repo': 'CodeGraphContext/CodeGraphContext', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': '🏗️ CodeGraphContext (CGC)\n\n**Turn code repositories into a queryable graph for AI agents.**\n\n🌐 **Languages:**\n- 🇬🇧 English\n- 🇨🇳 中文\n- 🇰🇷 한국어\n- 🇺🇦 Українська\n- 🇷🇺 Русский\n- 🇯🇵 日本語\n- 🇮🇳 தமிழ்\n- 🇪🇸 Español (Soon)\n\n🌍 **Help translate CodeGraphContext to your language by raising an issue & PR on GitHub Issues!**\n\n \n \n Bridge the gap between deep code graphs and AI context. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\nA powerful **MCP server** and **CLI toolkit** that indexes local code into a graph database to provide context to AI assistants and developers. Use it as a standalone CLI for comprehensive code analysis or connect it to your favorite AI IDE via MCP for AI-powered code understanding.\n\n---\n\n📍 Quick Navigation\n* 🚀 Quick Start \n* 📋 Prerequisites\n* 🏃 How to Run the Project Locally\n* 🌐 Supported Programming Languages \n* 🛠️ CLI Toolkit \n* 🤖 MCP Server \n* 🗄️ Database Options\n* 🔬 SCIP indexing (optional)\n\n---\n\n✨ Experience CGC\n\n👨🏻\u200d💻 Installation and CLI\n> Install in seconds with pip and unlock a powerful CLI for code graph analysis.\n \n\n🛠️ Indexing in Seconds\n> The CLI intelligently parses your tree-sitter nodes to build the graph.\n \n\n🤖 Powering your AI Assistant\n> Use natural language to query complex call-chains via MCP.\n \n\n---\n\nWhy CodeGraphContext?\n\nCodeGraphContext is built for the moment when plain text search stops being enough. It turns a repository into a graph of files, symbols, calls, inheritance, imports, and relationships so you can move from "where is this defined?" to "how does this actually connect?" without jumping between tools.\n\n \n\nComparison at a glance\n\n| Approach | Best for | Tradeoff |\n| --- | --- | --- |\n| grep / file search | Exact string lookup | Misses relationships and code structure |\n| RAG over code chunks | Natural-language retrieval | Can lose symbol-level precision |\n| CodeGraphContext | Repository-wide reasoning | Requires an indexing step |\n\nCommon use cases\n\n- Trace call chains across files and packages.\n- Understand class hierarchies, imports, and module boundaries.\n- Find dead code, hotspots, and unexpected dependencies.\n- Give an AI assistant the same structural context a maintainer would use.\n\nFAQ\n\n- **Do I need a remote service?** No. CGC works locally with embedded backends or with external graph databases when you want them.\n- **Is it only for one language?** No. It supports a broad mix of mainstream languages and keeps expanding.\n- **Can I use it with an AI assistant?** Yes. That is one of the main reasons the MCP server exists.\n\n---\n\nProject Details\n- **Version:** 0.5.1\n- **Authors:** Shashank Shekhar Singh \n- **License:** MIT License (See LICENSE for details)\n- **Website:** CodeGraphContext\n\n---\n\n👨\u200d💻 Maintainer\n**CodeGraphContext** is created and actively maintained by:\n\n**Shashank Shekhar Singh** \n- 📧 Email: shashankshekharsingh1205@gmail.com\n- 🐙 GitHub: @Shashankss1205\n- 🔗 LinkedIn: Shashank Shekhar Singh\n- 🌐 Website: codegraphcontext.vercel.app\n\n*Contributions and feedback are always welcome! Feel free to reach out for questions, suggestions, or collaboration opportunities.*\n\n---\n\nStar History\nStar History Chart ↗\n \n\n---\n\nFeatures\n- **Code Indexing:** Analyzes code and builds a knowledge graph of its components.\n- **Relationship Analysis:** Query for callers, callees, class hierarchies, call chains and more.\n- **Pre-indexed Bundles:** Load famous repositories instantly with .cgc bundles - no indexing required! (Learn more)\n- **Live File Watching:** Watch directories for changes and automatically update the graph in real-time (cgc watch).\n- **Interactive Setup:** A user-friendly command-line wizard for easy setup.\n- **Dual Mode:** Works as a standalone **CLI toolkit** for developers and as an **MCP server** for AI agents.\n- **Multi-Language Support:** Full support for 23 programming languages.\n- **Flexible Database Backend:** FalkorDB Lite (Default), KuzuDB, LadybugDB, FalkorDB Remote, Nornic DB, or Neo4j (all platforms via Docker/native).\n\n---\n\n🏗️ Architecture & Workflow\n\nCodeGraphContext parses your source code and builds a comprehensive knowledge graph. This graph can be queried directly via the CLI toolkit or exposed to AI assistants through the MCP server.\n\n \n\n---\n\nSupported Programming Languages\n\nCodeGraphContext provides comprehensive parsing and analysis for the following languages:\n\n| | Language | | Language | | Language |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 🐍 | **Python** | 📜 | **JavaScript** | 🔷 | **TypeScript** |\n| ☕ | **Java** | 🔵 | **C** | ➕ | **C++** |\n| #️⃣ | **C#** | 🐹 | **Go** | 🦀 | **Rust** |\n| 💎 | **Ruby** | 🐘 | **PHP** | 🍎 | **Swift** |\n| 🎨 | **Kotlin** | 🎯 | **Dart** | 🐪 | **Perl** |\n| 🌙 | **Lua** | 🚀 | **Scala** | λ | **Haskell** |\n| 💧 | **Elixir** | 📜 | **Emacs Lisp (elisp)** | 🌐 | **HTML** |\n| 🎨 | **CSS** | ⚛️ | **TSX** | ⛓️ | **Solidity** |\n| 🏗️ | **HCL (Terraform/Terragrunt)** | | | | |\n\nEach language parser extracts functions, classes, methods, parameters, inheritance relationships, function calls, and imports to build a comprehensive code graph.\n\n**HCL notes:** .tf, .tfvars and .hcl cover Terraform, OpenTofu and Terragrunt. HCL declares\nrather than calls, so it contributes blocks, variables and cross-configuration imports but no\nfunction calls. See docs/docs/contributing_languages.md § HCL.\n\n**Solidity notes:** .sol uses Tree-sitter via tree-sitter-language-pack (no SCIP). Supports Foundry remappings, modifier invocations, using Lib for T, and emit / custom-error revert as CALLS. See docs/docs/contributing_languages.md § Solidity.\n\n**Kotlin notes:** Annotations are recorded on functions and classes as decorators, retaining\ntheir arguments (@Preview(showBackground = true)). This makes find_dead_code\'s\nexclude_decorated_with usable on Kotlin and Android codebases — for example excluding\nComposable, Preview, Test and Provides. That filter matches on functions, so class-level\nannotations do not affect it. Annotations on interfaces, objects, constructors, parameters and\nproperties are not yet recorded.\n\n---\n\nDatabase Options\n\nCodeGraphContext supports multiple graph database backends to suit your environment:\n\n| Feature | KuzuDB | LadybugDB | FalkorDB Lite | Neo4j / Nornic DB |\n| :--- | :--- | :--- | :--- | :--- |\n| **Typical default** | Cross-platform fallback when FalkorDB Lite is unavailable | Optional embedded backend | **Default on Unix** (Python 3.12+, when falkordblite is installed) | When explicitly configured via cgc config db |\n| **Setup** | Zero-config / Embedded | Zero-config / Embedded | Zero-config / In-process | Docker / External |\n| **Platform** | **All (Windows Native, macOS, Linux)** | **All (Windows Native, macOS, Linux)** | Unix-only (Linux/macOS/WSL) | All Platforms |\n| **Use Case** | Desktop, IDE, Local development | Custom research projects | Specialized Unix development | Enterprise, Massive graphs |\n| **Requirement**| pip install kuzu | pip install ladybug | pip install falkordblite | Neo4j Server / Docker / Nornic Cloud |\n| **Speed** | ⚡ Extremely Fast | ⚡ Fast | 🚀 Scalable | 🌐 Network-dependent |\n| **Persistence**| Yes (to disk) | Yes (to disk) | Yes (to disk) | Yes (server-side) |\n\nEmbedded KuzuDB/LadybugDB default to a **4 GiB** buffer pool (CGC_EMBEDDED_BUFFER_POOL_MB). Set that env var to another MiB value, or 0 for the library default (~80% of system RAM).\n\n---\n\nSCIP indexing (optional)\n\nWhen SCIP_INDEXER=true in your CGC config (~/.codegraphcontext/.env), some languages use external **SCIP** indexers for more accurate calls and inheritance than Tree-sitter heuristics alone.\n\n**C and C++** use **scip-clang**, which requires a **compile_commands.json** file (a JSON compilation database): one entry per translation unit with the real compiler command (include paths, -D defines, -std, etc.). Without it, scip-clang cannot run; CGC logs a warning and **falls back to Tree-sitter** for that repo. Typical ways to produce the file: **CMake** with -DCMAKE_EXPORT_COMPILE_COMMANDS=ON, or wrap your real', 'readme_raw_len': 18076, 'one_line_zh': '将本地代码索引进图数据库，为 AI 助手提供上下文，并配有图形化代码浏览', 'one_line_plain': 'Index local code into a graph database to give AI assistants context, with graphical code…'}, {'id': 5867, 'key': 'pipeshub-ai-pipeshub-ai', 'kind': 'repo', 'name': 'pipeshub-ai/pipeshub-ai', 'one_line': 'The open-source context layer for AI agents', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '["开源项目"]', 'desc_raw': "The open-source context layer for AI agents. PipesHub turns your company's knowledge (Slack, Drive, Jira, GitHub, Microsoft 365 and 40+ connectors) into a permission-aware workspace that agents can search, grep, navigate and cite. MCP, SDKs and built-in agents. Self-hosted.", 'url': 'https://github.com/pipeshub-ai/pipeshub-ai', 'source': 'github', 'source_url': 'https://github.com/pipeshub-ai/pipeshub-ai', 'downloads': None, 'stars': 3840, 'installs': None, 'mentions': 2, 'score': 210.67, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-14', 'last_seen': '2026-10-19', 'times_seen': 7, 'icon': 'https://github.com/pipeshub-ai.png?size=64', 'official_desc': "The open-source context layer for AI agents. PipesHub turns your company's knowledge (Slack, Drive, Jira, GitHub, Microsoft 365 and 40+ connectors) into a permission-aware workspace that agents can search, grep, navigate and cite. MCP, SDKs and built-in agents. Self-hosted.", 'stars_own': 3840, 'stars_creator': None, 'stars_own_repo': 'pipeshub-ai/pipeshub-ai', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'The Open-Source Workplace AI Platform \n\n \n Website ·\n Docs ·\n Discord ·\n Roadmap \n \n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n**Translations:** Français · Deutsch · 简体中文 · 日本語 · Русский · עברית · 한국어 · Español · Português · Türkçe · Tiếng Việt · Italiano\n\n \n\n PipesHub - Explainable & Extensible \n\n PipesHub is an open-source platform for connecting AI applications to the knowledge stored across your company\'s business systems.\n\nConnect Slack, Google Drive, GitHub, Microsoft 365, Notion and 50+ other systems. Search across them and get answers with citations, filtered by what each person is allowed to see — or give that same context to your own agents, workflows and MCP clients.\n\n> [!TIP]\n> Deploy with a single command:\n> \n\n \n \n \n ▶ Watch the 2½-minute demo : Claude Code answering from Jira, Slack, GitHub and Drive, with each person\'s permissions\n \n\nWhat can you build with PipesHub?\n\nDon\'t just use PipesHub — build on it.\n\n**Give Claude Code, Cursor, or Codex secure access to your company\'s knowledge →**\nAbout ten minutes once PipesHub is running with data indexed. Mint a Personal Access Token (no admin needed), connect your assistant (one command for Claude Code, one config file for Cursor or Codex), and ask *"why was the retry logic in the billing worker changed?"* — it answers from the incident postmortem, the pull request, the chat thread, and the design doc, each cited, and only if you\'re allowed to see them.\n\nWant the same retrieval inside your own code, or behind a search box for your team? The SDK starter and search example cover both. Built something? Show us.\n\nPipesHub in Action\n\nConnectors\n \n\nCitations\n \n\nAll Records\n \n\nKnowledge Search\n \n\nFeatures\n\n**Answers you can trust**\n\n- 📝 **Explainable Answers:** PipesHub delivers grounded answers with precise block citations to the original documents.\n- 🔒 **Permission-Aware Search:** Enforces source-level access controls so users only see what they\'re authorized to.\n- 🧠 **Bring Your Own Model, Fully Self-Hostable:** Any LLM provider, deployed in your VPC — data never leaves your infrastructure.\n\n**Connected to your systems**\n\n- 🔌 **Enterprise Connectors:** 50+ connectors with real-time and scheduled indexing out of the box.\n- 🕸️ **Knowledge Graph Retrieval:** Graph-backed retrieval that captures relationships across enterprise data.\n- 🎙️ **Multimodal Support:** Image, diagram, and scanned-file understanding plus voice-based interaction.\n\n**What you can build**\n\n- 🔍 **Unified Search, Deep Research, and Agents:** Search, Q&A, deep research, web search, and AI agents on one context layer.\n- 🤖 **No-Code Agents and Actions:** Build agents visually and execute actions across enterprise tools.\n- 📊 **Artifacts and Code Execution:** Generate reports, charts, and dashboards in a safe execution sandbox.\n- 🛠️ **Developer-First and Extensible:** APIs, SDKs, MCP tools, custom connectors, and independently scalable services.\n\nPipesHub Cloud\n\nPrefer a fully managed PipesHub without running your own infrastructure? PipesHub Cloud is coming soon.\n\n👉 **Join the Cloud Waitlist** to get early access.\n\nConnectors\n\n \n \n \n\n🚀 Deployment Guide\n\nPipesHub can be run locally or deployed on any server using Docker Compose. The interactive installer handles all configuration — including secrets, graph DB, broker, and image tag selection — and generates a .env for you.\n\n> **HTTPS on cloud servers:** If you deploy PipesHub on a cloud server, use an HTTPS endpoint. Browsers block certain requests over plain HTTP. Use Cloudflare, Nginx, or Traefik to terminate TLS. A white screen after HTTP-only deployment is typically caused by this restriction.\n\n---\n\n⚡ Quickstart (Recommended)\n\nRequires Docker with Compose v2. One command:\n\n \n\nThis downloads the deployment files for the latest release into ./pipeshub and\nlaunches the interactive installer. Open **http://localhost:3000** once it\nfinishes.\n\n> **Prefer to read before running?** Download and inspect the script first:\n>\n> \n\nThe installer will:\n- Check Docker, RAM, and disk prerequisites\n- Ask whether you want a **slim** or **full** deployment\n- Let you optionally customise the graph DB, message broker, and KV store\n- Generate randomised secrets and write a .env file\n- Pull images and start the stack\n- Wait for PipesHub to become healthy, verify it is reachable, and print the URL\n\n🛠️ From a cloned repository (developers)\n\nTo build from source, contribute, or pin the installer to your checkout:\n\n \n\nBuilding local images from source requires this cloned-repo path (./install.sh --build);\nthe one-command installer above always uses prebuilt images.\n\nInstaller options\n\n| Flag | Description |\n|------|-------------|\n| -y / --yes | Accept all defaults; skip interactive prompts (CI-friendly) |\n| --version TAG | Pin a specific image tag, e.g. --version 0.7.0 |\n| --reconfigure | Re-run the wizard and overwrite an existing .env |\n| --print-env-only | Write .env and print the compose command without starting containers |\n\n> **Advanced options:** CI environment variables, slim vs. full deployment types, manual Compose profile usage, and local source builds are covered in Advanced Deployment Options.\n\nBuild with PipesHub\n\nThe built-in search experience is one way to use PipesHub. The same connected,\npermission-filtered context is available to your own agents and applications —\nover MCP for any compatible client, or through the SDKs when you are calling it\nfrom your own code.\n\nAn agent connects as a specific person rather than as the application, so it\nretrieves exactly what that person is allowed to see. Access is resolved when\nthe query runs, against the source system\'s own permissions, instead of being\napproximated at build time.\n\nStep-by-step tutorials for the most common builds — an MCP for your coding\nassistant, private enterprise search, and SDK starters — live in\n**pipeshub-ai/examples**. The\nreference material for each building block is below.\n\nMCP Server\n\nUse PipesHub with any MCP-compatible client to bring your enterprise context into AI workflows. Check the README for setup and usage.\n\n**Repository:** pipeshub-ai/mcp-server\n\nConnecting an Omnigent agent\n\nFirst, mint a long-lived credential: **workspace → Developer settings →\nPersonal Access Tokens → New token**. Pick an expiry (30/90/365 days, or\nnever) — this runs as *you*, so results respect your own per-user\npermissions, unlike an OAuth app\'s client-credentials flow.\n\nThree ways to connect, from least to most setup:\n\n1. **Attach in the Omnigent web UI (fastest, no clone).** Open a session\'s\n info panel → Manage MCP Servers → add PipesHub\'s URL and an\n Authorization: Bearer header → restart the session.\n2. **Run the packaged example agent.** A ready-made agent bundle (tuned\n prompt + instructions) ships with Omnigent:\n PIPESHUB_MCP_URL=... PIPESHUB_MCP_TOKEN=... omnigent run examples/pipeshub/.\n3. **Use the connect kit** in integrations/omnigent/\n (setup.sh / run.sh) for CI, service accounts, or password/OAuth\n client-credentials auth instead of a personal token.\n\nSDKs\n\nPipesHub provides developer SDKs for Python, TypeScript, and Go to help you integrate quickly. Check the respective SDK repository README for setup and usage details.\n\n| Name | Description | Link |\n|------|-------------|------|\n| **Python SDK** | Python SDK for PipesHub | pipeshub-ai/pipeshub-sdk-python |\n| **TypeScript SDK** | TypeScript SDK for PipesHub | pipeshub-ai/pipeshub-sdk-typescript |\n| **Go SDK** | Go SDK for PipesHub | pipeshub-ai/pipeshub-sdk-go |\n\n> Need an SDK in another language? Reach out to us at developer@pipeshub.com\n\nRoadMap\n\n We ship in the open. Here\'s what\'s done and what\'s next: \n\n \n ✅ 🤖 Workplace AI agents : first-class no-code agent builder \n ✅ 🔗 MCP (Model Context Protocol) support, both server and client \n ✅ 🧰 Developers SDKs \n ✅ 🔍 Code search across GitHub and GitLab \n ⬜ 👤 Personalized search based on team, role, and history \n ✅ ☸️ Production Kubernetes deployment with HA defaults \n ⬜ 📈 PageRank-augmented relevance across the knowl', 'readme_raw_len': 12957, 'one_line_zh': '面向 AI Agent 的开源上下文层', 'one_line_plain': 'This open-source context layer helps AI agents manage information'}, {'id': 1803, 'key': 'zcaceres-markdownify-mcp', 'kind': 'mcp', 'name': 'zcaceres/markdownify-mcp', 'one_line': '📇 🏠 - Convert almost any file or web content into Markdown', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol server for converting almost anything to Markdown', 'url': 'https://github.com/zcaceres/markdownify-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/zcaceres/markdownify-mcp', 'downloads': None, 'stars': 2997, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/zcaceres.png?size=64', 'official_desc': '📇 🏠 - Convert almost any file or web content into Markdown.', 'stars_own': 2997, 'stars_creator': None, 'stars_own_repo': 'zcaceres/markdownify-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Markdownify MCP Server\n\n \n\nMarkdownify is a Model Context Protocol (MCP) server that converts various file types and web content to Markdown format. It provides a set of tools to transform PDFs, images, audio files, web pages, and more into easily readable and shareable Markdown text.\n\n \n\nFeatures\n\n- Convert multiple file types to Markdown:\n - PDF\n - Images\n - Audio (with transcription)\n - DOCX\n - XLSX\n - PPTX\n- Convert web content to Markdown:\n - YouTube video transcripts\n - Bing search results\n - General web pages\n- Retrieve existing Markdown files\n\nGetting Started\n\n1. Clone this repository\n2. Install dependencies:\n \n\n The preinstall step creates a Python virtual environment at .venv and installs markitdown[all].\n\n3. Build the project:\n \n4. Start the server:\n \n\nDevelopment\n\n- Use bun run dev to start the TypeScript compiler in watch mode\n- Modify src/server.ts to customize server behavior\n- Add or modify tools in src/tools.ts\n\nUsage with Desktop App\n\nTo integrate this server with a desktop app, add the following to your app\'s server configuration:\n\n \n\nEnvironment variables\n\nAll paths default to sensible values; override only when the defaults don\'t fit your install layout.\n\n| Variable | Default | Purpose |\n|---|---|---|\n| MARKITDOWN_PATH | /.venv/bin/markitdown, then markitdown on PATH | Absolute path to the markitdown executable. Set this when you\'ve installed markitdown system-wide (e.g. pipx install "markitdown[pdf]") instead of using the bundled venv. |\n| REPOMIX_PATH | /node_modules/.bin/repomix, then repomix on PATH | Absolute path to the repomix executable used by git-repo-to-markdown. |\n| MD_ALLOWED_PATHS | unset (unrestricted) | Path-delimiter-separated list (: on POSIX, ; on Windows) of directories the server is allowed to read. When set, all file-input tools (pdf-to-markdown, get-markdown-file, etc.) reject paths outside these directories. |\n| MD_SHARE_DIR | unset | Deprecated alias for MD_ALLOWED_PATHS (single directory). Still honored for backward compatibility. |\n\nUsage with Docker\n\nBuild and run:\n \n\nNotes for the Docker MCP catalog (mcp/markdownify):\n- Mount any host directories you want the server to read into the container, then pass the **container** paths to the tools (e.g. /data/foo.pdf, not /Users/you/Documents/foo.pdf).\n- Set MD_ALLOWED_PATHS to the colon-separated list of mounted directories so the server enforces a read boundary that matches the bind mount.\n- The published Docker image installs markitdown[pdf] only — audio transcription and image OCR (audio-to-markdown, image-to-markdown) require the [all] extras and will fail in the slim image. Use the local install (bun install) for the full feature set.\n\nAvailable Tools\n\n- youtube-to-markdown: Convert YouTube videos to Markdown\n- pdf-to-markdown: Convert PDF files to Markdown\n- bing-search-to-markdown: Convert Bing search results to Markdown\n- webpage-to-markdown: Convert web pages to Markdown\n- image-to-markdown: Convert images to Markdown with metadata\n- audio-to-markdown: Convert audio files to Markdown with transcription\n- docx-to-markdown: Convert DOCX files to Markdown\n- xlsx-to-markdown: Convert XLSX files to Markdown\n- pptx-to-markdown: Convert PPTX files to Markdown\n- get-markdown-file: Retrieve an existing Markdown file. File extension must end with: *.md, *.markdown.\n\n OPTIONAL: set MD_ALLOWED_PATHS to restrict every file-input tool to a list of directories, e.g. MD_ALLOWED_PATHS=/data/in:/data/out bun start.\n\nContributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\nLicense\n\nThis project is licensed under the MIT License - see the LICENSE file for details.', 'readme_raw_len': 3639, 'one_line_zh': '将几乎所有文件或网页内容转换为 Markdown', 'one_line_plain': 'Convert almost any file or web content into Markdown'}, {'id': 2551, 'key': 'bitbonsai-mcp-obsidian', 'kind': 'mcp', 'name': 'bitbonsai/mcp-obsidian', 'one_line': '📇 🏠 🍎 🪟 🐧 - Safe read/write access to Obsidian vaults: search, batch operations, tag management and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/bitbonsai/mcpvault', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/bitbonsai/mcp-obsidian', 'downloads': None, 'stars': 1695, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/bitbonsai.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Safe read/write access to Obsidian vaults: search, batch operations, tag management and frontmatter handling.', 'stars_own': 1695, 'stars_creator': None, 'stars_own_repo': 'bitbonsai/mcpvault', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'MCPVault\n\n \n\nA local MCP server that lets compatible clients read, search, and edit notes in an Obsidian vault. MCPVault works directly with vault files, restricts file operations to the configured vault root, and preserves formatting for unchanged frontmatter fields.\n\n \n \nhttps://mcpvault.org\n\nChangelog\n\n \n\n \n\n \n\n \n\nSupported clients\n\nConfiguration examples are available for Claude Desktop, Claude Code, ChatGPT Desktop (Enterprise+), OpenCode, Gemini CLI, OpenAI Codex, IntelliJ IDEA 2025.1+, Cursor, Windsurf, and Ontheia. Other clients can use MCPVault if they support local stdio MCP servers.\n\nhttps://github.com/user-attachments/assets/657ac4c6-1cd2-4cc3-829f-fd095a32f71c\n\nQuick start\n\n1. **Install Node.js runtime:**\n\n \n\n2. **Test the server:**\n\n If using the published package:\n\n \n\n3. **Configure your AI client:**\n\n **Claude Desktop** - Copy this to claude_desktop_config.json:\n\n \n\n **Claude Code** - Copy this to ~/.claude.json:\n\n \n\n **OpenCode** - Copy this to ~/.config/opencode/opencode.json\n\n \n\n Replace /path/to/your/vault with your actual Obsidian vault path.\n\n For other platforms, see detailed configuration guides below.\n\n4. **Test with your AI:**\n - "List files in my Obsidian vault"\n - "Read my note called \'project-ideas.md\'"\n - "Create a new note with today\'s date"\n\nTo verify the connection, ask your client to list MCPVault tools or read a known note.\n\nHow it connects\n\nAn MCP client starts MCPVault as a local stdio process and passes the vault path. MCPVault exposes the same tools to each supported client, so the server is not tied to one AI provider. Obsidian does not need to be running, and no Obsidian plugin is required.\n\nFeatures\n\n- AST-aware frontmatter updates preserve formatting for unchanged YAML fields.\n- Path checks block traversal, symlink escapes, dotfiles, .obsidian, .git, and node_modules.\n- Eighteen MCP tools cover note and file operations:\n - File operations: read_note, write_note, patch_note, delete_note, move_note, move_file\n - Partial reads: get_note_outline, read_note_lines\n - Directory and batch reads: list_directory, read_multiple_notes\n - Search: search_notes with multi-word matching and BM25 reranking\n - Metadata and tags: get_frontmatter, update_frontmatter, get_notes_info, get_vault_stats, manage_tags, list_all_tags\n - Wiki links: wiki_link resolves names and returns alternative paths when a name is ambiguous\n- write_note supports overwrite, append, and prepend modes.\n- delete_note and move_file require matching confirmation paths.\n- Path arguments are trimmed before validation.\n- Search and batch tools return compact fields by default; set prettyPrint: true for expanded output.\n- The package exports TypeScript declarations and public types.\n- MCPVault requires no Obsidian plugin.\n\nPrerequisites\n\n- Node.js runtime (v20.0.0 or later)\n- An Obsidian vault (local directory with .md, .markdown, .txt, .base, or .canvas files)\n- MCP-compatible AI client (Claude Desktop, ChatGPT Desktop, Claude Code, etc.)\n\nInstallation\n\nFor end users\n\nnpx downloads and runs the package:\n\n \n\nIf you omit the vault path, the server uses your current working directory as the vault root.\n\nFor developers\n\n1. Clone this repository\n2. Use the correct Node.js version:\n\n \n\n3. Install dependencies with npm:\n\n \n\n4. Test locally with MCP inspector:\n\n \n\nUse MCP Inspector to test the server before adding it to a client:\n\n \n\nUsage\n\nRunning the Server\n\n**End users:**\n\n \n\n**Developers:**\n\n \n\nAI Client Configuration\n\nClaude Desktop\n\nAdd to your Claude Desktop configuration file:\n\n**Single Vault:**\n\n \n\n**Multiple Vaults:**\n\n \n\n**Read-only mode:**\n\nAdd --read-only after the vault path to expose only read tools. Mutating tools are omitted from discovery and rejected if called directly.\n\n \n\nThe CLI also accepts --read-only true and --read-only=true for configuration systems that require explicit values. Omit the option, or set it to false, to keep normal read/write access.\n\n**Configuration File Locations:**\n\n- **macOS:** ~/Library/Application Support/Claude/claude_desktop_config.json\n- **Windows:** C:\\Users\\{username}\\AppData\\Roaming\\Claude\\claude_desktop_config.json\n- **Linux:** ~/.config/Claude/claude_desktop_config.json\n\n_You can also access this through Claude Desktop → Settings → Developer → Edit Config_\n\nChatGPT Desktop\n\n**Requirements:** ChatGPT Enterprise, Education, or Team subscription (not available for individual Plus users)\n\nChatGPT uses MCP through Deep Research and developer mode. Configuration is done through the ChatGPT interface:\n\n1. Access ChatGPT developer mode (beta feature)\n2. Configure MCP servers through the built-in MCP client\n3. Create custom connectors for your organization\n\n_Note: ChatGPT Desktop\'s MCP integration is currently limited to enterprise subscriptions and uses a different setup process than file-based configuration._\n\nClaude Code\n\nClaude Code uses .claude.json configuration file:\n\n**User-scoped (recommended):** Edit ~/.claude.json:\n\n \n\n**Project-scoped:** Edit .claude.json in your project or add to the projects section:\n\n \n\n**Using Claude Code CLI:**\n\n \n\nGoose Desktop\n\nOn Goose Desktop settings, click **Add custom extension**, and on the command field add:\n\n \n\nOther MCP-Compatible Clients (2025)\n\n**Confirmed MCP Support:**\n\n- **IntelliJ IDEA 2025.1+** - Native MCP client support\n- **Cursor IDE** - Built-in MCP compatibility\n- **Windsurf IDE** - Full MCP integration\n- **Ontheia** - Self-hosted, open-source AI agent platform\n- **Zed, Replit, Codeium, Sourcegraph** - In development\n- **Microsoft Copilot Studio** - Native MCP support with one-click server connections\n\nMost modern MCP clients use similar JSON configuration patterns. Refer to your specific client\'s documentation for exact setup instructions.\n\nExamples\n\nAsk your AI assistant about your notes:\n\n- "What files are in my Obsidian vault?"\n- "Read my note called \'project-ideas.md\'"\n- "Show me all notes with \'AI\' in the title"\n\nHave your AI assistant help with note management:\n\n- "Create a new note called \'meeting-notes.md\' with today\'s date in the frontmatter"\n- "Append today\'s journal entry to my daily note"\n- "Prepend an urgent task to my todo list"\n- "Add the tags \'project\' and \'urgent\' to my task note"\n- "List all tags in my research note"\n- "Remove the \'draft\' tag from my completed article"\n- "List all markdown files in my \'Projects\' folder"\n- "Delete the old draft note \'draft-ideas.md\' (with confirmation)"\n\nExample workflows\n\n- "Summarize my research notes tagged with \'machine-learning\' from the last month"\n- "Update the status in my project notes to \'completed\' and add today\'s date"\n- "Find notes that mention \'API design\' and draft a guide from them"\n- "Review my untagged notes and suggest tags based on their content"\n\nTroubleshooting\n\nCommon Issues\n\n"command not found: npx"\n\n- **Solution:** Install Node.js runtime from nodejs.org\n- **Alternative:** Use global install: npm install -g @bitbonsai/mcpvault\n\n"File not found" when paths look correct\n\n- **Cause:** The server is using the wrong vault root\n- **Solution:** Either run the command from your vault directory or pass the vault path explicitly\n\n"Permission denied" errors\n\n- **Cause:** Insufficient file system permissions\n- **Solution:** Ensure the vault directory is readable/writable by your user\n\n"Path traversal not allowed"\n\n- **Cause:** Trying to access files outside the vault\n- **Solution:** All file paths must be relative to the vault root\n\nAI client not recognizing the server\n\n1. Check the configuration file path is correct for your OS\n2. Ensure JSON syntax is valid (use a JSON validator)\n3. Restart your AI client after configuration changes\n4. Check your AI client\'s logs for error messages\n5. Verify your AI client supports MCP (Model Context Protocol)\n\n".obsidian files still showing up"\n\n- **Expected:** The path filter automatically excludes .obsidian/** patterns\n- **If still seeing them:** The filter is working as designed for security\n\nDebug Mode\n\nRun with error logging:\n\n \n\nGetting Help\n\n- Open an iss', 'readme_raw_len': 13696, 'one_line_zh': '安全读写 Obsidian 库：检索、批量操作与标签管理等。', 'one_line_plain': 'Safe read/write access to Obsidian vaults: search, batch operations, and tag management'}, {'id': 756, 'key': 'ezyang-codemcp', 'kind': 'mcp', 'name': 'ezyang/codemcp', 'one_line': '🐍 🏠 - Coding agent with basic read, write and command line tools', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Coding assistant MCP for Claude Desktop', 'url': 'https://github.com/ezyang/codemcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ezyang/codemcp', 'downloads': None, 'stars': 1606, 'installs': None, 'mentions': 22, 'score': 2203.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ezyang.png?size=64', 'official_desc': '🐍 🏠 - Coding agent with basic read, write and command line tools.', 'stars_own': 1606, 'stars_creator': None, 'stars_own_repo': 'ezyang/codemcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'NOTICE: claude code is available with Anthropic\'s $20/mo subscription, so I consider\ncodemcp fully obsolete. However, there are some good design ideas (especially around\nthe Git-versioning scheme) that I eventually want to port into the current generation\nof agentic coding clis.\n\ncodemcp\n\nMake Claude Desktop a pair programming assistant by installing codemcp. With\nit, you can directly ask Claude to implement features, fix bugs and do\nrefactors on a codebase on your computer; Claude will directly edit files and\nrun tests. Say goodbye to copying code in and out of Claude\'s chat window!\n\n \n\ncodemcp offers similar functionality to other AI coding software (Claude Code,\nCursor, Cline, Aider), but it occupies a unique point in the design space:\n\n1. It\'s intended to be used with **Claude Pro**, Anthropic\'s $20/mo\n subscription offering. I like paying for my usage with a subscription plan\n because it means **zero marginal cost** for agent actions; no more feeling\n bad that you wasted five bucks on a changeset that doesn\'t work.\n\n Note that if you have Claude Max ($100/mo), Claude Code can also be used\n with subscription based pricing. The value proposition for codemcp is\n murkier in this case (and it is definitely inferior to Claude Code in some\n respects), but you can still use codemcp with Claude Max if you prefer some\n of the other UI decisions it makes. (Also, it\'s open source, so you can\n change it if you don\'t like it, unlike Claude Code!)\n\n2. It\'s built around **auto-accept by default**. I want my agent to get as\n far as it can without my supervision, so I can review everything in one go at\n the end. There are two key things that codemcp does differently than most\n coding agents: we **forbid unrestricted shell**, instead requiring you to\n predeclare commands the agent can use in codemcp.toml, and we **Git\n version all LLM edits**, so you can roll back agent changes on a\n fine-grained basis and don\'t have to worry about forgetting to commit\n changes.\n\n3. It\'s **IDE agnostic**: you ask Claude to make changes, it makes them, and\n then you can use your favorite IDE setup to review the changes and make\n further edits. I use vim as my daily driver editor, and coding environments\n that require VSCode or a specific editor are a turn off for me.\n\nIMPORTANT: For master users - Major changes for token efficiency\n\nTo improve codemcp\'s token efficiency, on master I am in the process of\nchanging codemcp back into a multi-tool tool (instead of a single tool whose\ninstructions are blatted into chat when you InitProject). This means you have\nto manually approve tool use. Because tool use approval is persistent across\nmultiple chats, I think this is a reasonable tradeoff to make, but if you\nreally don\'t like, file a bug at\nrefined-claude browser\nextension for supporting auto-approve tool use.\n\nInstallation\n\nI recommend this specific way of installing and using codemcp:\n\n1. Install uv and install git, if they are not installed already.\n\n2. Install claude-mcp on your browser.\n This enables you to connect to SSE MCP servers directly from the website,\n which means you don\'t need to use Claude Desktop and can easily have\n multiple chat windows going in parallel. We expect this extension should\n be soon obsoleted by the rollout of\n Integrations. At time of\n writing, however, Integrations have not yet arrived for Claude Pro subscribers.\n\n3. Run codemcp using uvx --from git+https://github.com/ezyang/codemcp@prod codemcp serve.\n You can add --port 1234 if you need it to listen on a non-standard port.\n\n Pro tip: if you like to live dangerously, you can change prod to main. If\n you want to pin to a specific release, replace it with 0.3.0 or similar.\n\n Pro tip: you can run codemcp remotely! If you use\n Tailscale and trust all devices on your Tailnet,\n you can do this securely by passing --host 100.101.102.103 (replace the\n IP with the Tailscale IP address of your node. This IP typically lives in\n the 100.64.0.0/10 range.) **WARNING:** Anyone with access to this MCP can perform\n arbitrary code execution on your computer, it is **EXTREMELY** unlikely you want to\n bind to 0.0.0.0.\n\n4. Configure claude-mcp with URL: http://127.0.0.1:8000/sse (replace the port if needed.)\n\n5. Unfortunately, the web UI inconsistently displays the hammer icon. However, you can verify\n that the MCP server is working by looking for "[MCP codemcp] SSE connection opened" in the\n Console, or by asking Claude what tools it has available (it should say\n tools from codemcp are available.)\n\nIf you prefer to use Claude Desktop or have unusual needs, check out INSTALL.md for\ninstallation instructions for a variety of non-standard situations.\n\nUsage\n\nFirst, you must create a codemcp.toml file in the Git repository checkout\nyou want to work on. If you want the agent to be able to do things like run\nyour formatter or run tests, add the commands to execute them in the commands\nsection (note: these commands need to appropriately setup any virtual\nenvironment they need):\n\n \n\nThe format command is special; it is always run after every file edit.\n\nNext, in Claude Desktop, we recommend creating a Project and putting this in\nthe Project Instructions:\n\n \n\nWhere $PROJECT_DIR is the path to the project you want to work on.\n\nThen chat with Claude about what changes you want to make to the project.\nEvery time codemcp makes a change to your code, it will generate a commit.\n\nTo see some sample transcripts using this tool, check out:\n\n- Implement a new feature\n- Fix failing tests\n- Do a refactor\n\ncodemcp will generate a commit per chat and amend it as it is working on your feature.\n\nPhilosophy\n\n- When you get rate limited, take the time to do something else (review\n Claude\'s code, review someone else\'s code, make plans, do some meetings)\n\n- This is *not* an autonomous agent. At minimum, you have to intervene after\n every chat to review the changes and request the next change. While you\n *can* ask for a long list of things to be done in a single chat, you will\n likely hit Claude Desktop\'s output limit and have to manually "continue" the\n agent anyway. Embrace it, and use the interruptions to make sure Claude is\n doing the right thing.\n\n- When Claude goes off the rails, it costs you time rather than dollars.\n Behave accordingly: if time is the bottleneck, watch Claude\'s incremental\n output carefully.\n\nConfiguration\n\nHere are all the config options supported by codemcp.toml:\n\n \n\nThe project_prompt will be loaded when you initialize the project in chats.\n\nThe commands section allows you to configure commands for specific tools. The\nnames are told to the LLM, who will decide when it wants to run them. You can add\ninstructions how to use tools in the project_prompt; we also support a more verbose\nsyntax where you can give specific instructions on a tool-by-tool basis:\n\n \n\nTroubleshooting\n\nTo run the server with inspector, use:\n\n \n\nLogs are written to ~/.codemcp/codemcp.log. The log level can be set in a global configuration file at ~/.codemcprc:\n\n \n\nLogging is not configurable on a per project basis, but this shouldn\'t matter\nmuch because it\'s difficult to use Claude Desktop in parallel on multiple\nprojects anyway.\n\nContributing\n\nSee CONTRIBUTING.md.', 'readme_raw_len': 7198, 'one_line_zh': '具备基础读写与命令行工具的编码 Agent', 'one_line_plain': 'Write, read, and run basic command-line tools as a coding agent'}, {'id': 3458, 'key': 'nickclyde-duckduckgo-mcp-server', 'kind': 'mcp', 'name': 'nickclyde/duckduckgo-mcp-server', 'one_line': '🐍 ☁️ - Web search using DuckDuckGo, with content fetching and parsing', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol (MCP) server that provides web search capabilities through DuckDuckGo, with additional features for content fetching and parsing.', 'url': 'https://github.com/nickclyde/duckduckgo-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/nickclyde/duckduckgo-mcp-server', 'downloads': None, 'stars': 1546, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/nickclyde.png?size=64', 'official_desc': '🐍 ☁️ - Web search using DuckDuckGo, with content fetching and parsing.', 'stars_own': 1546, 'stars_creator': None, 'stars_own_repo': 'nickclyde/duckduckgo-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'DuckDuckGo Search MCP Server\n\n \n \n \n\nA Model Context Protocol (MCP) server that provides web search capabilities through DuckDuckGo, with additional features for content fetching and parsing.\n\nQuick Start\n\n \n\nFeatures\n\n- **Web Search**: Search DuckDuckGo with advanced rate limiting and result formatting\n- **Content Fetching**: Retrieve and parse webpage content with intelligent text extraction\n- **Long URL Shortening**: Over-long result URLs become short ref:// tokens that fetch_content accepts directly, saving context\n- **Rate Limiting**: Built-in protection against rate limits for both search and content fetching\n- **Error Handling**: Comprehensive error handling and logging\n- **LLM-Friendly Output**: Results formatted specifically for large language model consumption\n\nInstallation\n\nInstall from PyPI using uv:\n\n \n\nUsage\n\nRunning with Claude Desktop\n\n1. Download Claude Desktop\n2. Create or edit your Claude Desktop configuration:\n - On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n - On Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n\nAdd the following configuration:\n\n**Basic Configuration (No SafeSearch, No Default Region):**\n \n\n**With SafeSearch and Region Configuration:**\n \n\n**Configuration Options:**\n- DDG_SAFE_SEARCH: SafeSearch filtering level (optional)\n - STRICT: Maximum content filtering (kp=1)\n - MODERATE: Balanced filtering (kp=-1, default if not specified)\n - OFF: No content filtering (kp=-2)\n- DDG_REGION: Default region/language code (optional, examples below)\n - us-en: United States (English)\n - cn-zh: China (Chinese)\n - jp-ja: Japan (Japanese)\n - wt-wt: No specific region\n - Leave empty for DuckDuckGo\'s default behavior\n- DDG_CA_CERTS: Path to a PEM CA bundle used to verify TLS certificates on outbound requests (optional). Needed behind TLS-intercepting proxies — see Running behind a TLS-intercepting proxy.\n- DDG_RATE_LIMIT_STRATEGY: sliding (default, historical 60s window) or token_bucket (burst, then smooth).\n- DDG_SEARCH_RPM: Search requests per minute (default: 30).\n- DDG_FETCH_RPM: Global fetch_content requests per minute (default: 20).\n- DDG_FETCH_HOST_RPM: Optional per-host fetch cap (default: 0, off). Set a positive number to enable.\n- DDG_CACHE_TTL: Seconds to keep a parsed page in the in-memory fetch_content cache (default: 300). Paginated reads of the same URL reuse one download. Set 0 to disable.\n- DDG_CACHE_MAX_ENTRIES: Maximum pages kept in that cache (default: 64). Least-recently-used eviction. Set 0 to disable.\n- DDG_PARSE_MODE: Default fetch_content extractor (text, main, or markdown). Default is text (historical flattened page). Per-call parse_mode overrides this.\n- DDG_REF_URL_THRESHOLD: Search-result URLs longer than this many characters are replaced with short ref:// tokens (default: 120). Set 0 to always show full URLs. Also --ref-url-threshold.\n\n3. Restart Claude Desktop\n\nRunning with Claude Code\n\n1. Download Claude Code\n2. Ensure uvenv is installed and the uvx command is available\n3. Add the MCP server: claude mcp add ddg-search uvx duckduckgo-mcp-server\n\nRunning with SSE or Streamable HTTP\n\nThe server supports alternative transports for use with other MCP clients:\n\n \n\nThe default transport is stdio, which is used by Claude Desktop and Claude Code.\n\nWhen running with sse or streamable-http, override the default bind address (127.0.0.1:8000) with the --host and --port flags:\n\n \n\nRunning behind a reverse proxy or in Docker\n\nThe MCP SDK enables DNS-rebinding protection for the HTTP transports and, by default, only accepts Host/Origin headers for localhost. Behind a reverse proxy or in a container the client\'s Host header won\'t match, so requests fail with **421 Misdirected Request**.\n\nFix it by allow-listing the host(s) and origin(s) clients actually use (preferred over disabling protection). Values support host, host:port, and wildcard-port host:*:\n\n \n\nEquivalent environment variables (comma-separated) are also available: DDG_ALLOWED_HOSTS, DDG_ALLOWED_ORIGINS.\n\nAs a last resort you can turn the check off entirely with --disable-dns-rebinding-protection (or DDG_DISABLE_DNS_REBINDING_PROTECTION=1). Prefer an allow-list — disabling protection removes a defense against DNS-rebinding attacks. When nothing is configured, the secure localhost-only default is preserved.\n\nRunning behind a TLS-intercepting proxy\n\nCorporate proxies that re-sign HTTPS traffic with their own CA (via HTTPS_PROXY) cause outbound requests to fail with certificate verification errors, because the HTTP clients don\'t trust the proxy\'s self-signed CA (and httpx no longer reads the SSL_CERT_FILE environment variable). Point the server at your proxy\'s CA bundle:\n\n \n\nOr set DDG_CA_CERTS=/path/to/proxy-ca.pem. The bundle is used by both the search and fetch_content tools, on the httpx and curl backends alike.\n\nAs a last resort, --no-ssl-verify (or DDG_SSL_VERIFY=0) disables certificate verification entirely. This exposes traffic to interception by anyone on the network path — prefer --ca-certs.\n\nBackends (bypassing bot detection)\n\nSome sites — and, as of recently, DuckDuckGo\'s own search endpoint (html.duckduckgo.com) — block the default httpx client because of its distinctive TLS fingerprint, regardless of User-Agent. Cloudflare Bot Management and similar filters key on the JA3/TLS handshake, not on headers, so html.duckduckgo.com may answer httpx with an empty **HTTP 202** page (silently yielding "no results"). An opt-in backend, curl (implemented via curl_cffi), impersonates a real Chrome browser\'s TLS handshake and passes through those checks.\n\nBoth the search tool and the fetch_content tool support these backends.\n\n**Installation:**\n\n \n\n**Backend options:**\n\n| Value | Behavior | Needs [browser] |\n| ------ | ----------------------------------------------------------------------------------------- | ----------------- |\n| httpx | Lightweight async HTTP. Default. Works on most sites. | no |\n| curl | Uses curl_cffi with Chrome 131 TLS impersonation. Passes TLS-fingerprint-based filters. | yes |\n| auto | Tries httpx first; on 403 or a Cloudflare challenge response, retries with curl. | yes |\n\n**Two ways to configure the backend:**\n\n1. **Server-wide default** via the --fetch-backend CLI flag (applies to every fetch_content call):\n\n \n\n2. **Per-call override** via the backend argument on the fetch_content tool (overrides the CLI default for that single call). The tool exposes backend in its input schema, so an MCP client can choose "httpx", "curl", or "auto" on a fetch-by-fetch basis.\n\nFor fetch_content, the default stays httpx so users who don\'t need the impersonation don\'t pay for the extra dependency.\n\nSearch backend\n\nBecause DuckDuckGo\'s search endpoint now fingerprint-blocks plain httpx, the search tool defaults to **auto**: it tries httpx first and falls back to curl when it detects a block (HTTP 202/403). The fallback only works if the [browser] extra is installed; otherwise search returns a message telling you to install it.\n\nConfigure the search backend with the --search-backend CLI flag or the DDG_SEARCH_BACKEND environment variable (auto (default) / httpx / curl):\n\n \n\nDevelopment\n\nFor local development:\n\n \n\nAvailable Tools\n\n1. Search Tool\n\n \n\nPerforms a web search on DuckDuckGo and returns formatted results.\n\n**Parameters:**\n- query: Search query string\n- max_results: Maximum number of results to return (default: 10)\n- region: (Optional) Region/language code to override the default. Leave empty to use the configured default region.\n\n**Region Code Examples:**\n- us-en: United States (English)\n- cn-zh: China (Chinese)\n- jp-ja: Japan (Japanese)\n- de-de: Germany (German)\n- fr-fr: France (French)\n- wt-wt: No specific region\n\n**Returns:**\nFormatted string containing search results with titles, URLs, and snippets.\n\n**Example Usage:**\n- Search with default settings: search("python tutorial")\n- Search with specific region: search("latest news", region="jp-ja") for Japanese news\n\n2. Content Fetching Tool\n\n \n\nFetches and parse', 'readme_raw_len': 12023, 'one_line_zh': '使用 DuckDuckGo 进行网页搜索，支持内容抓取与解析', 'one_line_plain': 'DuckDuckGo search tool lets users search the web with content fetching and parsing'}, {'id': 5749, 'key': 'skillsmp-com-creators-daymade-claude-code-skills-frontend-visual-qa', 'kind': 'skill', 'name': 'frontend-visual-qa', 'one_line': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'downloads': None, 'stars': 1452, 'installs': None, 'mentions': 5, 'score': 503.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/daymade.png?size=64', 'official_desc': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard, design-system, or Electron/native app — via real-browser/native-app journeys and a Playwright sweep. Use after UI implementation to find typography, overflow, responsive, routing, data-viz, browser-output', 'stars_own': None, 'stars_creator': 1477, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'daymade/claude-code-skills', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '审计已渲染的界面，以及 Markdown 与 Obsidian 中的图片排版。', 'one_line_plain': 'The tool audits already-rendered UI and Markdown/Obsidian image layout for web apps, decks, dashboards, and more'}, {'id': 3936, 'key': 'mbailey-voice-mcp', 'kind': 'mcp', 'name': 'mbailey/voice-mcp', 'one_line': '🐍 🏠 - Voice interaction: speech-to-text, text-to-speech and real-time voice conversations via local…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/mbailey/voicemode', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/mbailey/voice-mcp', 'downloads': None, 'stars': 1405, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/mbailey.png?size=64', 'official_desc': 'VoiceMode Natural voice conversations with Claude Code (and other MCP capable agents) VoiceMode enables natural voice conversations with Claude Code.', 'stars_own': 1405, 'stars_creator': None, 'stars_own_repo': 'mbailey/voicemode', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "VoiceMode\n\n> Natural voice conversations with Claude Code (and other MCP capable agents)\n\n \n \n \n\nVoiceMode enables natural voice conversations with Claude Code. Voice isn't about replacing typing - it's about being available when typing isn't.\n\n**Perfect for:**\n\n- Walking to your next meeting\n- Cooking while debugging\n- Giving your eyes a break after hours of screen time\n- Holding a coffee (or a dog)\n- Any moment when your hands or eyes are busy\n\nSee It In Action\n\n \n\nQuick Start\n\n**Requirements:** Computer with microphone and speakers\n\nOption 1: Claude Code Plugin (Recommended)\n\nThe fastest way for Claude Code users to get started:\n\n \n\nOption 2: Python installer package\n\nInstalls dependencies and the VoiceMode Python package.\n\n \n\nFor manual setup, see the Getting Started Guide.\n\nFeatures\n\n- **Natural conversations** - speak naturally, hear responses immediately\n- **Works offline** - optional local voice services (Whisper STT, Kokoro TTS)\n- **Low latency** - fast enough to feel like a real conversation\n- **Smart silence detection** - stops recording when you stop speaking\n- **Privacy options** - run entirely locally or use cloud services\n\nCompatibility\n\n**Platforms:** Linux, macOS, Windows (native or WSL), NixOS\n**Python:** 3.10-3.14\n\nConfiguration\n\nVoiceMode works out of the box. For customization:\n\n \n\nSee the Configuration Guide for all options.\n\nPermissions Setup (Optional)\n\nTo use VoiceMode without permission prompts, add to ~/.claude/settings.json:\n\n \n\nSee the Permissions Guide for more options.\n\nLocal Voice Services\n\nFor privacy or offline use, install local speech services:\n\n- **Whisper.cpp** - Local speech-to-text\n- **Kokoro** - Local text-to-speech with multiple voices\n\nThese provide the same API as OpenAI, so VoiceMode switches seamlessly between them.\n\nInstallation Details\n\n \n System Dependencies by Platform \n\nUbuntu/Debian\n\n \n\n**WSL2 users**: The pulseaudio packages above are required for microphone access.\n\nFedora/RHEL\n\n \n\nmacOS\n\n \n\nNixOS\n\n \n\n \n\n \n Alternative Installation Methods \n\nFrom source\n\n \n\nNixOS system-wide\n\n \n\n \n\nTroubleshooting\n\n| Problem | Solution |\n|---------|----------|\n| No microphone access | Check terminal/app permissions. WSL2 needs pulseaudio packages. |\n| UV not found | Run curl -LsSf https://astral.sh/uv/install.sh \\| sh |\n| OpenAI API error | Verify OPENAI_API_KEY is set correctly |\n| No audio output | Check system audio settings and available devices |\n\nSave Audio for Debugging\n\n \n\nDocumentation\n\n- Getting Started - Full setup guide\n- Configuration - All environment variables\n- Whisper Setup - Local speech-to-text\n- Kokoro Setup - Local text-to-speech\n- Development Setup - Contributing guide\n\nFull documentation: voicemode.dev\n\nLinks\n\n- **Website**: voicemode.dev\n- **GitHub**: github.com/mbailey/voicemode\n- **PyPI**: pypi.org/project/voice-mode\n- **YouTube**: @getvoicemode\n- **Twitter/X**: @getvoicemode\n- **Newsletter**: \n\nLicense\n\nMIT - A Failmode Project\n\n---\nmcp-name: dev.voicemode/voicemode", 'readme_raw_len': 2982, 'one_line_zh': '语音交互：语音转文字、文字转语音，并可经本地服务进行实时语音对话。', 'one_line_plain': 'Voice interaction lets users perform speech-to-text, text-to-speech and real-time voice conversations locally'}, {'id': 4192, 'key': 'caol64-wenyan-mcp', 'kind': 'mcp', 'name': 'caol64/wenyan-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Format Markdown articles with Wenyan and publish them to WeChat Official Accounts', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '文颜 MCP Server 可以让 AI 自动将 Markdown 文章排版后发布至微信公众号。', 'url': 'https://github.com/caol64/wenyan-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/caol64/wenyan-mcp', 'downloads': None, 'stars': 1345, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/caol64.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Format Markdown articles with Wenyan and publish them to WeChat Official Accounts.', 'stars_own': 1345, 'stars_creator': None, 'stars_own_repo': 'caol64/wenyan-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '文颜 MCP Server\n\n \n \n \n \n \n\n简介\n\n**文颜（Wenyan）** 是一款多平台 Markdown 排版与发布工具，支持将 Markdown 一键转换并发布至：\n\n- 微信公众号\n- 知乎\n- 今日头条\n- 以及其它内容平台（持续扩展中）\n\n文颜的目标是：**让写作者专注内容，而不是排版和平台适配**。\n\n文颜的不同版本\n\n文颜目前提供多种形态，覆盖不同使用场景：\n\n- macOS App Store 版 - MAC 桌面应用\n- 跨平台桌面版 - Windows/Linux\n- CLI 版本 - 命令行 / CI 自动化发布\n- 👉 MCP 版本 - 本项目\n\n文颜 MCP Server 是什么？\n\n简单来说，它打通了“AI 写作”与“公众号发文”的通道。\n\n基于 MCP 协议，Claude Desktop 等 AI 客户端现在可以直接调用文颜（Wenyan）的排版引擎。写完文章后，不需要再去第三方编辑器里来回复制粘贴，直接让 AI 帮你排版并塞进微信草稿箱。\n\n**核心特性：**\n\n- **绕过排版工具**：AI 生成的 Markdown 直接转成微信富文本并上传，省去中间步骤。\n- **对话式排版**：直接打字跟 AI 说“换个橙色风格主题”，样式自动生效。\n- **不出窗口完成闭环**：在同一个聊天框里，顺滑搞定“想选题 -> 写文章 -> 调排版 -> 存草稿”的所有操作。\n\n**实战演示**：\n* 让 AI 帮你管理公众号的排版和发布\n* Moraya MCP 使用案例：微信公众号全托管\n\n功能特性\n\n- 一键发布 Markdown 到微信公众号草稿箱\n- 自动上传本地图片与封面\n- 支持远程 Server 发布（绕过 IP 白名单限制）\n- 内置多套精美排版主题\n- 支持自定义主题\n- 提供标准 MCP Tool 接口\n- 支持 AI 自动调用：\n - 渲染 Markdown\n - 主题管理\n - 发布草稿\n\n快速开始\n\n**安装**\n\n \n\n**Claude Desktop 配置 (claude_desktop_config.json)：**：\n\n \n\n基本用法\n\n列出主题\n\n对AI说\n\n> 目前你可以使用哪些公众号主题？\n\nAI回复：\n\n \n\n注册主题\n\n对AI说\n\n> 帮我把这个css(https://wenyan.yuzhi.tech/manhua.css)注册为一个新的公众号主题，名称为：xiuluochang\n\nAI回复：\n\n \n\n删除主题\n\n对AI说\n\n> 把名为“xiuluochang”的公众号主题删除\n\nAI回复：\n\n \n\n发布文章\n\n对AI说\n\n> 使用phycat主题将这篇文章发布到微信公众号：./tests/publish.md\n\nAI回复：\n\n \n\n概念\n\n环境变量配置\n\n> [!IMPORTANT]\n>\n> 请确保 MCP 启动时已配置如下环境变量，否则上传接口将调用失败。\n\n- WECHAT_APP_ID\n- WECHAT_APP_SECRET\n\n微信公众号 IP 白名单\n\n> [!IMPORTANT]\n>\n> 请确保运行文颜的机器 IP 已加入微信公众号后台的 IP 白名单，否则上传接口将调用失败。\n\n配置说明文档：https://yuzhi.tech/docs/wenyan/upload\n\n文章格式\n\n为了正确上传文章，每篇 Markdown 顶部需要包含一段 frontmatter：\n\n \n\n字段说明：\n\n| 字段 | 必填 | 说明 |\n| ---------- | -- | ----------------- |\n| title | ✅ | 文章标题 |\n| cover | ❌ | 封面图片（本地路径或网络 URL） |\n| author | ❌ | 作者 |\n| source_url | ❌ | 原文链接 |\n| type | ❌ | 文章类型，设为 image 表示图片消息（小绿书） |\n| image_list | ❌ | 图片路径列表（小绿书专用，最多 20 张） |\n| need_open_comment | ❌ | 是否打开评论 |\n| only_fans_can_comment | ❌ | 是否仅粉丝可评论 |\n\n说明：\n\n* 如果未指定 cover，将自动使用正文第一张图片作为封面\n* cover 支持本地路径和网络 URL\n* type 和 image_list 用于图片消息发布\n\n**示例文章**\n\n文内图片和文章封面\n\n把文章发布到公众号之前，文颜会按照微信要求自动处理文章内的所有图片，将其上传到公众号素材库。目前文颜对于以下图片都能很好的支持：\n\n- 本地硬盘绝对路径（如：/Users/xxx/image.jpg）\n- 网络路径（如：https://example.com/image.jpg）\n- 当前文章的相对路径（如：./assets/image.png）\n\n发布图片消息（小绿书）\n\n发布图片消息与发布图文消息的区别在于 frontmatter 中需要指定图片列表。支持两种方式：\n\n方式一：使用 type: image（推荐）\n\n在 frontmatter 中设置 type: image，CLI 会自动从正文中提取所有图片：\n\n \n\n方式二：手动指定 image_list\n\n直接在 frontmatter 中列出图片路径：\n\n \n\nimage_list 最多 20 张，首张即为封面。\n\nServer 模式\n\n相较于纯本地运行的**本地模式（Stdio Mode）**，wenyan-mcp还提供了 **远程客户端模式（Client–Server Mode）**。两种模式运行效果完全一致，你可以根据运行环境和网络条件选择最合适的方式。\n\n在本地模式下，MCP 直接调用微信公众号 API 完成图片上传和草稿发布。\n\n \n\n在远程客户端模式下，MCP 作为客户端，将发布请求发送到部署在云服务器上的 Wenyan Server，由 Server 完成微信公众号 API 调用。\n\n \n\n**适用于：**\n\n* 无本地固定 IP，需频繁添加IP 白名单的用户\n* 需团队协作的用户\n* 支持 CI/CD 自动发布\n* 支持 AI Agent 自动发布\n\n**Server 模式部署**\n\n**Claude Desktop 配置：**：\n\n \n\n多公众号发布（进阶）\n\n> [!IMPORTANT]\n>\n> 必须使用server模式才能使用多公众号发布功能。\n\n步骤：\n\n- 首先部署server\n- 在server端配置多个微信凭据，参考文档\n- 让MCP 客户端连接server\n\n对AI说\n\n> 使用phycat主题将这篇文章发布到微信公众号(your_app_id)：./tests/publish.md\n\nDocker 部署\n\n适合不希望安装 Node.js 环境的用户。\n\n \n\n* **Claude Desktop 配置：**：\n\n \n\n> **Docker 配置特别说明：**\n>\n> * **挂载目录 (-v)**：必须将宿主机的文件/图片目录挂载到容器内的 /mnt/host-downloads。\n> * **环境变量 (HOST_FILE_PATH)**：必须与宿主机挂载的文件/图片目录路径保持一致。\n> * **原理**：你的 Markdown 文件/文章内所引用的本地图片应放置在该目录中，Docker 会自动将其映射，使容器能够读取并上传。\n\n如何调试\n\n推荐使用官方 Inspector 进行调试：\n\n \n\n启动成功出现类似提示：\n\n \n\n访问以上链接即可打开调试页面。\n\n \n\n1. 正确填写启动命令\n2. 添加环境变量\n3. 点击 Connect\n4. 选择 Tools -> List Tools\n5. 选择要调试的接口\n6. 填入参数并点击 Run Tool\n7. 查看完整参数\n\n赞助\n\n如果你觉得文颜对你有帮助，可以给我家猫咪买点罐头 ❤️\n\nhttps://yuzhi.tech/sponsor\n\nLicense\n\nApache License Version 2.0', 'readme_raw_len': 3399, 'one_line_zh': '📇 🏠 🍎 🪟 🐧 - 用 Wenyan 排版 Markdown 文章，并发布到微信公众号', 'one_line_plain': 'Convert Markdown articles into formatted posts for WeChat Official Accounts'}, {'id': 2541, 'key': 'apecloud-aperag', 'kind': 'mcp', 'name': 'apecloud/ApeRAG', 'one_line': '🐍 ☁️ 🏠 - RAG platform combining Graph RAG, vector search and full-text search for building knowledge graphs…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'ApeRAG: Production-ready GraphRAG with multi-modal indexing, AI agents, MCP support, and scalable K8s deployment', 'url': 'https://github.com/apecloud/ApeRAG', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/apecloud/ApeRAG', 'downloads': None, 'stars': 1322, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/apecloud.png?size=64', 'official_desc': '🐍 ☁️ 🏠 - RAG platform combining Graph RAG, vector search and full-text search for building knowledge graphs and context engineering.', 'stars_own': 1322, 'stars_creator': None, 'stars_own_repo': 'apecloud/ApeRAG', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'ApeRAG\n \n\n \n\n \n\nApeRAG is a production-ready RAG (Retrieval-Augmented Generation) platform that combines Graph RAG, vector search, and full-text search with advanced AI agents. Build sophisticated AI applications with hybrid retrieval, multimodal document processing, intelligent agents, and enterprise-grade management features.\n\nApeRAG is the best choice for building your own Knowledge Graph, Context Engineering, and deploying intelligent AI agents that can autonomously search and reason across your knowledge base.\n\n阅读中文文档\n\n- Quick Start\n- Key Features\n- Kubernetes Deployment (Recommended for Production)\n- Development\n- Build Docker Image\n- Acknowledgments\n- License\n\nQuick Start\n\n> Before installing ApeRAG, make sure your machine meets the following minimum system requirements:\n>\n> - CPU >= 2 Core\n> - RAM >= 4 GiB\n> - Docker & Docker Compose\n\nThe easiest way to start ApeRAG is through Docker Compose. Before running the following commands, make sure that Docker and Docker Compose are installed on your machine:\n\n \n\nAfter running, you can access ApeRAG in your browser at:\n- **Web Interface**: http://localhost:3000/web/\n- **API Documentation**: http://localhost:8000/docs\n\nMCP (Model Context Protocol) Support\n\nApeRAG supports MCP (Model Context Protocol) integration, allowing AI assistants to interact with your knowledge base directly. After starting the services, configure your MCP client with:\n\n \n\n**Authentication** (by priority):\n1. **HTTP Authorization Header** (Recommended): Authorization: Bearer your-api-key\n2. **Environment Variable** (Fallback): APERAG_API_KEY=your-api-key\n\n**Important**: Use your deployed API origin if not local (e.g. https://your-host/mcp/). Replace your-api-key-here with a valid API key from your ApeRAG settings.\n\nThe MCP server provides:\n- **Collection browsing**: List and explore your knowledge collections\n- **Hybrid search**: Search using vector, full-text, and graph methods\n- **Intelligent querying**: Ask natural language questions about your documents\n\nEnhanced Document Parsing\n\nFor enhanced document parsing capabilities, ApeRAG supports an **advanced document parsing service** powered by MinerU, which provides superior parsing for complex documents, tables, and formulas. \n\n \n Enhanced Document Parsing Commands \n\n \n\nOr use the Makefile shortcuts (requires GNU Make):\n \n\n \n\nDevelopment & Contributing\n\nFor developers interested in source code development, advanced configurations, or contributing to ApeRAG, please refer to our Development Guide for detailed setup instructions.\n\nKey Features\n\n**1. Advanced Index Types**:\nFive comprehensive index types for optimal retrieval: **Vector**, **Full-text**, **Graph**, **Summary**, and **Vision** - providing multi-dimensional document understanding and search capabilities.\n\n**2. Intelligent AI Agents**:\nBuilt-in AI agents with MCP (Model Context Protocol) tool support that can automatically identify relevant collections, search content intelligently, and provide web search capabilities for comprehensive question answering.\n\n**3. Enhanced Graph RAG with Entity Normalization**:\nDeeply modified LightRAG implementation with advanced entity normalization (entity merging) for cleaner knowledge graphs and improved relational understanding.\n\n**4. Multimodal Processing & Vision Support**:\nComplete multimodal document processing including vision capabilities for images, charts, and visual content analysis alongside traditional text processing.\n\n**5. Hybrid Retrieval Engine**:\nSophisticated retrieval system combining Graph RAG, vector search, full-text search, summary-based retrieval, and vision-based search for comprehensive document understanding.\n\n**6. MinerU Integration**:\nAdvanced document parsing service powered by MinerU technology, providing superior parsing for complex documents, tables, formulas, and scientific content with optional GPU acceleration.\n\n**7. Production-Grade Deployment**:\nFull Kubernetes support with Helm charts and KubeBlocks integration for simplified deployment of production-grade databases (PostgreSQL, Redis, Qdrant, Elasticsearch, Neo4j).\n\n**8. Enterprise Management**:\nBuilt-in audit logging, LLM model management, graph visualization, comprehensive document management interface, and agent workflow management.\n\n**9. MCP Integration**:\nFull support for Model Context Protocol (MCP), enabling seamless integration with AI assistants and tools for direct knowledge base access and intelligent querying.\n\n**10. Developer Friendly**:\nFastAPI backend, React frontend, async task processing with Celery, extensive testing, comprehensive development guides, and agent development framework for easy contribution and customization.\n\nKubernetes Deployment (Recommended for Production)\n\n> **Enterprise-grade deployment with high availability and scalability**\n\nDeploy ApeRAG to Kubernetes using our provided Helm chart. This approach offers high availability, scalability, and production-grade management capabilities.\n\nPrerequisites\n\n* Kubernetes cluster (v1.20+)\n* kubectl configured and connected to your cluster\n* Helm v3+ installed\n\nClone the Repository\n\nFirst, clone the ApeRAG repository to get the deployment files:\n\n \n\nStep 1: Deploy Database Services\n\nApeRAG requires PostgreSQL, Redis, Qdrant, and Elasticsearch. You have two options:\n\n**Option A: Use existing databases** - If you already have these databases running in your cluster, edit deploy/aperag/values.yaml to configure your database connection details, then skip to Step 2.\n\n**Option B: Deploy databases with KubeBlocks** - Use our automated database deployment (database connections are pre-configured):\n\n \n\nWait for all database pods to be in Running status before proceeding.\n\nStep 2: Deploy ApeRAG Application\n\n \n\nConfiguration Options\n\n**Resource Requirements**: By default, includes doc-ray service (requires 4+ CPU cores, 8GB+ RAM). To disable: set docray.enabled: false in values.yaml.\n\n**Advanced Settings**: Review values.yaml for additional configuration options including images, resources, and Ingress settings.\n\nAccess Your Deployment\n\nOnce deployed, access ApeRAG using port forwarding:\n\n \n\nFor production environments, configure Ingress in values.yaml for external access.\n\nTroubleshooting\n\n**Database Issues**: See deploy/databases/README.md for KubeBlocks management, credentials, and uninstall procedures.\n\n**Pod Status**: Check pod logs for any deployment issues:\n \n\nAcknowledgments\n\nApeRAG integrates and builds upon several excellent open-source projects:\n\nLightRAG\nThe graph-based knowledge retrieval capabilities in ApeRAG are powered by a deeply modified version of LightRAG:\n- **Paper**: "LightRAG: Simple and Fast Retrieval-Augmented Generation" (arXiv:2410.05779)\n- **Authors**: Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, Chao Huang\n- **License**: MIT License\n\nWe have extensively modified LightRAG to support production-grade concurrent processing, distributed task queues (Celery/Prefect), and stateless operations. See our LightRAG modifications changelog for details.\n\nCommunity\n\n* Discord\n* Feishu\n\n \n\nStar History\n\n \n\nLicense\n\nApeRAG is licensed under the Apache License 2.0. See the LICENSE file for details.', 'readme_raw_len': 7166, 'one_line_zh': '融合 Graph RAG、向量检索与全文检索的 RAG 平台，用于构建知识图谱', 'one_line_plain': 'Build knowledge graphs with Graph RAG, vector and full-text search'}, {'id': 3109, 'key': 'aidc-ai-pixelle-mcp', 'kind': 'mcp', 'name': 'AIDC-AI/Pixelle-MCP', 'one_line': '🐍 📇 🏠 - Omnimodal AIGC framework that turns ComfyUI workflows into MCP tools without code, for text, image,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/ATH-MaaS/Pixelle-MCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/AIDC-AI/Pixelle-MCP', 'downloads': None, 'stars': 1131, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/AIDC-AI.png?size=64', 'official_desc': 'or quick experience and testing** Temporary Run **View uvx CLI Reference →** Persistent Installation **View pip CLI Reference →** After startup, it will automatically enter the **configuration wizard** to guide you through execution engine selection (ComfyUI/RunningHub) and LLM configuration.', 'stars_own': 1131, 'stars_creator': None, 'stars_own_repo': 'ATH-MaaS/Pixelle-MCP', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "🎨 Pixelle MCP - Omnimodal Agent Framework \n\n English | 中文 \n\n ✨ An AIGC solution based on the MCP protocol, supporting both local ComfyUI and cloud ComfyUI (RunningHub) modes, seamlessly converting workflows into MCP tools with zero code. \n\n \n\nhttps://github.com/user-attachments/assets/65422cef-96f9-44fe-a82b-6a124674c417\n\n📋 Recent Updates\n\n- ✅ **2025-09-29**: Added RunningHub cloud ComfyUI support, enabling workflow execution without local GPU and ComfyUI environment\n- ✅ **2025-09-03**: Architecture refactoring from three services to unified application; added CLI tool support; published to PyPI\n- ✅ **2025-08-12**: Integrated the LiteLLM framework, adding multi-model support for Gemini, DeepSeek, Claude, Qwen, and more\n\n🚀 Features\n\n- ✅ 🔄 **Full-modal Support**: Supports TISV (Text, Image, Sound/Speech, Video) full-modal conversion and generation\n- ✅ 🚀 **Dual Execution Modes**: Local ComfyUI self-hosted environment + RunningHub cloud ComfyUI service, users can flexibly choose based on their needs\n- ✅ 🧩 **ComfyUI Ecosystem**: Built on ComfyUI, inheriting all capabilities from the open ComfyUI ecosystem\n- ✅ 🔧 **Zero-code Development**: Defines and implements the Workflow-as-MCP Tool solution, enabling zero-code development and dynamic addition of new MCP Tools\n- ✅ 🗄️ **MCP Server**: Based on the MCP protocol, supporting integration with any MCP client (including but not limited to Cursor, Claude Desktop, etc.)\n- ✅ 🌐 **Web Interface**: Developed based on the Chainlit framework, inheriting Chainlit's UI controls and supporting integration with more MCP Servers\n- ✅ 📦 **One-click Deployment**: Supports PyPI installation, CLI commands, Docker and other deployment methods, ready to use out of the box\n- ✅ ⚙️ **Simplified Configuration**: Uses environment variable configuration scheme, simple and intuitive configuration\n- ✅ 🤖 **Multi-LLM Support**: Supports multiple mainstream LLMs, including OpenAI, Ollama, Gemini, DeepSeek, Claude, Qwen, and more\n\n📁 Project Architecture\n\nPixelle MCP adopts a **unified architecture design**, integrating MCP server, web interface, and file services into one application, providing:\n\n- 🌐 **Web Interface**: Chainlit-based chat interface supporting multimodal interaction\n- 🔌 **MCP Endpoint**: For external MCP clients (such as Cursor, Claude Desktop) to connect\n- 📁 **File Service**: Handles file upload, download, and storage\n- 🛠️ **Workflow Engine**: Supports both local ComfyUI and cloud ComfyUI (RunningHub) workflows, automatically converts workflows into MCP tools\n\n \n\n \n\n🏃\u200d♂️ Quick Start\n\nChoose the deployment method that best suits your needs, from simple to complex:\n\n🎯 Method 1: One-click Experience\n\n> 💡 **Zero configuration startup, perfect for quick experience and testing**\n\n🚀 Temporary Run\n\n \n\n📚 **View uvx CLI Reference →**\n\n📦 Persistent Installation\n\n \n\n📚 **View pip CLI Reference →**\n\nAfter startup, it will automatically enter the **configuration wizard** to guide you through execution engine selection (ComfyUI/RunningHub) and LLM configuration.\n\n🛠️ Method 2: Local Development Deployment\n\n> 💡 **Supports custom workflows and secondary development**\n\n📥 1. Get Source Code\n\n \n\n🚀 2. Start Service\n\n \n\n📚 **View Complete CLI Reference →**\n\n🔧 3. Add Custom Workflows (Optional)\n\n \n\n**⚠️ Important**: Make sure to test workflows in ComfyUI first to ensure they run properly, otherwise execution will fail.\n\n🐳 Method 3: Docker Deployment\n\n> 💡 **Suitable for production environments and containerized deployment**\n\n📋 1. Prepare Configuration\n\n \n\n🚀 2. Start Container\n\n \n\n🌐 Access Services\n\nRegardless of which method you use, after startup you can access via:\n\n- **🌐 Web Interface**: http://localhost:9004 \n *Default username and password are both dev, can be modified after startup*\n- **🔌 MCP Endpoint**: http://localhost:9004/pixelle/mcp \n *For MCP clients like Cursor, Claude Desktop to connect*\n\n**💡 Port Configuration**: Default port is 9004, can be customized via environment variable PORT=your_port.\n\n⚙️ Initial Configuration\n\nOn first startup, the system will automatically detect configuration status:\n\n1. **🚀 Execution Engine Selection**: Choose between local ComfyUI or RunningHub cloud service\n2. **🤖 LLM Configuration**: Configure at least one LLM provider (OpenAI, Ollama, etc.)\n3. **📁 Workflow Directory**: System will automatically create necessary directory structure\n\n🌐 RunningHub Cloud Mode Advantages\n- ✅ **Zero Hardware Requirements**: No need for local GPU or high-performance hardware\n- ✅ **No Environment Setup**: No need to install and configure ComfyUI locally\n- ✅ **Ready to Use**: Register and get API key to start immediately\n- ✅ **Stable Performance**: Professional cloud infrastructure ensures stable execution\n- ✅ **Auto Scaling**: Automatically handles concurrent requests and resource allocation\n\n🏠 Local ComfyUI Mode Advantages\n- ✅ **Full Control**: Complete control over execution environment and model versions\n- ✅ **Privacy Protection**: All data processing happens locally, ensuring data privacy\n- ✅ **Custom Models**: Support for custom models and nodes not available in cloud\n- ✅ **No Network Dependency**: Can work offline without internet connection\n- ✅ **Cost Control**: No cloud service fees for high-frequency usage\n\n**🆘 Need Help?** Join community groups for support (see Community section below)\n\n🛠️ Add Your Own MCP Tool\n\n⚡ One workflow = One MCP Tool, supports two addition methods:\n\n📋 **Method 1: Local ComfyUI Workflow** - Export API format workflow files\n📋 **Method 2: RunningHub Workflow ID** - Use cloud workflow IDs directly\n\n \n\n🎯 1. Add the Simplest MCP Tool\n\n* 📝 Build a workflow in ComfyUI for image Gaussian blur (Get it here), then set the LoadImage node's title to $image.image! as shown below:\n \n\n* 📤 Export it as an API format file and rename it to i_blur.json. You can export it yourself or use our pre-exported version (Get it here)\n\n* 📋 Copy the exported API workflow file (must be API format), input it on the web page, and let the LLM add this Tool\n\n \n\n* ✨ After sending, the LLM will automatically convert this workflow into an MCP Tool\n\n \n\n* 🎨 Now, refresh the page and send any image to perform Gaussian blur processing via LLM\n\n \n\n🔌 2. Add a Complex MCP Tool\n\nThe steps are the same as above, only the workflow part differs (Download workflow: UI format and API format)\n\n> **Note:** When using RunningHub, you only need to input the corresponding workflow ID, no need to download and upload workflow files.\n\n \n\n🔧 ComfyUI Workflow Custom Specification\n\n🎨 Workflow Format\nThe system supports ComfyUI workflows. Just design your workflow in the canvas and export it as API format. Use special syntax in node titles to define parameters and outputs.\n\n📝 Parameter Definition Specification\n\nIn the ComfyUI canvas, double-click the node title to edit, and use the following DSL syntax to define parameters:\n\n \n\n🔍 Syntax Explanation:\n- param_name: The parameter name for the generated MCP tool function\n- ~: Optional, indicates URL parameter upload processing, returns relative path\n- field_name: The corresponding input field in the node\n- !: Indicates this parameter is required\n- description: Description of the parameter\n\n💡 Example:\n\n**Required parameter example:**\n\n- Set LoadImage node title to: $image.image!:Input image URL\n- Meaning: Creates a required parameter named image, mapped to the node's image field\n\n**URL upload processing example:**\n\n- Set any node title to: $image.~image!:Input image URL\n- Meaning: Creates a required parameter named image, system will automatically download URL and upload to ComfyUI, returns relative path\n\n> 📝 Note: LoadImage, VHS_LoadAudioUpload, VHS_LoadVideo and other nodes have built-in functionality, no need to add ~ marker\n\n🎯 Type Inference Rules\n\nThe system automatically infers parameter types based on the current value of the node field:\n- 🔢 int: Integer values (e.g. 512, 1024)\n- 📊 float: Floating-point values (e.g. 1.5, 3.14)\n- ✅ bool: Boolean values (e.g. true, false)\n- 📝 str: String values (default typ", 'readme_raw_len': 11185, 'one_line_zh': '全模态 AIGC 框架，无需写代码即可把 ComfyUI 工作流转成 MCP 工具，覆盖文本与图像等。', 'one_line_plain': 'Omnimodal AIGC framework converts ComfyUI workflows into MCP tools for text, image, and other media'}, {'id': 3409, 'key': 'jae-jae-fetcher-mcp', 'kind': 'mcp', 'name': 'jae-jae/fetcher-mcp', 'one_line': '📇 🏠 - Fetch web page content using a Playwright headless browser, supporting JavaScript rendering, content…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for fetch web page content using Playwright headless browser.', 'url': 'https://github.com/jae-jae/fetcher-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/jae-jae/fetcher-mcp', 'downloads': None, 'stars': 1088, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/jae-jae.png?size=64', 'official_desc': '📇 🏠 - Fetch web page content using a Playwright headless browser, supporting JavaScript rendering, content extraction and Markdown or HTML output.', 'stars_own': 1088, 'stars_creator': None, 'stars_own_repo': 'jae-jae/fetcher-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "中文 |\nDeutsch |\nEspañol |\nfrançais |\n日本語 |\n한국어 |\nPortuguês |\nРусский\n\nFetcher MCP\n\nMCP server for fetch web page content using Playwright headless browser.\n\n> 🌟 **Recommended**: OllaMan - Powerful Ollama AI Model Manager.\n\nAdvantages\n\n- **JavaScript Support**: Unlike traditional web scrapers, Fetcher MCP uses Playwright to execute JavaScript, making it capable of handling dynamic web content and modern web applications.\n\n- **Intelligent Content Extraction**: Built-in Readability algorithm automatically extracts the main content from web pages, removing ads, navigation, and other non-essential elements.\n\n- **Flexible Output Format**: Supports both HTML and Markdown output formats, making it easy to integrate with various downstream applications.\n\n- **Parallel Processing**: The fetch_urls tool enables concurrent fetching of multiple URLs, significantly improving efficiency for batch operations.\n\n- **Resource Optimization**: Automatically blocks unnecessary resources (images, stylesheets, fonts, media) to reduce bandwidth usage and improve performance.\n\n- **Robust Error Handling**: Comprehensive error handling and logging ensure reliable operation even when dealing with problematic web pages.\n\n- **Configurable Parameters**: Fine-grained control over timeouts, content extraction, and output formatting to suit different use cases.\n\nQuick Start\n\nRun directly with npx:\n\n \n\nFirst time setup - install the required browser by running the following command in your terminal:\n\n \n\nHTTP and SSE Transport\n\nUse the --transport=http parameter to start both Streamable HTTP endpoint and SSE endpoint services simultaneously:\n\n \n\nAfter startup, the server provides the following endpoints:\n\n- /mcp - Streamable HTTP endpoint (modern MCP protocol)\n- /sse - SSE endpoint (legacy MCP protocol)\n\nClients can choose which method to connect based on their needs.\n\nDebug Mode\n\nRun with the --debug option to show the browser window for debugging:\n\n \n\nConfiguration MCP\n\nConfigure this MCP server in Claude Desktop:\n\nOn MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n\nDocker Deployment\n\nRunning with Docker\n\n \n\nDeploying with Docker Compose\n\nCreate a docker-compose.yml file:\n\n \n\nThen run:\n\n \n\nFeatures\n\n- fetch_url - Retrieve web page content from a specified URL\n\n - Uses Playwright headless browser to parse JavaScript\n - Supports intelligent extraction of main content and conversion to Markdown\n - Supports the following parameters:\n - url: The URL of the web page to fetch (required parameter)\n - timeout: Page loading timeout in milliseconds, default is 30000 (30 seconds)\n - waitUntil: Specifies when navigation is considered complete, options: 'load', 'domcontentloaded', 'networkidle', 'commit', default is 'load'\n - extractContent: Whether to intelligently extract the main content, default is true\n - maxLength: Maximum length of returned content (in characters), default is no limit\n - returnHtml: Whether to return HTML content instead of Markdown, default is false\n - waitForNavigation: Whether to wait for additional navigation after initial page load (useful for sites with anti-bot verification), default is false\n - navigationTimeout: Maximum time to wait for additional navigation in milliseconds, default is 10000 (10 seconds)\n - disableMedia: Whether to disable media resources (images, stylesheets, fonts, media), default is true\n - debug: Whether to enable debug mode (showing browser window), overrides the --debug command line flag if specified\n\n- fetch_urls - Batch retrieve web page content from multiple URLs in parallel\n - Uses multi-tab parallel fetching for improved performance\n - Returns combined results with clear separation between webpages\n - Supports the following parameters:\n - urls: Array of URLs to fetch (required parameter)\n - Other parameters are the same as fetch_url\n\n- browser_install - Install Playwright Chromium browser binary automatically\n\n - Installs required Chromium browser binary when not available\n - Automatically suggested when browser installation errors occur\n - Supports the following parameters:\n - withDeps: Install system dependencies required by Chromium browser, default is false\n - force: Force installation even if Chromium is already installed, default is false\n\nTips\n\nHandling Special Website Scenarios\n\nDealing with Anti-Crawler Mechanisms\n\n- **Wait for Complete Loading**: For websites using CAPTCHA, redirects, or other verification mechanisms, include in your prompt:\n\n \n\n This will use the waitForNavigation: true parameter.\n\n- **Increase Timeout Duration**: For websites that load slowly:\n \n This adjusts both timeout and navigationTimeout parameters accordingly.\n\nContent Retrieval Adjustments\n\n- **Preserve Original HTML Structure**: When content extraction might fail:\n\n \n\n Sets extractContent: false and returnHtml: true.\n\n- **Fetch Complete Page Content**: When extracted content is too limited:\n\n \n\n Sets extractContent: false.\n\n- **Return Content as HTML**: When HTML format is needed instead of default Markdown:\n \n Sets returnHtml: true.\n\nDebugging and Authentication\n\nEnabling Debug Mode\n\n- **Dynamic Debug Activation**: To display the browser window during a specific fetch operation:\n \n This sets debug: true even if the server was started without the --debug flag.\n\nUsing Custom Cookies for Authentication\n\n- **Manual Login**: To login using your own credentials:\n\n \n\n Sets debug: true or uses the --debug flag, keeping the browser window open for manual login.\n\n- **Interacting with Debug Browser**: When debug mode is enabled:\n\n 1. The browser window remains open\n 2. You can manually log into the website using your credentials\n 3. After login is complete, content will be fetched with your authenticated session\n\n- **Enable Debug for Specific Requests**: Even if the server is already running, you can enable debug mode for a specific request:\n \n Sets debug: true for this specific request only, opening the browser window for manual login.\n\nDevelopment\n\nInstall Dependencies\n\n \n\nInstall Playwright Browser\n\nInstall the browsers needed for Playwright:\n\n \n\nBuild the Server\n\n \n\nDebugging\n\nUse MCP Inspector for debugging:\n\n \n\nYou can also enable visible browser mode for debugging:\n\n \n\nRelated Projects\n\n- g-search-mcp: A powerful MCP server for Google search that enables parallel searching with multiple keywords simultaneously. Perfect for batch search operations and data collection.\n\nLicense\n\nLicensed under the MIT License", 'readme_raw_len': 6505, 'one_line_zh': '使用 Playwright 无头浏览器抓取网页内容，支持 JavaScript 渲染与内容提取', 'one_line_plain': 'Playwright headless browser lets you fetch web page content, including JavaScript-rendered pages'}]/2026-10-20 https://anclum.com/zh/[{'id': 312, 'key': 'ahujasid-blender-mcp', 'kind': 'mcp', 'name': 'ahujasid/blender-mcp', 'one_line': '🐍 - Connect AI assistants to Blender', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/ahujasid/mcp-for-blender', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ahujasid/blender-mcp', 'downloads': None, 'stars': 31675, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ahujasid.png?size=64', 'official_desc': 'MCP for Blender Connect Blender to any LLM** formerly blender-mcp — the PyPI package is now mcp-for-blender.* Existing setups keep working; no config change is required.', 'stars_own': 31675, 'stars_creator': None, 'stars_own_repo': 'ahujasid/mcp-for-blender', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'MCP for Blender\n\n**Connect Blender to any LLM**\n\n*formerly blender-mcp — the PyPI package is now mcp-for-blender.*\n*Existing setups keep working; no config change is required. Read more*\n\n**Disclaimer:** This is a third-party integration and not made by Blender\n\nPrompt-assisted 3D modeling, scene creation, and manipulation — driven by AI.\n\n \n \n \n \n \n \n\n \n\n \n\n**Supporters**\n\nCodeRabbit\nGuillermo Rauch\n\n**All supporters:** Support this project\n\n \n\n---\n\nQuickstart\n\n> **Note:** the PyPI package blender-mcp is now **mcp-for-blender**. Existing setups\n> keep working — uvx blender-mcp still runs the server and **no config change is\n> required**. New installs should use mcp-for-blender.\n> What changed and why\n\n**One command** sets up your AI apps and the Blender addon:\n\n**macOS / Linux**\n\n \n\n**Windows** (PowerShell)\n\n \n\nAlready have uv? Run uvx mcp-for-blender setup instead.\n\nIt installs uv if needed, finds the MCP clients on your machine (Claude Desktop, Claude Code, Codex, Cursor, VS Code, Devin Desktop/Windsurf, OpenCode, Antigravity), and lets you pick which to set up. Then it installs the Blender addon and enables it. Your existing config is kept: it only adds a blender entry and saves a .bak of each file it changes.\n\nThen **fully quit and reopen your AI app** (on Windows, quit it from the system tray), open Blender, and ask it to build something. The addon starts its server when Blender opens.\n\n \n Manual setup, in case the automatic setup doesn\'t work \n\nThree steps: install uv, point your MCP client at the server, install the Blender addon.\n\n**1. Install uv**\n\n \n\n> **Warning:** Do not proceed before installing uv. Use the official installer — *not* pip install uv.\n\n**2. Add the MCP server to your client**\n\n \n Claude Desktop — Settings → Developer → Edit Config \n\n \n \n\n \n Claude Code \n\n \n \n\n \n Codex \n\n \n \n\n \n Cursor / VS Code / OpenCode / Antigravity \n\nSee MCP Client Setup below for per-client instructions and one-click install buttons.\n \n\n**3. Install the Blender addon**\n\n \n\nThen in Blender: **Edit → Preferences → Add-ons** → enable **Interface: MCP for Blender**.\n\n**4. Connect**\n\nThe addon starts its server when Blender opens. To check, press N in the 3D viewport → open the **MCP for Blender** tab (click **Start MCP Server** if it isn\'t running). That\'s it — ask Claude to build something.\n\n \n\n> **Note:** Only run **one** instance of the MCP server (either Cursor or Claude Desktop), not both.\n\n---\n\nTable of Contents\n\n- Quickstart\n- Features\n- Premium\n- Components\n- Installation\n - Automatic setup\n - Prerequisites\n - Make your client find uvx\n - Pin the Python version\n - Install without uv\n - Run with Docker\n - Environment Variables\n- MCP Client Setup\n - Claude for Desktop\n - Codex\n - Cursor\n - Visual Studio Code\n - OpenCode\n - Antigravity\n- Installing the Blender Addon\n- Upgrading (existing users)\n- Usage\n - Starting the Connection\n - Using with Claude\n - Capabilities\n - Example Commands\n- Persistent API Credentials\n- Troubleshooting\n- Technical Details\n- Limitations & Security Considerations\n- Telemetry Control\n- Feedback\n- Contributing\n- Disclaimer\n- Star History\n\n---\n\nFeatures\n\n| | |\n|---|---|\n| **Two-way communication** | Connect Claude AI to Blender through a socket-based server |\n| **Object manipulation** | Create, modify, and delete 3D objects in Blender |\n| **Material control** | Apply and modify materials and colors |\n| **Visual verification** | Multi-angle, camera, wireframe, X-ray and animation-strip views, plus renders, so the AI checks its own work |\n| **Code execution** | The AI writes Blender Python directly |\n| **Asset & model generation** | Poly Haven assets, Sketchfab models, Poly Pizza low-poly models, and AI-generated 3D models via Tripo, Hyper3D Rodin and Hunyuan3D |\n\nPremium\n\nGenerate AI 3D models (Hunyuan3D, Tripo, Hyper3D Rodin) straight into Blender without bringing your own API keys. More details\n\nComponents\n\nThe system consists of two main components:\n\n1. **Blender Addon** (addon.py) — a Blender addon that creates a socket server within Blender to receive and execute commands\n2. **MCP Server** (src/blender_mcp/server.py) — a Python server that implements the Model Context Protocol and connects to the Blender addon\n\n---\n\nInstallation\n\nAutomatic setup\n\nThe quickest way is the one-line installer. It installs uv, configures your MCP clients, and installs and enables the Blender addon:\n\n \n\n \n\nAlready have uv? Run uvx mcp-for-blender setup instead. See Quickstart for what it changes. The rest of this section covers manual setup.\n\nPrerequisites\n\n- **Blender** 3.0 or newer\n- **Python** 3.10 or newer\n- **uv** package manager\n\n \n Installing uv, per platform \n\n**macOS**\n \n\n**Windows**\n \n\nThen add uv to the user path in Windows (you may need to restart Claude Desktop after):\n\n \n\n**Linux**\n \n\nIt lands in ~/.local/bin — open a new shell so it\'s on your PATH.\n\nOtherwise, installation instructions are on their website: Install uv\n\nOn every OS, use uv\'s **official installer above — not pip install uv**, which may not create the uvx command and can hide uv inside an environment your client can\'t see.\n \n\n> **Warning:** Do not proceed before installing uv.\n\nMake your client find uvx\n\nMCP clients started from a GUI (Claude Desktop, Cursor, VS Code from the Dock/Start menu) do **not** inherit your terminal\'s PATH, so a bare "command": "uvx" can fail with **spawn uvx ENOENT** even though uvx works in your terminal. If that happens:\n\n- Find uvx\'s full path — which uvx (macOS/Linux) or where uvx (Windows) — and use it as "command", e.g. /opt/homebrew/bin/uvx or C:\\Users\\ \\.local\\bin\\uvx.exe.\n- On Windows you can instead wrap it: "command": "cmd", "args": ["/c", "uvx", "mcp-for-blender"].\n- After any PATH or config change, **fully quit and relaunch** the client (Windows: quit from the system tray, not just the window; macOS: Cmd + Q ).\n\nPin the Python version\n\n*Avoid conda / pyenv / version conflicts.*\n\nuv chooses which Python runs the server. On machines with conda (auto-activated base), pyenv, or asdf — or with a newer CPython release that some dependencies do not have wheels for yet — uv can grab an interpreter that makes installation fail. Pin Python 3.11 and prefer uv-managed interpreters to avoid using whatever is on your PATH:\n\n \n\n--python 3.11 still satisfies this package\'s requires-python >=3.10, and UV_PYTHON_PREFERENCE=only-managed keeps uv from selecting conda, pyenv, asdf, or system Python first. (The repo\'s .python-version is only a hint for contributors and does **not** affect uvx.)\n\nIf a previous failed attempt keeps replaying after a fix, clear the cache:\n\n \n\nInstall without uv\n\nOn locked-down machines you can skip uvx entirely with pipx, then point your client at the installed command:\n\n \n\nUse the resulting absolute path as "command" (find it with which mcp-for-blender / where mcp-for-blender) and omit args.\n\nRun with Docker\n\nYou can run the MCP server in a container instead of installing it. Blender itself still runs on your machine — the container only hosts the MCP server, which connects out to the Blender addon.\n\nBuild the image from the repo root:\n\n \n\nThen point your MCP client at it (the -i flag is required — the server talks to the client over stdin/stdout):\n\n \n\nThe image defaults to BLENDER_HOST=host.docker.internal, which reaches the host\'s Blender out of the box with Docker Desktop on **macOS and Windows**.\n\nOn **Linux**, host.docker.internal doesn\'t exist and the addon only listens on localhost, so use host networking instead:\n\n \n\nTo enable safe mode in the container, add "-e", "BLENDER_MCP_SAFE_MODE=1" to args.\n\nEnvironment Variables\n\nThe following environment variables can be used to configure the Blender connection:\n\n| Variable | Default | Description |\n|---|---|---|\n| BLENDER_HOST | localhost | Host address for Blender socket server |\n| BLENDER_PORT | 9876 | Port number for Blender socket server |\n| BLENDER_MCP_SAFE_MODE | off | Set to 1 to validate scripts before they run in Blender (see below) |\n| BLENDER_MCP_APPS |', 'readme_raw_len': 25500, 'one_line_zh': '🐍 - 将 AI 助手连接到 Blender', 'one_line_plain': 'Lets AI assistants control Blender'}, {'id': 797, 'key': 'shashankss1205-codegraphcontext', 'kind': 'mcp', 'name': 'shashankss1205/codegraphcontext', 'one_line': '🐍 🏠 🍎 🪟 🐧 - Index local code into a graph database to give AI assistants context, with graphical code…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/CodeGraphContext/CodeGraphContext', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Shashankss1205/CodeGraphContext', 'downloads': None, 'stars': 4299, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Shashankss1205.png?size=64', 'official_desc': '🐍 🏠 🍎 🪟 🐧 - Index local code into a graph database to give AI assistants context, with graphical code visualizations for humans.', 'stars_own': 4299, 'stars_creator': None, 'stars_own_repo': 'CodeGraphContext/CodeGraphContext', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': '🏗️ CodeGraphContext (CGC)\n\n**Turn code repositories into a queryable graph for AI agents.**\n\n🌐 **Languages:**\n- 🇬🇧 English\n- 🇨🇳 中文\n- 🇰🇷 한국어\n- 🇺🇦 Українська\n- 🇷🇺 Русский\n- 🇯🇵 日本語\n- 🇮🇳 தமிழ்\n- 🇪🇸 Español (Soon)\n\n🌍 **Help translate CodeGraphContext to your language by raising an issue & PR on GitHub Issues!**\n\n \n \n Bridge the gap between deep code graphs and AI context. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\nA powerful **MCP server** and **CLI toolkit** that indexes local code into a graph database to provide context to AI assistants and developers. Use it as a standalone CLI for comprehensive code analysis or connect it to your favorite AI IDE via MCP for AI-powered code understanding.\n\n---\n\n📍 Quick Navigation\n* 🚀 Quick Start \n* 📋 Prerequisites\n* 🏃 How to Run the Project Locally\n* 🌐 Supported Programming Languages \n* 🛠️ CLI Toolkit \n* 🤖 MCP Server \n* 🗄️ Database Options\n* 🔬 SCIP indexing (optional)\n\n---\n\n✨ Experience CGC\n\n👨🏻\u200d💻 Installation and CLI\n> Install in seconds with pip and unlock a powerful CLI for code graph analysis.\n \n\n🛠️ Indexing in Seconds\n> The CLI intelligently parses your tree-sitter nodes to build the graph.\n \n\n🤖 Powering your AI Assistant\n> Use natural language to query complex call-chains via MCP.\n \n\n---\n\nWhy CodeGraphContext?\n\nCodeGraphContext is built for the moment when plain text search stops being enough. It turns a repository into a graph of files, symbols, calls, inheritance, imports, and relationships so you can move from "where is this defined?" to "how does this actually connect?" without jumping between tools.\n\n \n\nComparison at a glance\n\n| Approach | Best for | Tradeoff |\n| --- | --- | --- |\n| grep / file search | Exact string lookup | Misses relationships and code structure |\n| RAG over code chunks | Natural-language retrieval | Can lose symbol-level precision |\n| CodeGraphContext | Repository-wide reasoning | Requires an indexing step |\n\nCommon use cases\n\n- Trace call chains across files and packages.\n- Understand class hierarchies, imports, and module boundaries.\n- Find dead code, hotspots, and unexpected dependencies.\n- Give an AI assistant the same structural context a maintainer would use.\n\nFAQ\n\n- **Do I need a remote service?** No. CGC works locally with embedded backends or with external graph databases when you want them.\n- **Is it only for one language?** No. It supports a broad mix of mainstream languages and keeps expanding.\n- **Can I use it with an AI assistant?** Yes. That is one of the main reasons the MCP server exists.\n\n---\n\nProject Details\n- **Version:** 0.5.1\n- **Authors:** Shashank Shekhar Singh \n- **License:** MIT License (See LICENSE for details)\n- **Website:** CodeGraphContext\n\n---\n\n👨\u200d💻 Maintainer\n**CodeGraphContext** is created and actively maintained by:\n\n**Shashank Shekhar Singh** \n- 📧 Email: shashankshekharsingh1205@gmail.com\n- 🐙 GitHub: @Shashankss1205\n- 🔗 LinkedIn: Shashank Shekhar Singh\n- 🌐 Website: codegraphcontext.vercel.app\n\n*Contributions and feedback are always welcome! Feel free to reach out for questions, suggestions, or collaboration opportunities.*\n\n---\n\nStar History\nStar History Chart ↗\n \n\n---\n\nFeatures\n- **Code Indexing:** Analyzes code and builds a knowledge graph of its components.\n- **Relationship Analysis:** Query for callers, callees, class hierarchies, call chains and more.\n- **Pre-indexed Bundles:** Load famous repositories instantly with .cgc bundles - no indexing required! (Learn more)\n- **Live File Watching:** Watch directories for changes and automatically update the graph in real-time (cgc watch).\n- **Interactive Setup:** A user-friendly command-line wizard for easy setup.\n- **Dual Mode:** Works as a standalone **CLI toolkit** for developers and as an **MCP server** for AI agents.\n- **Multi-Language Support:** Full support for 23 programming languages.\n- **Flexible Database Backend:** FalkorDB Lite (Default), KuzuDB, LadybugDB, FalkorDB Remote, Nornic DB, or Neo4j (all platforms via Docker/native).\n\n---\n\n🏗️ Architecture & Workflow\n\nCodeGraphContext parses your source code and builds a comprehensive knowledge graph. This graph can be queried directly via the CLI toolkit or exposed to AI assistants through the MCP server.\n\n \n\n---\n\nSupported Programming Languages\n\nCodeGraphContext provides comprehensive parsing and analysis for the following languages:\n\n| | Language | | Language | | Language |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 🐍 | **Python** | 📜 | **JavaScript** | 🔷 | **TypeScript** |\n| ☕ | **Java** | 🔵 | **C** | ➕ | **C++** |\n| #️⃣ | **C#** | 🐹 | **Go** | 🦀 | **Rust** |\n| 💎 | **Ruby** | 🐘 | **PHP** | 🍎 | **Swift** |\n| 🎨 | **Kotlin** | 🎯 | **Dart** | 🐪 | **Perl** |\n| 🌙 | **Lua** | 🚀 | **Scala** | λ | **Haskell** |\n| 💧 | **Elixir** | 📜 | **Emacs Lisp (elisp)** | 🌐 | **HTML** |\n| 🎨 | **CSS** | ⚛️ | **TSX** | ⛓️ | **Solidity** |\n| 🏗️ | **HCL (Terraform/Terragrunt)** | | | | |\n\nEach language parser extracts functions, classes, methods, parameters, inheritance relationships, function calls, and imports to build a comprehensive code graph.\n\n**HCL notes:** .tf, .tfvars and .hcl cover Terraform, OpenTofu and Terragrunt. HCL declares\nrather than calls, so it contributes blocks, variables and cross-configuration imports but no\nfunction calls. See docs/docs/contributing_languages.md § HCL.\n\n**Solidity notes:** .sol uses Tree-sitter via tree-sitter-language-pack (no SCIP). Supports Foundry remappings, modifier invocations, using Lib for T, and emit / custom-error revert as CALLS. See docs/docs/contributing_languages.md § Solidity.\n\n**Kotlin notes:** Annotations are recorded on functions and classes as decorators, retaining\ntheir arguments (@Preview(showBackground = true)). This makes find_dead_code\'s\nexclude_decorated_with usable on Kotlin and Android codebases — for example excluding\nComposable, Preview, Test and Provides. That filter matches on functions, so class-level\nannotations do not affect it. Annotations on interfaces, objects, constructors, parameters and\nproperties are not yet recorded.\n\n---\n\nDatabase Options\n\nCodeGraphContext supports multiple graph database backends to suit your environment:\n\n| Feature | KuzuDB | LadybugDB | FalkorDB Lite | Neo4j / Nornic DB |\n| :--- | :--- | :--- | :--- | :--- |\n| **Typical default** | Cross-platform fallback when FalkorDB Lite is unavailable | Optional embedded backend | **Default on Unix** (Python 3.12+, when falkordblite is installed) | When explicitly configured via cgc config db |\n| **Setup** | Zero-config / Embedded | Zero-config / Embedded | Zero-config / In-process | Docker / External |\n| **Platform** | **All (Windows Native, macOS, Linux)** | **All (Windows Native, macOS, Linux)** | Unix-only (Linux/macOS/WSL) | All Platforms |\n| **Use Case** | Desktop, IDE, Local development | Custom research projects | Specialized Unix development | Enterprise, Massive graphs |\n| **Requirement**| pip install kuzu | pip install ladybug | pip install falkordblite | Neo4j Server / Docker / Nornic Cloud |\n| **Speed** | ⚡ Extremely Fast | ⚡ Fast | 🚀 Scalable | 🌐 Network-dependent |\n| **Persistence**| Yes (to disk) | Yes (to disk) | Yes (to disk) | Yes (server-side) |\n\nEmbedded KuzuDB/LadybugDB default to a **4 GiB** buffer pool (CGC_EMBEDDED_BUFFER_POOL_MB). Set that env var to another MiB value, or 0 for the library default (~80% of system RAM).\n\n---\n\nSCIP indexing (optional)\n\nWhen SCIP_INDEXER=true in your CGC config (~/.codegraphcontext/.env), some languages use external **SCIP** indexers for more accurate calls and inheritance than Tree-sitter heuristics alone.\n\n**C and C++** use **scip-clang**, which requires a **compile_commands.json** file (a JSON compilation database): one entry per translation unit with the real compiler command (include paths, -D defines, -std, etc.). Without it, scip-clang cannot run; CGC logs a warning and **falls back to Tree-sitter** for that repo. Typical ways to produce the file: **CMake** with -DCMAKE_EXPORT_COMPILE_COMMANDS=ON, or wrap your real', 'readme_raw_len': 18076, 'one_line_zh': '将本地代码索引进图数据库，为 AI 助手提供上下文，并配有图形化代码浏览', 'one_line_plain': 'Index local code into a graph database to give AI assistants context, with graphical code…'}, {'id': 5753, 'key': 'skillsmp-com-creators-code-yeongyu-lazycodex-plugins-omo-skills-ultimate-browsing', 'kind': 'skill', 'name': 'ultimate-browsing', 'one_line': "Reaches web pages the user's browser cannot open: WAF-blocked and bot-scored hosts, platform-native readers…", 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/code-yeongyu/lazycodex/plugins-omo-skills-ultimate-browsing', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/code-yeongyu/lazycodex/plugins-omo-skills-ultimate-browsing', 'downloads': None, 'stars': 3760, 'installs': None, 'mentions': 5, 'score': 503.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/code-yeongyu.png?size=64', 'official_desc': "Reaches web pages the user's browser cannot open: WAF-blocked and bot-scored hosts, platform-native readers (Chinese and social platforms), archive snapshots with provenance, and a browser you own for stealth or throwaway profiles. Not for plain search, an unblocked static fetch, or a page the user'", 'stars_own': None, 'stars_creator': 3797, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'code-yeongyu/lazycodex', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '访问用户浏览器打不开的网页，包括被防火墙拦截或经机器人评分的站点。', 'one_line_plain': "The tool reaches web pages a user's browser cannot open, including WAF-blocked and bot-scored hosts, and platform-native readers"}, {'id': 4118, 'key': 'taylorwilsdon-google-workspace-mcp', 'kind': 'mcp', 'name': 'taylorwilsdon/google_workspace_mcp', 'one_line': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace integration covering Calendar, Drive, Gmail, Docs, Forms, Chat, Slides and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search & Drive with AI - Comprehensive Google Workspace MCP Server & CLI Tool', 'url': 'https://github.com/taylorwilsdon/google_workspace_mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/taylorwilsdon/google_workspace_mcp', 'downloads': None, 'stars': 3376, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/taylorwilsdon.png?size=64', 'official_desc': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace integration covering Calendar, Drive, Gmail, Docs, Forms, Chat, Slides and Sheets.', 'stars_own': 3376, 'stars_creator': None, 'stars_own_repo': 'taylorwilsdon/google_workspace_mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Google Workspace MCP Server \n\n \n \n \n \n \n \n\n \n\n*Full natural language control over Google Calendar, Drive, Gmail, Docs, Sheets, Slides, Forms, Tasks, Contacts, and Chat through all MCP clients, AI assistants and developer tools.*\n*Includes a full featured CLI & Code Mode for use with tools like Claude Code and Codex!*\n\n**The most feature-complete Google Workspace MCP server** is in a class of it\'s own: it can do things that Google\'s own tooling and the built in integrations with Claude and ChatGPT can\'t come close to with multi-user support, rich fine-grained editing tools and the most extensive coverage of any Workspace AI integration in existence. \n\nBy leveraging native OAuth 2.1, stateless deployment capability and external auth server & gateway passthrough auth support, it\'s also the only Workspace MCP you can host for your whole organization centrally & securely!\n\nSupports all free Google accounts & Google Workspace plans with expanded app options like Chat & Spaces. Interested in a managed cloud instance? That can be arranged (starting at $5/mo).\n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n---\n\n**See it in action:**\n \n \n \n\n---\n\nWhat It Does\n\nWorkspace MCP connects AI assistants to all twelve major Google Workspace services - 120+ tools behind a single MCP server, with OAuth 2.1 multi-user auth, three progressive tool tiers, read-only mode, a full CLI, and stateless container deployment. It runs locally over stdio for legacy clients and remotely over streamable HTTP with full implementation of the latest MCP spec.\n\nThe README covers just enough to get you running, with extensive documentation on the website:\n\n| Where to go | What you\'ll find |\n|:---|:---|\n| **Quick Start** | Google Cloud setup, credentials, and client connection with screenshots |\n| **Full Documentation** | Every tool, parameter, and auth mode |\n| **Advanced Deployment** | Reverse proxy & nginx config, origin validation, credential store backends (GCS/CMEK), trusted-gateway identity, and the complete environment variable reference |\n| **Client Setup Guides** | Claude Desktop/web Connectors, ChatGPT Developer Mode, and more |\n| **FAQ & Troubleshooting** | Enabling Google APIs, OAuth errors, redirect URIs, Google Chat setup, client quirks |\n\nSecurity & Compliance \n\n \n \n \n\n**For Security Teams**\n\nBy default, this server sends no data anywhere except Google\'s APIs, on behalf of the authenticated user, using your own OAuth client credentials. There is no usage reporting, analytics, license server, or SaaS dependency outside optional OTel support for your own usage.\n\n- **Fully open source** — every line is auditable in this repo\n- **Your OAuth client, your GCP project** — credentials never leave your environment & you control scopes\n- **You control the network** — deploy behind your reverse proxy, in your VPC, on your own terms\n- **Stateless mode** — zero disk writes for locked-down container environments\n- **Sensitive path blocking** — local file reads default to the managed attachment directory, and validate_file_path() still blocks .env* files plus common home-directory credential stores such as ~/.ssh/ and ~/.aws/ even if ALLOWED_FILE_DIRS is broadened\n\nFull dependency tree in pyproject.toml, pinned in uv.lock.\n\n \n \n\n**For Legal & Procurement**\n\nThis project is MIT licensed — not "open core," not "source available," not "free with a CLA." There is no dual licensing, no commercial tier gating features, and no contributor license agreement.\n\n- **Use commercially without restriction** — build products, sell services, deploy internally\n- **Fork, embed, redistribute** — MIT requires only attribution\n- **No CLA** — contributions remain under MIT\n- **No built-in telemetry to disclose** — optional tracing is off unless you configure it\n- **No network effects** — the server never contacts any endpoint you didn\'t configure\n- **Standard dependency licenses** — MIT, Apache 2.0, and BSD throughout the dependency chain; no copyleft, no AGPL\n \n \n \n\nServices\n\n \n \n \n 📧 Gmail \n 15 tools - search, send, draft, labels, filters, attachments \n \n \n 📁 Drive \n 16 tools - search, create, share, import Office files \n \n \n 📅 Calendar \n 7 tools - events, free/busy, Out of Office, Focus Time \n \n \n 📝 Docs \n 19 tools - edit, style, tables, tabs, comments, export \n \n \n \n \n 📊 Sheets \n 14 tools - ranges, tables, formatting, conditional rules \n \n \n 🖼️ Slides \n 7 tools - create, batch update, thumbnails, comments \n \n \n 📋 Forms \n 6 tools - build forms, publish, read responses \n \n \n ✅ Tasks \n 6 tools - tasks & lists with hierarchy \n \n \n \n \n 👤 Contacts \n 8 tools - people, groups, batch operations \n \n \n 💬 Chat \n 6 tools - spaces, messages, search, reactions \n \n \n 🔍 Custom Search \n 2 tools - programmable web search \n \n \n ⚡ Apps Script \n 15 tools - write, deploy, run & debug scripts \n \n \n \n\nEach page lists every tool with its tier, parameters, required scopes, and example prompts. The complete reference covers all twelve in one place.\n\n> 💬 **Google Chat** needs a one-time Chat app configuration and a Workspace account - see the Chat setup FAQ.\n\nQuick Start\n\n> Set credentials → pick a launch command → connect your client. Full walkthrough with screenshots: **workspacemcp.com/quick-start**\n\nYou\'ll need an OAuth client from Google Cloud Console in a project with the Google APIs enabled for the services you plan to use. The docs have one-click enable links for every API plus a single gcloud services enable command that covers them all, and the quick start guide walks through the whole setup in about five minutes.\n\n \n \n \n\n**Confidential Client**\n\n \n\n \n \n\n**OAuth 2.1 (PKCE)**\n\n \n\n \n \n \n\n**Tool tiers** keep context windows lean: core is the essential set, extended adds management operations, complete loads everything. Combine with --tools ..., --read-only, or per-service --permissions, and subtract individual tools with --disabled-tools ... - details in the server modes docs.\n\nConnect Your Client\n\n**Claude Desktop, web & mobile** - run the server in HTTP mode and add it as a **Connector** (Settings → Connectors → Add custom connector). This is the recommended path; the Connector guide has step-by-step screenshots. Legacy stdio configuration remains available for clients without Connector support - see the FAQ.\n\n**Claude Code**\n\n \n\n**ChatGPT** - connect via Developer Mode with the ChatGPT guide.\n\n**VS Code, LM Studio, Open WebUI, and everything else** - any MCP client works over streamable HTTP (recommended) or stdio. Client-specific walkthroughs live in the guides and FAQ.\n\nCLI\n\nworkspace-cli lists and calls tools against a running server with encrypted, disk-backed OAuth token caching - authenticate once, script forever:\n\n \n\nInstall globally with uv tool install . from this repo. ⚠️ Don\'t use uvx workspace-cli - an abandoned PyPI package squats that name.\n\nDeployment & Advanced Configuration\n\nEverything you need to run this in production lives in two places. The documentation covers auth modes and server configuration:\n\n- **OAuth 2.1 multi-user auth** - bearer tokens, required for remote or shared HTTP endpoints\n- **Stateless container mode** - zero disk writes for locked-down deployments\n- **OAuth proxy storage backends** - memory, disk, or Valkey/Redis for distributed setups\n- **External OAuth provider mode** - bring your own auth server, validate bearer tokens only\n- **Service accounts with domain-wide delegation** - per-request user impersonation with an optional domain allowlist\n- **Trusted-gateway identity** - proxy-verified per-user isolation with Pomerium, Cloudflare Access, oauth2-proxy, or any JWKS-verifiable gateway\n- **OpenTelemetry tracing** - optional, off unless you configure an OTLP endpoint\n- **Docker** - docker build -t workspace-mcp . && docker run -p 8000:8000 workspace-mcp\n\nThe **Advanced Deployment guide** covers self-hosting specifics: reverse proxy setup with WORKSPACE_EXTERNAL_URL (including the nginx Origin: null consent workaround, the WORKSPACE_MCP_ALLOW_NULL_ORIGIN_CONSENT escape', 'readme_raw_len': 11461, 'one_line_zh': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace 集成，覆盖 Calendar、Drive、Gmail、Docs、Forms、Chat、Slides 等', 'one_line_plain': 'Access Google Workspace tools including Calendar, Drive, Gmail, Docs, Forms, Chat, and Slides'}, {'id': 5045, 'key': 'mcpjam-inspector', 'kind': 'repo', 'name': 'MCPJam/inspector', 'one_line': 'Testing and evaluation platform to chat, inspect, and debug MCP servers, MCP apps, and ChatGPT apps.', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Testing and evaluation platform to chat, inspect, and debug MCP servers, MCP apps, and ChatGPT apps.', 'url': 'https://github.com/MCPJam/inspector', 'source': 'github', 'source_url': 'https://github.com/MCPJam/inspector', 'downloads': None, 'stars': 2272, 'installs': None, 'mentions': 1, 'score': 107.49, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-10', 'times_seen': 1, 'icon': 'https://github.com/MCPJam.png?size=64', 'official_desc': 'Testing and evaluation platform to chat, inspect, and debug MCP servers, MCP apps, and ChatGPT apps.', 'stars_own': 2272, 'stars_creator': None, 'stars_own_repo': 'MCPJam/inspector', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx @mcpjam/inspector@latest', 'install_src': 'HEAD/README.md', 'install_conf': 'high', 'desc_src': 'github-api', 'readme_body': "MCPJam \n\n \n\n \n Hosted app  · \n Docs  · \n Website \n \n\n \n \n \n\n \n\n**MCPJam** is the open-source **testing & evaluations platform for MCP server developers** who ship. Interactively test tools, prompts, resources, and authorization across 16 client configurations and 170+ models. Run evals on test cases, get insights on what's breaking, and gate regressions to secure every deployment.\n\n🚀 Quick start\n\nOpen the hosted app. No install needed.\n\n👉 **app.mcpjam.com**\n\n... or run MCPJam locally for HTTP/S and local STDIO servers:\n\n \n\n👋🏽 Why MCPJam\n\nClients like ChatGPT, Claude, and Cursor all read your server differently. MCPJam catches those differences, shows where your server breaks, and tells you how to fix it:\n\n- **Inspect & debug**: Explore your server's tools, resources, and prompts in one place. Test prompts from real LLMs, with full visibility & traces on every JSON-RPC message and OAuth exchange.\n- **Cross-client evals**: Score agent behavior across 16 client configurations — ChatGPT, Claude, Cursor, Copilot, and more — to understand how your MCP integrations perform across all users. Track accuracy, latency, and tool-call performance over time.\n- **Security & reliability**: Automate conformance & behavior checks in CI/CD to catch regressions before they reach production.\n\n \n\n \n\n_Trace view: every tool call, agent step, and JSON-RPC message in one timeline._\n\n \n\n🔨 Features\n\n| Capability | What it does |\n| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| **Playground** | Cross-client chat interface that emulates UIs, tool calls, and skills (with full traces). OpenAI Apps SDK and MCP app UIs, text tools, and a Chrome DevTools-style widget emulator. Read more |\n| **OAuth Debugger** | Visualize your OAuth & EMA requests step-by-step to find the source of errors. Guided MCP OAuth conformance checks across protocol versions 03-26, 06-18, 11-25, and 2026-07-28; DCR, client pre-registration, and CIMD. Read more |\n| **Server Debugging** | Manually run tools, resources, templates, prompts, and elicitation flows with full JSON-RPC logs. |\n| **Skills** | Extend models with reusable behaviors in Playground. Local skills are read from your filesystem and never leave your machine; a project can also carry hosted skills, available on accounts where that is enabled. Read more |\n| **Workspaces** | Shared server groups with real-time team sync, so everyone tests the same config. Read more |\n| **Evals** | Test cases with expected tool calls, run across LLMs, with accuracy metrics over time. Read more |\n| **CLI** | Probe servers, run doctor checks, exercise OAuth, and list tools/resources/prompts from your terminal. Read more |\n| **SDK** | Programmatically drive inspections, snapshot capabilities, and assert on tool/resource shapes from your own tests. Read more |\n| **CI/CD** | Run conformance, E2E tests, evals, and OAuth checks on every PR in GitHub Actions or any pipeline. Read more |\n\n---\n\nPlayground\n\nDebug your server against a model using tool calls or in-panel chat, with Chat, Trace, and Raw views. Supports the OpenAI Apps SDK and MCP app UIs, text tools, and a Chrome DevTools-style widget emulator so you can iterate on widgets locally.\n\n- Invoke a tool to render its widget instantly, or drive your server with an LLM.\n- Watch all JSON-RPC and window.openai messages in the logs.\n- Switch the emulator between Desktop, Tablet, and Mobile.\n- Test locale changes, CSP permissions, light/dark mode, hover & touch, and safe-area insets.\n\n \n\nOAuth Debugger\n\nGuided MCP OAuth conformance checks with step-by-step explanations. Test against every version of the OAuth spec (2025-03-26, 2025-06-18, 2025-11-25, and the 2026-07-28 draft), with support for client pre-registration, Dynamic Client Registration (DCR), and Client ID Metadata Documents (CIMD).\n\n \n\nServer Debugging\n\nEverything you need to test an MCP server by hand: run tools, resources, resource templates, prompts, and elicitation flows with full JSON-RPC observability. Every feature of the original inspector, and more.\n\n \n\nSkills\n\nUse Skills in Playground to extend models with reusable behaviors. Local skills are read from your filesystem and never leave your machine; a project can also carry hosted skills, available on accounts where that is enabled. Read more\n\nWorkspaces\n\nGroup servers into shared workspaces with real-time team sync, so everyone on your team tests against the same configuration. Read more\n\nEvals\n\nDefine test cases with expected tool calls and run them across multiple LLMs. Track accuracy over time to catch regressions early and improve your server with every iteration. Read more\n\nCLI\n\nRun MCPJam from the terminal for fast local dev loops and CI. Probe servers, run OAuth checks, inspect tools and resources, and execute evals without leaving your shell. Read more\n\nSDK\n\nProgrammatic access to MCPJam for custom tooling, scripting, and integrations. Build your own workflows on top of MCPJam's inspection and evaluation primitives. Read more\n\nCI/CD\n\nWire MCPJam into GitHub Actions, GitLab CI, or any CI system to run conformance, E2E tests, and evals on every PR — and catch MCP server regressions before they ship. Read more\n\n🔌 Install\n\nMCPJam Inspector runs three ways: a hosted web app, a desktop app for Mac and Windows, or via your terminal. The web app is HTTPS-only and has no install. Terminal and Desktop support HTTP/S and local STDIO servers.\n\n \n \n\n> Node.js 20+ is only required for the terminal install (npx). The hosted and desktop apps have no local runtime requirements.\n\n**Hosted web app**: Open app.mcpjam.com. No install, always latest, and you can share server links with teammates like a Google Doc. HTTPS server URLs only; no STDIO, tunneling, skills, or tasks (those need the local inspector). Hosted docs\n\n**Desktop app**: Download the installer. Supports HTTP/S and local STDIO, no Node.js required.\nInstall Mac · Install Windows\n\n**Terminal**: Supports HTTP/S and local STDIO. Open the printed localhost URL after it starts.\n\n \n\nThe launcher opens a private access link automatically. To sign in from another browser, use the link printed in your terminal. Docker users can retrieve it with docker logs . Keep it private; a new link is generated on restart unless MCPJAM_SESSION_TOKEN is configured. See the network access instructions for remote addresses and remapped ports.\n\n**Docker**: Bound to localhost for security. Available at http://127.0.0.1:6274. Always use -p 127.0.0.1:6274:6274 (not -p 6274:6274) to keep the inspector local-only. On macOS/Windows, reach host MCP servers via http://host.docker.internal:PORT.\n\n \n\n👨\u200d💻 Contributing\n\nWe're grateful you're considering it. Read the contributing guide to get started, and come say hi in Discord.\n\n🌍 Community & links\n\nWebsite · Blog · Pricing · Docs · Discord · 𝕏 (Twitter) · LinkedIn\n\n📄 License\n\nLicensed under the **Apache License 2.0**. See LICENSE.", 'readme_raw_len': 7082, 'one_line_zh': '用于聊天、检查与调试 MCP 服务器、MCP 应用及 ChatGPT 应用的测试评估平台', 'one_line_plain': 'Let users chat, inspect, and debug MCP servers, apps, and ChatGPT apps'}, {'id': 867, 'key': 'chigwell-telegram-mcp', 'kind': 'mcp', 'name': 'chigwell/telegram-mcp', 'one_line': '🐍 🏠 - Telegram API integration: access user data, manage dialogs (chats, channels, groups), retrieve and send…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Telegram MCP server powered by Telethon to let MCP clients read chats, manage groups, and send/modify messages, media, contacts, and settings.', 'url': 'https://github.com/chigwell/telegram-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/chigwell/telegram-mcp', 'downloads': None, 'stars': 1857, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/chigwell.png?size=64', 'official_desc': '🐍 🏠 - Telegram API integration: access user data, manage dialogs (chats, channels, groups), retrieve and send messages, and handle read status.', 'stars_own': 1857, 'stars_creator': None, 'stars_own_repo': 'chigwell/telegram-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'A Telegram integration for Claude, Cursor, and other MCP-compatible clients. It exposes Telegram account, chat, message, contact, media, folder, and admin operations through the Model Context Protocol using Telethon.\n\n🤖 MCP in Action\n\nBasic Telegram MCP usage in Claude:\n\n \n\nAsking Claude to analyze chat history and send a response:\n\n \n\nMessage sent successfully:\n\n \n\nContents\n\n- Skills & Practical Workflows\n- What It Can Do\n- Requirements\n- Quick Start\n- MCP Client Configuration\n- Multi-Account Setup\n- Device Identity\n- Expected Account Check\n- Proxy Support\n- File Path Security\n- Chat Access Privacy (Allowlist)\n- Docker\n- Development\n- Security Notes\n- Troubleshooting\n- License\n\nSkills & Workflows\n\nLooking for ready-to-use workflows, prompt examples, or integration recipes?\nExplore the **Skills Documentation** for step-by-step guides on:\n- Summarizing unread messages safely\n- Drafting replies without sending\n- Triaging action items & urgent requests\n- Searching chat history and expanding context\n\nWhat It Can Do\n\nThe server currently includes 80+ MCP tools grouped into these areas:\n\n- **Accounts:** list configured accounts and route tool calls by account label.\n- **Chats and groups:** list chats, inspect metadata, create groups/channels, join or leave chats, invite or remove users, manage admins, bans, default permissions, slow mode, topics, invite links, common chats, read receipts, and message links.\n- **Messages:** send, schedule, edit, delete, forward, pin, unpin, mark read, reply, search, inspect context, create polls, manage reactions, inspect inline buttons, and press inline callbacks. send_message, reply_to_message, and edit_message support classic formatting (parse_mode=\'md\'/\'html\') and server-side rich formatting (parse_mode=\'rich\'/\'rich_markdown\'/\'rich_html\' — full Markdown/HTML with tables, headings, formulas, and collapsible sections). Rich modes require Telegram Premium on the account; Premium is re-checked on every call, and without it nothing is sent — the tool returns a structured telegram_premium_required result so the agent can reformat with classic modes and retry. send_message, reply_to_message, and edit_message also accept format_date to render a date as a tappable chip.\n\nget_message_reactions returns an empty list for a message with no reactions. To reuse a custom reaction, pass the returned custom: value to send_reaction.\n\nvote_poll(chat_id, message_id, option_indices, account=None) votes in an existing poll.\nRead the poll with get_messages first, then select zero-based answer indices: [0]\nfor the first answer, or [0, 2] for the first and third in a multiple-choice poll.\nThis may replace an existing vote; empty selections, duplicate or out-of-range\nindices, closed polls, and multiple answers in single-choice polls are rejected.\nTelegram enforces voting permissions and restrictions on changing quiz votes.\n\n- **Contacts:** list, search, add, delete, block, unblock, import, export, inspect direct chats, find recent contact interactions, and remember contacts by the names you actually use (see below).\n\nRemembered contacts\n\nset_contact_alias teaches the server what you call someone, and every tool that takes a chat_id understands it from then on — send_message("андрей бекендер", ...) just works. A contact can carry any number of aliases, which is how tags work: save both андрей бекендер and бекендер for the same person and either resolves.\n\n**Only an exact saved wording ever sends.** Similar wording (Андрею бекендеру for a saved андрей бекендер) is matched too, but only to *suggest*: the tool sends nothing and asks you to confirm the contact by name. This is deliberate — Лена/Леня and Иван/Иванов differ exactly as much as a case ending does, so a matcher confident enough to handle declensions is also confident enough to message the wrong person whenever the one you meant is not saved yet. Confirming saves that wording as its own alias, so each new phrasing costs one yes/no the first time and nothing ever again. Set TELEGRAM_CONTACT_FUZZY=0 to drop the suggestions too.\n\nWhen a reference is unknown, resembles one contact, matches several, or points at a contact that no longer resolves, tools send nothing and return a structured instruction telling the agent exactly what to ask you, to save the answer with set_contact_alias, and to retry once. list_contact_aliases shows one row per person with all their aliases (use it to spot a wrong memory), delete_contact_alias forgets one, and repointing an alias at someone else requires replace=True. The save path itself refuses a target it would have to guess at: contacts are saved by @username, phone, numeric ID, or an alias already confirmed for them.\n\nAliases live in ${XDG_STATE_HOME:-~/.local/state}/telegram-mcp/aliases.json (owner-only, written atomically); TELEGRAM_ALIASES_FILE overrides the path, and a pre-existing aliases.json next to the code is still read as a fallback.\n- **Media:** send files, download media, upload files, send voice notes, stickers, GIFs, inspect message media, and transcribe voice messages/video notes (see below).\n\nVoice transcription\n\ntranscribe_voice(chat_id, message_id, engine=None) turns a voice message or video note into text. Four engines are available:\n\n- groq (default): uploads the recording to Groq\'s hosted whisper-large-v3-turbo. Leaves the server and costs a download+upload per call, but doesn\'t drop the recording\'s last few words the way native transcription does. Requires GROQ_API_KEY. Groq caps the size of a single upload, so a recording above TELEGRAM_TRANSCRIBE_GROQ_MAX_MB (default 25, the free-tier limit) is refused locally with a too_large error naming its size instead of being downloaded and rejected by the API. Raise the limit if your Groq tier allows bigger files, or transcribe that message with engine=\'telegram\', which has no such cap.\n- telegram: native Telegram Premium transcription (messages.TranscribeAudioRequest). Free and never leaves Telegram, but empirically drops the last speech segment in roughly 2 of 3 recordings and requires Telegram Premium on the account. Long recordings come back pending and are polled automatically.\n- openai: any OpenAI-compatible /audio/transcriptions endpoint — OpenAI itself, a self-hosted Parakeet or speaches server, LocalAI, a vLLM Whisper deployment, and so on. Set TELEGRAM_TRANSCRIBE_OPENAI_URL to the API base URL (e.g. https://api.openai.com/v1; a full .../audio/transcriptions URL also works), TELEGRAM_TRANSCRIBE_OPENAI_API_KEY for the bearer token (optional for keyless local servers), and TELEGRAM_TRANSCRIBE_OPENAI_MODEL (default whisper-1). Size cap: TELEGRAM_TRANSCRIBE_OPENAI_MAX_MB (default 25). For Parakeet use TELEGRAM_TRANSCRIBE_OPENAI_URL=http://localhost:5092/v1, the API key only if the server sets PARAKEET_API_KEY, and set TELEGRAM_TRANSCRIBE_LANGUAGE for anything that isn\'t English — Parakeet assumes en when no language is sent.\n- whisper: a local faster-whisper model loaded inside the MCP server process. The audio never leaves the machine. Install the extra with pip install \'telegram-mcp[whisper]\' (or uv sync --extra whisper). TELEGRAM_TRANSCRIBE_WHISPER_MODEL picks the model (default small; e.g. large-v3-turbo for better quality), TELEGRAM_TRANSCRIBE_WHISPER_DEVICE (auto/cpu/cuda), TELEGRAM_TRANSCRIBE_WHISPER_COMPUTE_TYPE (e.g. int8 on CPU) and TELEGRAM_TRANSCRIBE_WHISPER_MODEL_DIR (where models are downloaded) tune it. The model is loaded once on first use and recordings are transcribed one at a time. Not available in the Alpine Docker image; use openai against a Parakeet or other OpenAI-compatible server next to the container instead.\n\nTELEGRAM_TRANSCRIBE_LANGUAGE (ISO-639-1, e.g. nl) is passed as a language hint to every engine except telegram; unset, the engines auto-detect. TELEGRAM_TRANSCRIBE_TIMEOUT (default 120 seconds) bounds a single request to the HTTP engines (groq, openai).\n\nThe engine is chosen per call via the engine argument, or otherwise defaults to TELEGRAM_TRANSCRIBE_ENGIN', 'readme_raw_len': 38333, 'one_line_zh': '🐍 🏠 - Telegram API 集成：访问用户数据，管理对话（聊天、频道、群组），获取并发送…', 'one_line_plain': 'Access Telegram user data and manage chats, channels, and groups'}, {'id': 1828, 'key': 'stack-chan-stack-chan', 'kind': 'mcp', 'name': 'stack-chan/stack-chan', 'one_line': '📇 📟 - M5Stack-based robot with an MCP server for AI-controlled interactions and emotions', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A JavaScript-driven M5Stack-embedded super-kawaii robot.', 'url': 'https://github.com/stack-chan/stack-chan', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/stack-chan/stack-chan', 'downloads': None, 'stars': 1796, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/stack-chan.png?size=64', 'official_desc': '📇 📟 - M5Stack-based robot with an MCP server for AI-controlled interactions and emotions.', 'stars_own': 1796, 'stars_creator': None, 'stars_own_repo': 'stack-chan/stack-chan', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Stack-chan\n\n \n \n\n日本語\n\n \n\nStack-chan is an open-source, JavaScript-driven, super-kawaii robot built with M5Stack.\n\nThis repository provides firmware with M5StackChan CoreS3 as its standard configuration, user applications called MODs, browser development tools, cases, and schematics.\n\n- Video (with English subtitles): https://youtu.be/fZb_mF08xV0\n- Official hashtag: #stackchan | #ｽﾀｯｸﾁｬﾝ (JP)\n\nGetting started\n\nIf this is your first Stack-chan, start with a preassembled M5StackChan and the browser tools.\n\n1. Get an M5StackChan\n\nThe standard M5StackChan AI Desktop Robot (K151) package is enough to get started.\nThe joystick remote controller is optional.\n\n- Worldwide: M5Stack Official Store\n- Japan: M5StackChan\n- Japan: M5StackChan with joystick remote controller\n\n2. Flash the firmware\n\nConnect M5StackChan to a computer with a data-capable USB cable, then open the Web firmware installer in Chrome or Edge.\nSelect "M5StackChan CoreS3" as the device.\nYou do not need to install the Moddable SDK or ESP-IDF.\n\n> [!IMPORTANT]\n> Flashing this repository\'s firmware replaces the factory firmware supplied by M5Stack.\n> To restore it, follow the restore procedure in the M5Stack product documentation and use M5Burner.\n\n \n\n3. Try a MOD\n\nFind published MODs and mini apps in the MOD Gallery.\nChoose a MOD, then run it in the simulator or install it on a device.\nBlock-based samples can be opened directly in the block editor and changed.\n\n \n\nBrowser tools\n\nUse the Stack-chan browser tools to set up a device, change its preferences, and create MODs in a browser.\n\n \n\n| Tool | Purpose |\n| --- | --- |\n| Firmware installer | Flash firmware to a supported M5Stack over USB |\n| Preferences | Change Wi-Fi and device preferences over BLE |\n| MOD Gallery | Find, try, and install published MODs |\n| Block editor | Create MODs with Blockly and run them in the simulator or on a device |\n| Shape face editor | Arrange eyes and a mouth to create a custom Face |\n| Simulator | Run MODs with the WebAssembly firmware and a 3D model |\n| MediaPipe BLE tracking | Send tracked face and hand movement over BLE |\n\n \n\nSupported hardware\n\nThe distributed firmware supports M5Stack, M5Stack Core2, M5Stack CoreS3, and M5StackChan CoreS3.\nM5StackChan CoreS3 is the standard configuration and the target used for physical-device release validation.\n\nFirmware for Stack-chan RT and Takao Core2 + SG90 can be built from source.\nSee the firmware guide for target-specific commands and constraints.\n\nTo build the hardware yourself, see the case guide for parts and assembly instructions.\nSee the schematics for board information.\n\nRepository contents\n\n- firmware: Host firmware, modules, MODs, and development scripts\n- web: Firmware installer, preferences, editors, Gallery, and simulator\n- case: Case models for 3D printing\n- schematics: Schematics and board layouts\n- docs: Roadmap, specifications, operations guides, and release notes\n\nDevelopment\n\n- Firmware development\n- MOD development\n- Firmware API\n- Contribution guide\n- Latest release\n- Development roadmap\n\nContributing\n\nFeature requests, bug reports, and pull requests are welcome.\nRead the contribution guide, or open an issue.\n\nTo support the project financially, visit the GitHub Sponsors page.\n\nLicense\n\nThe resources in this repository are distributed under the Apache License 2.0.\nSee LICENSE.\n\nBibTeX', 'readme_raw_len': 3347, 'one_line_zh': '基于 M5Stack 的机器人，内置 MCP 服务器，可由 AI 控制交互与情绪表达', 'one_line_plain': 'M5Stack-based robot with an MCP server for AI-controlled interactions and emotions'}, {'id': 1516, 'key': 'lpigeon-ros-mcp-server', 'kind': 'mcp', 'name': 'lpigeon/ros-mcp-server', 'one_line': '🐍 🏠 🍎 🪟 🐧 - Control robots by converting natural language commands into ROS or ROS2 control commands', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/robotmcp/ros-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/lpigeon/ros-mcp-server', 'downloads': None, 'stars': 1501, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/lpigeon.png?size=64', 'official_desc': 'server Natural voice conversations with Claude Code (and other MCP capable agents)\n\n \n \n \n\nVoiceMode enables natural voice conversations with Claude Code. Voice isn't about replacing typing - it's about being available when typing isn't.\n\n**Perfect for:**\n\n- Walking to your next meeting\n- Cooking while debugging\n- Giving your eyes a break after hours of screen time\n- Holding a coffee (or a dog)\n- Any moment when your hands or eyes are busy\n\nSee It In Action\n\n \n\nQuick Start\n\n**Requirements:** Computer with microphone and speakers\n\nOption 1: Claude Code Plugin (Recommended)\n\nThe fastest way for Claude Code users to get started:\n\n \n\nOption 2: Python installer package\n\nInstalls dependencies and the VoiceMode Python package.\n\n \n\nFor manual setup, see the Getting Started Guide.\n\nFeatures\n\n- **Natural conversations** - speak naturally, hear responses immediately\n- **Works offline** - optional local voice services (Whisper STT, Kokoro TTS)\n- **Low latency** - fast enough to feel like a real conversation\n- **Smart silence detection** - stops recording when you stop speaking\n- **Privacy options** - run entirely locally or use cloud services\n\nCompatibility\n\n**Platforms:** Linux, macOS, Windows (native or WSL), NixOS\n**Python:** 3.10-3.14\n\nConfiguration\n\nVoiceMode works out of the box. For customization:\n\n \n\nSee the Configuration Guide for all options.\n\nPermissions Setup (Optional)\n\nTo use VoiceMode without permission prompts, add to ~/.claude/settings.json:\n\n \n\nSee the Permissions Guide for more options.\n\nLocal Voice Services\n\nFor privacy or offline use, install local speech services:\n\n- **Whisper.cpp** - Local speech-to-text\n- **Kokoro** - Local text-to-speech with multiple voices\n\nThese provide the same API as OpenAI, so VoiceMode switches seamlessly between them.\n\nInstallation Details\n\n \n System Dependencies by Platform \n\nUbuntu/Debian\n\n \n\n**WSL2 users**: The pulseaudio packages above are required for microphone access.\n\nFedora/RHEL\n\n \n\nmacOS\n\n \n\nNixOS\n\n \n\n \n\n \n Alternative Installation Methods \n\nFrom source\n\n \n\nNixOS system-wide\n\n \n\n \n\nTroubleshooting\n\n| Problem | Solution |\n|---------|----------|\n| No microphone access | Check terminal/app permissions. WSL2 needs pulseaudio packages. |\n| UV not found | Run curl -LsSf https://astral.sh/uv/install.sh \\| sh |\n| OpenAI API error | Verify OPENAI_API_KEY is set correctly |\n| No audio output | Check system audio settings and available devices |\n\nSave Audio for Debugging\n\n \n\nDocumentation\n\n- Getting Started - Full setup guide\n- Configuration - All environment variables\n- Whisper Setup - Local speech-to-text\n- Kokoro Setup - Local text-to-speech\n- Development Setup - Contributing guide\n\nFull documentation: voicemode.dev\n\nLinks\n\n- **Website**: voicemode.dev\n- **GitHub**: github.com/mbailey/voicemode\n- **PyPI**: pypi.org/project/voice-mode\n- **YouTube**: @getvoicemode\n- **Twitter/X**: @getvoicemode\n- **Newsletter**: \n\nLicense\n\nMIT - A Failmode Project\n\n---\nmcp-name: dev.voicemode/voicemode", 'readme_raw_len': 2982, 'one_line_zh': '语音交互：语音转文字、文字转语音，并可经本地服务进行实时语音对话。', 'one_line_plain': 'Voice interaction lets users perform speech-to-text, text-to-speech and real-time voice conversations locally'}, {'id': 4192, 'key': 'caol64-wenyan-mcp', 'kind': 'mcp', 'name': 'caol64/wenyan-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Format Markdown articles with Wenyan and publish them to WeChat Official Accounts', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '文颜 MCP Server 可以让 AI 自动将 Markdown 文章排版后发布至微信公众号。', 'url': 'https://github.com/caol64/wenyan-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/caol64/wenyan-mcp', 'downloads': None, 'stars': 1345, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/caol64.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Format Markdown articles with Wenyan and publish them to WeChat Official Accounts.', 'stars_own': 1345, 'stars_creator': None, 'stars_own_repo': 'caol64/wenyan-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '文颜 MCP Server\n\n \n \n \n \n \n\n简介\n\n**文颜（Wenyan）** 是一款多平台 Markdown 排版与发布工具，支持将 Markdown 一键转换并发布至：\n\n- 微信公众号\n- 知乎\n- 今日头条\n- 以及其它内容平台（持续扩展中）\n\n文颜的目标是：**让写作者专注内容，而不是排版和平台适配**。\n\n文颜的不同版本\n\n文颜目前提供多种形态，覆盖不同使用场景：\n\n- macOS App Store 版 - MAC 桌面应用\n- 跨平台桌面版 - Windows/Linux\n- CLI 版本 - 命令行 / CI 自动化发布\n- 👉 MCP 版本 - 本项目\n\n文颜 MCP Server 是什么？\n\n简单来说，它打通了“AI 写作”与“公众号发文”的通道。\n\n基于 MCP 协议，Claude Desktop 等 AI 客户端现在可以直接调用文颜（Wenyan）的排版引擎。写完文章后，不需要再去第三方编辑器里来回复制粘贴，直接让 AI 帮你排版并塞进微信草稿箱。\n\n**核心特性：**\n\n- **绕过排版工具**：AI 生成的 Markdown 直接转成微信富文本并上传，省去中间步骤。\n- **对话式排版**：直接打字跟 AI 说“换个橙色风格主题”，样式自动生效。\n- **不出窗口完成闭环**：在同一个聊天框里，顺滑搞定“想选题 -> 写文章 -> 调排版 -> 存草稿”的所有操作。\n\n**实战演示**：\n* 让 AI 帮你管理公众号的排版和发布\n* Moraya MCP 使用案例：微信公众号全托管\n\n功能特性\n\n- 一键发布 Markdown 到微信公众号草稿箱\n- 自动上传本地图片与封面\n- 支持远程 Server 发布（绕过 IP 白名单限制）\n- 内置多套精美排版主题\n- 支持自定义主题\n- 提供标准 MCP Tool 接口\n- 支持 AI 自动调用：\n - 渲染 Markdown\n - 主题管理\n - 发布草稿\n\n快速开始\n\n**安装**\n\n \n\n**Claude Desktop 配置 (claude_desktop_config.json)：**：\n\n \n\n基本用法\n\n列出主题\n\n对AI说\n\n> 目前你可以使用哪些公众号主题？\n\nAI回复：\n\n \n\n注册主题\n\n对AI说\n\n> 帮我把这个css(https://wenyan.yuzhi.tech/manhua.css)注册为一个新的公众号主题，名称为：xiuluochang\n\nAI回复：\n\n \n\n删除主题\n\n对AI说\n\n> 把名为“xiuluochang”的公众号主题删除\n\nAI回复：\n\n \n\n发布文章\n\n对AI说\n\n> 使用phycat主题将这篇文章发布到微信公众号：./tests/publish.md\n\nAI回复：\n\n \n\n概念\n\n环境变量配置\n\n> [!IMPORTANT]\n>\n> 请确保 MCP 启动时已配置如下环境变量，否则上传接口将调用失败。\n\n- WECHAT_APP_ID\n- WECHAT_APP_SECRET\n\n微信公众号 IP 白名单\n\n> [!IMPORTANT]\n>\n> 请确保运行文颜的机器 IP 已加入微信公众号后台的 IP 白名单，否则上传接口将调用失败。\n\n配置说明文档：https://yuzhi.tech/docs/wenyan/upload\n\n文章格式\n\n为了正确上传文章，每篇 Markdown 顶部需要包含一段 frontmatter：\n\n \n\n字段说明：\n\n| 字段 | 必填 | 说明 |\n| ---------- | -- | ----------------- |\n| title | ✅ | 文章标题 |\n| cover | ❌ | 封面图片（本地路径或网络 URL） |\n| author | ❌ | 作者 |\n| source_url | ❌ | 原文链接 |\n| type | ❌ | 文章类型，设为 image 表示图片消息（小绿书） |\n| image_list | ❌ | 图片路径列表（小绿书专用，最多 20 张） |\n| need_open_comment | ❌ | 是否打开评论 |\n| only_fans_can_comment | ❌ | 是否仅粉丝可评论 |\n\n说明：\n\n* 如果未指定 cover，将自动使用正文第一张图片作为封面\n* cover 支持本地路径和网络 URL\n* type 和 image_list 用于图片消息发布\n\n**示例文章**\n\n文内图片和文章封面\n\n把文章发布到公众号之前，文颜会按照微信要求自动处理文章内的所有图片，将其上传到公众号素材库。目前文颜对于以下图片都能很好的支持：\n\n- 本地硬盘绝对路径（如：/Users/xxx/image.jpg）\n- 网络路径（如：https://example.com/image.jpg）\n- 当前文章的相对路径（如：./assets/image.png）\n\n发布图片消息（小绿书）\n\n发布图片消息与发布图文消息的区别在于 frontmatter 中需要指定图片列表。支持两种方式：\n\n方式一：使用 type: image（推荐）\n\n在 frontmatter 中设置 type: image，CLI 会自动从正文中提取所有图片：\n\n \n\n方式二：手动指定 image_list\n\n直接在 frontmatter 中列出图片路径：\n\n \n\nimage_list 最多 20 张，首张即为封面。\n\nServer 模式\n\n相较于纯本地运行的**本地模式（Stdio Mode）**，wenyan-mcp还提供了 **远程客户端模式（Client–Server Mode）**。两种模式运行效果完全一致，你可以根据运行环境和网络条件选择最合适的方式。\n\n在本地模式下，MCP 直接调用微信公众号 API 完成图片上传和草稿发布。\n\n \n\n在远程客户端模式下，MCP 作为客户端，将发布请求发送到部署在云服务器上的 Wenyan Server，由 Server 完成微信公众号 API 调用。\n\n \n\n**适用于：**\n\n* 无本地固定 IP，需频繁添加IP 白名单的用户\n* 需团队协作的用户\n* 支持 CI/CD 自动发布\n* 支持 AI Agent 自动发布\n\n**Server 模式部署**\n\n**Claude Desktop 配置：**：\n\n \n\n多公众号发布（进阶）\n\n> [!IMPORTANT]\n>\n> 必须使用server模式才能使用多公众号发布功能。\n\n步骤：\n\n- 首先部署server\n- 在server端配置多个微信凭据，参考文档\n- 让MCP 客户端连接server\n\n对AI说\n\n> 使用phycat主题将这篇文章发布到微信公众号(your_app_id)：./tests/publish.md\n\nDocker 部署\n\n适合不希望安装 Node.js 环境的用户。\n\n \n\n* **Claude Desktop 配置：**：\n\n \n\n> **Docker 配置特别说明：**\n>\n> * **挂载目录 (-v)**：必须将宿主机的文件/图片目录挂载到容器内的 /mnt/host-downloads。\n> * **环境变量 (HOST_FILE_PATH)**：必须与宿主机挂载的文件/图片目录路径保持一致。\n> * **原理**：你的 Markdown 文件/文章内所引用的本地图片应放置在该目录中，Docker 会自动将其映射，使容器能够读取并上传。\n\n如何调试\n\n推荐使用官方 Inspector 进行调试：\n\n \n\n启动成功出现类似提示：\n\n \n\n访问以上链接即可打开调试页面。\n\n \n\n1. 正确填写启动命令\n2. 添加环境变量\n3. 点击 Connect\n4. 选择 Tools -> List Tools\n5. 选择要调试的接口\n6. 填入参数并点击 Run Tool\n7. 查看完整参数\n\n赞助\n\n如果你觉得文颜对你有帮助，可以给我家猫咪买点罐头 ❤️\n\nhttps://yuzhi.tech/sponsor\n\nLicense\n\nApache License Version 2.0', 'readme_raw_len': 3399, 'one_line_zh': '📇 🏠 🍎 🪟 🐧 - 用 Wenyan 排版 Markdown 文章，并发布到微信公众号', 'one_line_plain': 'Convert Markdown articles into formatted posts for WeChat Official Accounts'}, {'id': 5218, 'key': 'autonomous-ai-openharness', 'kind': 'repo', 'name': 'autonomous-ai/openharness', 'one_line': 'The ultimate harness for coding agents and beyond. All your agents. All your machines. One command center. Start with code, then follow your curiosity and build across disciplines: CAD, circuits, robots, games and music.', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'The ultimate harness for coding agents and beyond. All your agents. All your machines. One command center. Start with code, then follow your curiosity and build across disciplines: CAD, circuits, robots, games and music.', 'url': 'https://github.com/autonomous-ai/openharness', 'source': 'github', 'source_url': 'https://github.com/autonomous-ai/openharness', 'downloads': None, 'stars': 1320, 'installs': None, 'mentions': 1, 'score': 105.45, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-12', 'times_seen': 2, 'icon': 'https://github.com/autonomous-ai.png?size=64', 'official_desc': 'The ultimate harness for coding agents and beyond. All your agents. All your machines. One command center. Start with code, then follow your curiosity and build across disciplines: CAD, circuits, robots, games and music.', 'stars_own': 1320, 'stars_creator': None, 'stars_own_repo': 'autonomous-ai/openharness', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Harness like a boss. \n\n \n The ultimate harness for coding agents and beyond. \n Claude Code at work. Codex at home. Hermes in the cloud. One command center. \n Start with code. Then follow your curiosity and build across disciplines: CAD, circuits, robots, games and music.\n \n\n \n Download ·\n How it works ·\n Beyond code ·\n Harnesses ·\n Device \n \n\nEvery agent, side by side\n\nClaude Code, Codex, Cursor, OpenCode, Devin, Amp, Copilot and seven more.\n\n \n\nEvery machine, side by side\n\nYour laptop, home server and GPU box in one window. Link each one with a password.\nNo SSH keys. No Tailscale. No port forwarding.\n\n \n\nKeyboard first\n\n⌘P finds any harness on any machine. ⇧⌘I jumps to the harness waiting on you. ⌘D splits. Every key remaps.\n\n \n\nEnd-to-end encrypted\n\nCode, keys and keystrokes are sealed on your machine. The relay forwards bytes it can't read.\n\n \n\nFast and light\n\nA native app, not Electron. Close it and your agents keep working.\n\nIdle measurements on an M2 Max:\n\n- **⌘N, ⌘O, ⌘T UI:** 11–13 ms median, 15–18 ms p95.\n- **Local terminal echo:** 1.1 ms median, 7.2 ms p95, excluding UI rendering.\n- **Desktop resources:** ~0.1% of one CPU core and 304 MiB, including terminal rendering and scrollback; agent CLIs and the daemon are excluded.\n\nDesktop results use a Release fixture with 16 terminals and 1,000 scrollback lines each.\nSee the workflow and resource benchmarks\nand remote P2P, TURN and relay results\nfor workloads, slow tails, connection failures and raw data.\n\nBuilt the way developers work\n\n- **Real terminals.** Every agent runs in its own tmux pane. Scrollback, colors and keys just work.\n- **Your CLIs, as they are.** Harness never wraps an agent. It reads transcripts and uses the vendor's own hooks.\n- **A worktree per harness.** Start an agent on its own branch. Your working copy stays clean.\n- **Remap every key.** One JSONC file, reloaded on save. Chords up to four strokes.\n- **Local models.** Run open-weight models on your own machines with Grid, Ollama, MLX-LM and vLLM.\n- **Bytes, not pixels.** Remote terminals stream text peer to peer. No remote desktop.\n- **Open source, all of it.** App, CLI, daemon, relay and device, in this repo.\n\nHow it works\n\nIn the workspace, a **harness** is one running session of an agent such as Codex\nor Claude Code, with its own conversation and working context. A **tab** groups\nharnesses. Use **New Harness** to start one and **New Tab** to group work.\nHarnesses in the same tab can message each other with harness send: **Agent Messages**,\nan experiment of your account, off by default and turned on in Settings ▸ Experimental.\n\nThe Store offers **harnesses** with instructions, tools, and optional viewers for\nspecific crafts. Install a harness, then start it in your workspace. See the\nNaming System for the complete naming rules.\n\nOne daemon per machine runs your agents in tmux. It dials out, so no machine opens a port.\n\n \n\nEvery path is sealed end to end: ChaCha20-Poly1305, X25519 session keys, pinned Ed25519 identities.\nHarness picks the best path on its own.\n\n**Direct.** A WebRTC channel between your machines. No server in the path.\n\n \n\n**Through Cloudflare.** When a firewall blocks the direct path, the same encrypted channel runs over\nCloudflare's TURN network. Harness keeps trying for a direct path.\n\n \n\n**Through our relay.** A fallback while WebRTC negotiates. The relay holds no keys and forwards ciphertext.\n\n \n\nThe architecture guide has the details.\n\n \n\nGet started\n\n**Download the app** for macOS or Linux.\n\nAdd a machine. Run this on it, then **Machines → Link Machine** in the app:\n\n \n\n**In a terminal:** the same line installs hn — tmux's keys and your ~/.tmux.conf, with every\nharness on every machine (hn). The first time, hn signs in and connects the\ncomputer:\n\n \n\n**For agents:** Claude Code and Codex can start, wait for and read other harnesses with the harness skill\n(harness.autonomous.ai/agents, which has the Codex lines too):\n\n \n\n \n Build from source \n\nNeeds Node.js 20+, tmux, Xcode and Flutter 3.47+ / Dart 3.13+:\n\n \n\nmake install-cli installs this checkout's CLI and restarts the local daemon. See the\ndevelopment guide.\n\n \n\n \n\nBeyond code: Build across disciplines\n\n> “World-class entrepreneurs are polymaths.” — Peter Thiel\n\nCoding agents can build far more than software. Give one a harness and it works with the real\ntools of a craft. You steer in a live viewer. Every clip below is a real session.\n\nBeyond code: Design\n\n**Blender.** Ask for a lamp and the sliders that matter. Turn them and Blender rebuilds the geometry. Keep the versions you love.\n\n \n\nBeyond code: Circuits\n\n**CircuitJS.** Build a filter. Change one resistor. Overlay the new trace, measure the difference and keep both.\n\n \n\nBeyond code: Robotics\n\n**MuJoCo.** Pin a moment in a robot's run. Shove it with 100 N. Watch two futures split.\n\n \n\nBeyond code: Games\n\n**Godogen.** Your agent makes a playable game. Miss a jump, rewind, try again. Pin the moment so the agent sees what you mean.\n\n \n\nBeyond code: Music\n\n**Strudel.** The track is code you can perform. Bring voices in and out, mark the good parts, keep the WAV. Hear it.\n\n \n\nBeyond code: Chemistry\n\n**RDKit.** Turn a bond and watch the molecule move. Follow the real energy curve. Keep the pose worth a closer look.\n\n \n\nBeyond code: Documents\n\n**Typst.** Your agent writes a real PDF. Circle a detail, quote a line, leave a note. The next draft answers it.\n\n \n\nDomain-specific harnesses (DSH)\n\nCode is the common medium. Geometry scripts make parts. Netlists make boards. Animation code makes film.\n\nA **domain-specific harness** turns a coding agent into a specialist. It brings the craft's\ninstructions and skills, a pinned toolchain, a project template, checks and a **live viewer**.\nYou chat on one side. The board, the part or the game takes shape on the other.\n\nThe agent does the reasoning. The harness supplies the tools and the view. It's a folder with a\nharness.json, so adding a craft never touches the app.\n\n \n49 harnesses in the Store\n\n| Category | Agents and harnesses |\n|---|---|\n| **Coding** | Claude Code, Codex, Cursor, OpenCode, Pi, Hermes, Command Code, Devin, Muse Code, Amp, Antigravity, GitHub Copilot, Grok Build, Kilo Code, Machine Monitor, Trail of Bits Skills |\n| Essentials | Harness Monitor, Memories |\n| Design | Autonomous Workshop, Blender, Bonsai MCP, Creative Direction, Excalidraw, FreeCAD, Generative Art, OpenSCAD, text-to-cad |\n| Engineering | Autonomous Circuit, CircuitJS, Home Assistant, KiCad, Orca Slicer, Yosys |\n| Media | Comfy MCP, Manim, OpenMontage, Remotion |\n| Music | Ableton AI, JUCE Agent Toolkit, Music Studio, Score, Strudel |\n| Productivity | Marp, Typst |\n| Science & Data | autoresearch-mlx, Data Studio, Lab Bench, marimo, RDKit |\n| Simulation | DimOS, Drone Pilot, Foam-Agent, MuJoCo, SimSkill |\n| Games | Game Master, Godogen, Phaser, Voxel Worlds |\n| Research | Roundtable |\n| Local AI | MLX-LM, Model Manager, Ollama, vLLM |\n\nUpstream open-source tools and original workflows. 10 shared viewers install alongside\nthe harnesses that need them. Unlisted experiments are not shown.\n \n\nBuild your own\n\nThe next harness is the one for your craft. Wrap a tool you love or your team's toolchain.\nIt can live here or in your own repo. From this repo:\n\n \n\n \n\nPress **⌘N → Hello World** and say hello. The authoring guide covers the rest.\n\nHarness device\n\n**Get a Harness device**, or build your own from the files below. The\nfirmware guide lists the supported boards and build\ncommands, and the hardware guide covers the design files.\n\n \n\nThe optional **Harness device** is a round, always-on display that sits beside\nyour keyboard and shows your agents at a glance: what each one is doing, which one has finished, and\nwhich one is waiting on you. Read a question and answer it on the screen, or tap and speak a new task,\nwithout switching windows.\n\nIt is open hardware, all the way down. This repository has everything it takes to build one:\n\n| Layer | What's here |\n|---|---|\n| Firmware | ESP32-S3", 'readme_raw_len': 8692, 'one_line_zh': '面向编程 Agent 及更广场景的终极框架：所有 Agent、所有机器、一个指挥中心，从代码出发探索 CAD、电路、机器人、游戏与音乐', 'one_line_plain': 'Run and control coding agents across disciplines like CAD, circuits, robots, games and music from a single command center'}, {'id': 688, 'key': 'tencentcloudbase-cloudbase-ai-toolkit', 'kind': 'mcp', 'name': 'TencentCloudBase/CloudBase-AI-ToolKit', 'one_line': '📇 ☁️ 🏠 🍎 🪟 🐧 - Backend services for WeChat Mini Programs and full-stack apps: serverless cloud functions,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Backend for AI coding agents on CloudBase — database, auth, functions via Plugin, Skills & MCP.', 'url': 'https://github.com/TencentCloudBase/CloudBase-AI-ToolKit', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/TencentCloudBase/CloudBase-AI-ToolKit', 'downloads': None, 'stars': 1140, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/TencentCloudBase.png?size=64', 'official_desc': '📇 ☁️ 🏠 🍎 🪟 🐧 - Backend services for WeChat Mini Programs and full-stack apps: serverless cloud functions, databases and one-click deployment.', 'stars_own': 1140, 'stars_creator': None, 'stars_own_repo': 'TencentCloudBase/CloudBase-AI-ToolKit', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "CloudBase AI Toolkit\n\n**AI writes the code. CloudBase runs the backend.**\n\nThe CloudBase integration layer for AI coding tools: Plugin installs the stack, Skills steer how code is written, MCP operates databases, functions, storage, and deploys from chat.\n\n**English** · 简体中文 · [Docs][docs] · [Changelog][changelog] · [Issues][github-issues-link]\n\n[![][npm-version-shield]][npm-link]\n[![][npm-downloads-shield]][npm-link]\n[![][github-stars-shield]][github-stars-link]\n[![][github-forks-shield]][github-forks-link]\n[![][github-issues-shield]][github-issues-link]\n![][github-license-shield]\n![][github-contributors-shield]\n[![][cnb-shield]][cnb-link]\n[![][deepwiki-shield]][deepwiki-link]\n\n \n\nRecent updates\n\n**v2.35.x** (2026-10)\n\n- Hosted / Auth: each hosted MCP request runs on its own transport with client metadata cached per credential, and a project-level credential now takes precedence over the account login state\n- Tools / Skills: MySQL provisioning is retired from the tool surface; every skill package ships a LICENSE.md next to SKILL.md; a new MCP reference skill documents the remote endpoints, the plugin flags and the tool schemas\n- Feedback: prepareFeedback returns the repository's new-issue page instead of an auto-filled error report, and a hosted server now enables the same default plugin set as a local one\n- Onboarding / Docs: every initialize response now carries server instructions — the call to make first, the order a first run needs, and prompts that can be copied verbatim; the remote setup docs now lead with OAuth, so the URL alone is the configuration and a key pair is the CI path\n\n**v2.34.x** (2026-09)\n\n- i18n / IDE: full tool-copy localization with an instance-level lang, plus auth site / region params so international-site login and region routing resolve correctly\n- Cloud API: callCloudApi service allowlist widened to 57 with built-in version mapping (multi-version services such as tke / mongodb / vod require an explicit version)\n- Deploy / Env: new appBuild tool with hosting build neutralization; queryEnv reports the region actually applied and domains honors a passed envId\n- Skills / Docs: skill fallback reads now point at the official distribution repo with a references address list; SDK-first database decision gate for cloudrun; site doc links moved to the current Markdown addresses; post-deployment share offered after delivery in the expert packs and the deploy skills (opt-in, redacted, at most once)\n- Deploy / Apps: the cloud upload channel now completes end to end (deployApp accepts the timestamp getUploadUrl returns, getBuildLog accepts the build ID a deploy returns), and gateway route creation verifies the upstream exists before writing\n- Skills / Context: new PostgreSQL access-pattern best-practices skill (batching, indexes, RLS role gating, launch capacity); searchKnowledgeBase's inline skill / OpenAPI catalogs now load on demand, cutting the 43-tool surface by 9.1% on every tools/list\n- Runtime / Hosting: CloudRun Function mode must not bind PORT (the function framework does) and gets a credential decision gate naming CLOUDBASE_APIKEY; hosting paths and prefixes are normalized so a leading slash can no longer read as an empty directory\n- Connectors: the international-site WorkBuddy connector (cloudbase-intl) is built from the same config/source/** corpus as the domestic one — remote streamableHttp with standard MCP OAuth instead of local stdio, with China-site hosts rewritten to their international equivalents\n\n[Releases][changelog] · [Star][github-stars-link] · Watch → Releases\n\nWhat it is\n\nAI IDEs (Cursor, Claude Code, Codex, CodeBuddy, and others) are strong at generating code. What usually blocks you is the backend: schemas, permissions, functions, storage, environments, and release.\n\nCloudBase is Tencent Cloud’s AI-native all-in-one backend (database, storage, auth, cloud functions, Cloud Run, and more). This repo is the Toolkit that connects that backend to AI tools:\n\n| Piece | Role |\n|------|------|\n| **Plugin** | Installs MCP Server, Agent Skills, and Hooks together—less per-IDE wiring |\n| **Agent Skills** | Scenario skills (Web / Mini Program / database / auth / functions, etc.) toward workable CloudBase practice |\n| **MCP** | Login, query and change data, manage functions and hosting, read logs—from the conversation |\n\nThis repository ships the npm package @cloudbase/cloudbase-mcp, Skills, and AI plugins.\n\nYou still need your own CloudBase environment, and you should confirm sensitive actions the AI proposes. The Toolkit provides capability and path—not judgment.\n\nRelated repositories\n\nPublishing and sync repos live under TencentCloudBase. Directly related to this Toolkit:\n\n| Repository | Contents | Typical entry |\n|------|------|----------|\n| CloudBase-AI-Toolkit (this repo) | MCP Server source; marketplace source for Claude Code / Codex | npx @cloudbase/cloudbase-mcp@latest |\n| dsh-plugin | DeepSeek Harness plugin (@cloudbase/dsh-plugin): MCP bridge + DB/Storage/Auth panel | dsh plugin add @cloudbase/dsh-plugin |\n| cloudbase-plugin | Open Plugin Spec publish repo (CI-synced): MCP + Skills + Hooks | npx plugins add TencentCloudBase/cloudbase-plugin · CNB fallback: npx plugins add https://cnb.cool/tencent/cloud/cloudbase/cloudbase-plugin.git |\n| cloudbase-sites-plugin | Sites plugin: Vite Web create & deploy | npx plugins add TencentCloudBase/cloudbase-sites-plugin · CNB: https://cnb.cool/tencent/cloud/cloudbase/cloudbase-sites-plugin.git |\n| cloudbase-skills | Agent Skills collection | npx skills add TencentCloudBase/cloudbase-skills · CNB: https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills.git |\n| skills | Per-skill install catalog (also on skills.sh) | npx skills add tencentcloudbase/skills --skill |\n| awesome-cloudbase-examples | CloudBase examples and case studies | Browse / clone examples |\n| OpenVibeCoding | Vibecoding template on CloudBase | Use as a project starter |\n\nPrefer Plugin for the full stack; Skills alone when you only need knowledge constraints. For marketplace IDEs use this repo—do not also run npx plugins add on the same tool.\n\nQuick start\n\nFastest way to get started\n\nCopy this AI prompt into your AI IDE. The agent reads skill.md and completes the setup:\n\n \n\n \n Pick one default path for your tool (Plugin / CLI / MCP) \n\n| Your tool | Suggested path |\n|----------|----------|\n| Claude Code / Codex (native marketplace) | Add this repo as marketplace, then install the cloudbase plugin (plugin docs) |\n| Open Plugin Spec tools | npx plugins add TencentCloudBase/cloudbase-plugin |\n| Prefer one CLI for many tools | CloudBase AI CLI: npm i -g @cloudbase/cli && tcb ai |\n| CodeBuddy / WorkBuddy / Doubao / MiniMax Code / DeepSeek Harness / ZCode / Kimi (built-in) | Use the IDE's built-in CloudBase plugin or connector; for CodeBuddy you can also install via plugin marketplace |\n| Other MCP-capable IDEs | MCP config only (below) |\n\nPlugin\n\n \n\nDetails and IDE differences: AI plugin docs.\n\nMCP only\n\n \n\nRemote HTTP, self-hosted Cloud Mode, and plugin scoping: Install & connect.\n\n \n\nFirst prompts\n\n \n\n \n\nSkills shape structure and practice; MCP handles environment and resources. You should be able to verify data and APIs in your own environment—not only get local source files.\n\nSupported AI IDEs\n\n \n\n| Tool | Platform | Guide |\n|------|------|------|\n| CloudBase AI CLI | CLI | Guide |\n| OpenClaw | CLI | Guide |\n| WorkBuddy | Standalone IDE | Guide |\n| Doubao | Desktop & web (plugin marketplace) | Guide |\n| MiniMax Code | Desktop app & terminal CLI (plugin marketplace) | Guide |\n| DeepSeek Harness | Desktop app (plugin) | Guide |\n| ZCode | Standalone IDE (≥ 3.4.1 built-in) | Guide |\n| Kimi Code | CLI (plugin marketplace) | Guide |\n| Kimi Work | Desktop app (plugin panel) | Guide |\n| Codex App | App | Guide |\n| Cursor | Standalone IDE | Guide |\n| WindSurf | IDE / plugins | Guide |\n| CodeBuddy | Standalone IDE (built-in) | Guide |\n| CLINE | VS Code plugin | Guide |\n| GitHub Copilot | VS Code plugin | Guide |\n| T", 'readme_raw_len': 17055, 'one_line_zh': '📇 ☁️ 🏠 🍎 🪟 🐧 - 微信小程序与全栈应用的后端服务：无服务器云函数、…', 'one_line_plain': 'Backend services for WeChat Mini Programs and full-stack apps: serverless cloud functions'}, {'id': 2565, 'key': 'chatmcp-mcp-server-chatsum', 'kind': 'mcp', 'name': 'chatmcp/mcp-server-chatsum', 'one_line': 'Query and summarize your chat messages with AI prompts', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Query and Summarize your chat messages.', 'url': 'https://github.com/chatmcp/mcp-server-chatsum', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/chatmcp/mcp-server-chatsum', 'downloads': None, 'stars': 1027, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/chatmcp.png?size=64', 'official_desc': 'Query and summarize your chat messages with AI prompts.', 'stars_own': 1027, 'stars_creator': None, 'stars_own_repo': 'chatmcp/mcp-server-chatsum', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'mcp-server-chatsum\n\nThis MCP Server is used to summarize your chat messages.\n\n中文说明\n\n \n\n> **Before you start**\n>\n> move to chatbot directory, follow the README to setup the chat database.\n>\n> start chatbot to save your chat messages.\n\nFeatures\n\nResources\n\nTools\n\n- query_chat_messages - Query chat messages\n - Query chat messages with given parameters\n - Summarize chat messages based on the query prompt\n\nPrompts\n\nDevelopment\n\n1. Set up environment variables:\n\ncreate .env file in the root directory, and set your chat database path.\n\n \n\n2. Install dependencies:\n\n \n\nBuild the server:\n\n \n\nFor development with auto-rebuild:\n\n \n\nInstallation\n\nTo use with Claude Desktop, add the server config:\n\nOn MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n\nDebugging\n\nSince MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:\n\n \n\nThe Inspector will provide a URL to access debugging tools in your browser.\n\nCommunity\n\n- MCP Server Telegram\n- MCP Server Discord\n\nAbout the author\n\n- idoubi', 'readme_raw_len': 1151, 'one_line_zh': '用 AI Prompt 查询并总结你的聊天消息', 'one_line_plain': 'Query and summarize your chat messages with AI prompts'}, {'id': 946, 'key': 'line-line-bot-mcp-server', 'kind': 'mcp', 'name': 'line/line-bot-mcp-server', 'one_line': '🎖 📇 ☁️ - Integrate with a LINE Official Account', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server that integrates the LINE Messaging API to connect an AI Agent to the LINE Official Account.', 'url': 'https://github.com/line/line-bot-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/line/line-bot-mcp-server', 'downloads': None, 'stars': 789, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/line.png?size=64', 'official_desc': '🎖 📇 ☁️ - Integrate with a LINE Official Account.', 'stars_own': 789, 'stars_creator': None, 'stars_own_repo': 'line/line-bot-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "日本語版 READMEはこちら\n\nLINE Bot MCP Server\n\n \n\nModel Context Protocol (MCP) server implementation that integrates the LINE Messaging API to connect an AI Agent to the LINE Official Account.\n\n \n\n> [!NOTE]\n> This repository is provided as a preview version. While we offer it for experimental purposes, please be aware that it may not include complete functionality or comprehensive support.\n\nTools\n\n1. **push_text_message**\n - Push a simple text message to a user via LINE.\n - **Inputs:**\n - userId (string?): The user ID to receive a message. Defaults to DESTINATION_USER_ID. Either userId or DESTINATION_USER_ID must be set.\n - message.text (string): The plain text content to send to the user.\n2. **push_flex_message**\n - Push a highly customizable flex message to a user via LINE.\n - **Inputs:**\n - userId (string?): The user ID to receive a message. Defaults to DESTINATION_USER_ID. Either userId or DESTINATION_USER_ID must be set.\n - message.altText (string): Alternative text shown when flex message cannot be displayed.\n - message.contents (any): The contents of the flex message. This is a JSON object that defines the layout and components of the message.\n - message.contents.type (enum): Type of the container. 'bubble' for single container, 'carousel' for multiple swipeable bubbles.\n3. **broadcast_text_message**\n - Broadcast a simple text message via LINE to all users who have followed your LINE Official Account.\n - **Inputs:**\n - message.text (string): The plain text content to send to the users.\n4. **broadcast_flex_message**\n - Broadcast a highly customizable flex message via LINE to all users who have added your LINE Official Account.\n - **Inputs:**\n - message.altText (string): Alternative text shown when flex message cannot be displayed.\n - message.contents (any): The contents of the flex message. This is a JSON object that defines the layout and components of the message.\n - message.contents.type (enum): Type of the container. 'bubble' for single container, 'carousel' for multiple swipeable bubbles.\n5. **get_profile**\n - Get detailed profile information of a LINE user including display name, profile picture URL, status message and language.\n - **Inputs:**\n - userId (string?): The ID of the user whose profile you want to retrieve. Defaults to DESTINATION_USER_ID.\n6. **get_message_quota**\n - Get the message quota and consumption of the LINE Official Account. This shows the monthly message limit and current usage.\n - **Inputs:**\n - None\n7. **get_rich_menu_list**\n - Get the list of rich menus associated with your LINE Official Account.\n - **Inputs:**\n - None\n8. **delete_rich_menu**\n - Delete a rich menu from your LINE Official Account.\n - **Inputs:**\n - richMenuId (string): The ID of the rich menu to delete.\n9. **set_rich_menu_default**\n - Set a rich menu as the default rich menu.\n - **Inputs:**\n - richMenuId (string): The ID of the rich menu to set as default.\n10. **cancel_rich_menu_default**\n - Cancel the default rich menu.\n - **Inputs:**\n - None\n11. **create_rich_menu**\n - Create a rich menu based on the given actions. Generate and upload an image. Set as default.\n - **Inputs:**\n - chatBarText (string): Text displayed in chat bar, also used as rich menu name.\n - actions (array): The actions of the rich menu. You can specify minimum 1 to maximum 6 actions. Each action can be one of the following types:\n - postback: For sending a postback action\n - message: For sending a text message\n - uri: For opening a URL\n - datetimepicker: For opening a date/time picker\n - camera: For opening the camera\n - cameraRoll: For opening the camera roll\n - location: For sending the current location\n - richmenuswitch: For switching to another rich menu\n - clipboard: For copying text to clipboard\n\n12. **get_follower_ids**\n - Get a list of user IDs of users who have added the LINE Official Account as a friend. This allows you to obtain user IDs for sending messages without manually preparing them.\n - **Inputs:**\n - start (string?): Continuation token to get the next array of user IDs. Returned in the next property of a previous response.\n - limit (number?): The maximum number of user IDs to retrieve in a single request.\n13. **get_group_summary**\n - Get the group chat summary including group ID, group name, and group icon URL, using the group ID.\n - **Inputs:**\n - groupId (string): The group ID of the target group chat.\n\nInstallation (Using npx)\n\nrequirements:\n- Node.js v22 or later\n\nStep 1: Create LINE Official Account\n\nThis MCP server utilizes a LINE Official Account. If you do not have one, please create it by following this instructions. \n\nIf you have a LINE Official Account, enable the Messaging API for your LINE Official Account by following this instructions.\n\nStep 2: Configure AI Agent\n\nPlease add the following configuration for an AI Agent like Claude Desktop or Cline. \n\nSet the environment variables or arguments as follows:\n\n- CHANNEL_ACCESS_TOKEN: (required) Channel Access Token. You can confirm this by following this instructions.\n- DESTINATION_USER_ID: (optional) The default user ID of the recipient. If the Tool's input does not include userId, DESTINATION_USER_ID is required. You can confirm this by following this instructions.\n\n \n\nInstallation (Using Docker)\n\nStep 1: Create LINE Official Account\n\nThis MCP server utilizes a LINE Official Account. If you do not have one, please create it by following this instructions.\n\nIf you have a LINE Official Account, enable the Messaging API for your LINE Official Account by following this instructions.\n\nStep 2: Build line-bot-mcp-server image\n\nClone this repository:\n\n \n\nBuild the Docker image:\n\n \n\nStep 3: Configure AI Agent\n\nPlease add the following configuration for an AI Agent like Claude Desktop or Cline.\n\nSet the environment variables or arguments as follows:\n\n- mcpServers.args: (required) The path to line-bot-mcp-server.\n- CHANNEL_ACCESS_TOKEN: (required) Channel Access Token. You can confirm this by following this instructions.\n- DESTINATION_USER_ID: (optional) The default user ID of the recipient. If the Tool's input does not include userId, DESTINATION_USER_ID is required.\nYou can confirm this by following this instructions.\n\n \n\nLocal Development with Inspector\n\nYou can use the MCP Inspector to test and debug the server locally.\n\nPrerequisites\n\n1. Clone the repository:\n \n\n2. Install dependencies:\n \n\n3. Build the project:\n \n\nRun the Inspector\n\nAfter building the project, you can start the MCP Inspector:\n\n \n\nThis will start the MCP Inspector interface where you can interact with the LINE Bot MCP Server tools and test their functionality.\n\nVersioning\n\nThis project respects semantic versioning\n\nSee http://semver.org/\n\nContributing\n\nPlease check CONTRIBUTING before making a contribution.", 'readme_raw_len': 6735, 'one_line_zh': '🎖 📇 ☁️ - 对接 LINE 官方账号', 'one_line_plain': 'Integrate with a LINE Official Account'}, {'id': 4309, 'key': 'tevonsb-homeassistant-mcp', 'kind': 'mcp', 'name': 'tevonsb/homeassistant-mcp', 'one_line': '📇 🏠 - Access Home Assistant data and control devices such as lights, switches and thermostats', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A MCP server for Home Assistant', 'url': 'https://github.com/tevonsb/homeassistant-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/tevonsb/homeassistant-mcp', 'downloads': None, 'stars': 576, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/tevonsb.png?size=64', 'official_desc': '📇 🏠 - Access Home Assistant data and control devices such as lights, switches and thermostats.', 'stars_own': 576, 'stars_creator': None, 'stars_own_repo': 'tevonsb/homeassistant-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Model Context Protocol Server for Home Assistant\n\nThe server uses the MCP protocol to share access to a local Home Assistant instance with an LLM application.\n\nA powerful bridge between your Home Assistant instance and Language Learning Models (LLMs), enabling natural language control and monitoring of your smart home devices through the Model Context Protocol (MCP). This server provides a comprehensive API for managing your entire Home Assistant ecosystem, from device control to system administration.\n\n \n \n \n \n \n \n\nFeatures\n\n- 🎮 **Device Control**: Control any Home Assistant device through natural language\n- 🔄 **Real-time Updates**: Get instant updates through Server-Sent Events (SSE)\n- 🤖 **Automation Management**: Create, update, and manage automations\n- 📊 **State Monitoring**: Track and query device states\n- 🔐 **Secure**: Token-based authentication and rate limiting\n- 📱 **Mobile Ready**: Works with any HTTP-capable client\n\nReal-time Updates with SSE\n\nThe server includes a powerful Server-Sent Events (SSE) system that provides real-time updates from your Home Assistant instance. This allows you to:\n\n- 🔄 Get instant state changes for any device\n- 📡 Monitor automation triggers and executions\n- 🎯 Subscribe to specific domains or entities\n- 📊 Track service calls and script executions\n\nQuick SSE Example\n\n \n\nSee SSE_API.md for complete documentation of the SSE system.\n\nTable of Contents\n\n- Key Features\n- Prerequisites\n- Installation\n - Basic Setup\n - Docker Setup (Recommended)\n- Configuration\n- Development\n- API Reference\n - Device Control\n - Add-on Management\n - Package Management\n - Automation Management\n- Natural Language Integration\n- Troubleshooting\n- Project Status\n- Contributing\n- Resources\n- License\n\nKey Features\n\nCore Functionality 🎮\n- **Smart Device Control**\n - 💡 **Lights**: Brightness, color temperature, RGB color\n - 🌡️ **Climate**: Temperature, HVAC modes, fan modes, humidity\n - 🚪 **Covers**: Position and tilt control\n - 🔌 **Switches**: On/off control\n - 🚨 **Sensors & Contacts**: State monitoring\n - 🎵 **Media Players**: Playback control, volume, source selection\n - 🌪️ **Fans**: Speed, oscillation, direction\n - 🔒 **Locks**: Lock/unlock control\n - 🧹 **Vacuums**: Start, stop, return to base\n - 📹 **Cameras**: Motion detection, snapshots\n\nSystem Management 🛠️\n- **Add-on Management**\n - Browse available add-ons\n - Install/uninstall add-ons\n - Start/stop/restart add-ons\n - Version management\n - Configuration access\n\n- **Package Management (HACS)**\n - Integration with Home Assistant Community Store\n - Multiple package types support:\n - Custom integrations\n - Frontend themes\n - Python scripts\n - AppDaemon apps\n - NetDaemon apps\n - Version control and updates\n - Repository management\n\n- **Automation Management**\n - Create and edit automations\n - Advanced configuration options:\n - Multiple trigger types\n - Complex conditions\n - Action sequences\n - Execution modes\n - Duplicate and modify existing automations\n - Enable/disable automation rules\n - Trigger automation manually\n\nArchitecture Features 🏗️\n- **Intelligent Organization**\n - Area and floor-based device grouping\n - State monitoring and querying\n - Smart context awareness\n - Historical data access\n\n- **Robust Architecture**\n - Comprehensive error handling\n - State validation\n - Secure API integration\n - TypeScript type safety\n - Extensive test coverage\n\nPrerequisites\n\n- **Node.js** 20.10.0 or higher\n- **NPM** package manager\n- **Docker Compose** for containerization\n- Running **Home Assistant** instance\n- Home Assistant long-lived access token (How to get token)\n- **HACS** installed for package management features\n- **Supervisor** access for add-on management\n\nInstallation\n\nBasic Setup\n\n \n\nDocker Setup (Recommended)\n\nThe project includes Docker support for easy deployment and consistent environments across different platforms.\n\n1. **Clone the repository:**\n \n\n2. **Configure environment:**\n \n Edit the .env file with your Home Assistant configuration:\n \n\n3. **Build and run with Docker Compose:**\n \n\n4. **Verify the installation:**\n The server should now be running at http://localhost:3000. You can check the health endpoint at http://localhost:3000/health.\n\n5. **Update the application:**\n \n\nDocker Configuration\n\nThe Docker setup includes:\n- Multi-stage build for optimal image size\n- Health checks for container monitoring\n- Volume mounting for environment configuration\n- Automatic container restart on failure\n- Exposed port 3000 for API access\n\nDocker Compose Environment Variables\n\nAll environment variables can be configured in the .env file. The following variables are supported:\n- HASS_HOST: Your Home Assistant instance URL\n- HASS_TOKEN: Long-lived access token for Home Assistant\n- HASS_SOCKET_URL: WebSocket URL for Home Assistant\n- PORT: Server port (default: 3000)\n- NODE_ENV: Environment (production/development)\n- DEBUG: Enable debug mode (true/false)\n\nConfiguration\n\nEnvironment Variables\n\n \n\nConfiguration Files\n\n1. **Development**: Copy .env.example to .env.development\n2. **Production**: Copy .env.example to .env.production\n3. **Testing**: Copy .env.example to .env.test\n\nAdding to Claude Desktop (or other clients)\n\nTo use your new Home Assistant MCP server, you can add Claude Desktop as a client. Add the following to the configuration. Note this will run the MCP within claude and does not work with the Docker method.\n\n \n\nAPI Reference\n\nDevice Control\n\nCommon Entity Controls\n \n\nLight Control\n \n\nAdd-on Management\n\nList Available Add-ons\n \n\nInstall Add-on\n \n\nManage Add-on State\n \n\nPackage Management\n\nList HACS Packages\n \n\nInstall Package\n \n\nAutomation Management\n\nCreate Automation\n \n\nDuplicate Automation\n \n\nCore Functions\n\nState Management\n \n\nManages the current state of the system.\n\n**Example Request:**\n \n\nContext Updates\n \n\nUpdates the current context with new information.\n\n**Example Request:**\n \n\nAction Endpoints\n\nExecute Action\n \n\nExecutes a specified action with given parameters.\n\n**Example Request:**\n \n\nBatch Actions\n \n\nExecutes multiple actions in sequence.\n\n**Example Request:**\n \n\nQuery Functions\n\nGet Available Actions\n \n\nReturns a list of all available actions.\n\n**Example Response:**\n \n\nContext Query\n \n\nRetrieves context information.\n\n**Example Response:**\n \n\nWebSocket Events\n\nThe server supports real-time updates via WebSocket connections.\n\n \n\nSupported Events\n\n- state_change: Emitted when system state changes\n- context_update: Emitted when context is updated\n- action_executed: Emitted when an action is completed\n- error: Emitted when an error occurs\n\n**Example Event Data:**\n \n\nError Handling\n\nAll endpoints return standard HTTP status codes:\n\n- 200: Success\n- 400: Bad Request\n- 401: Unauthorized\n- 403: Forbidden\n- 404: Not Found\n- 500: Internal Server Error\n\n**Error Response Format:**\n \n\nRate Limiting\n\nThe API implements rate limiting to prevent abuse:\n\n- 100 requests per minute per IP for regular endpoints\n- 1000 requests per minute per IP for WebSocket connections\n\nWhen rate limit is exceeded, the server returns:\n\n \n\nExample Usage\n\nUsing curl\n \n\nUsing JavaScript\n \n\nDevelopment\n\n \n\nTroubleshooting\n\nCommon Issues\n\n1. **Node.js Version (toSorted is not a function)**\n - **Solution:** Update to Node.js 20.10.0+\n \n\n2. **Connection Issues**\n - Verify Home Assistant is running\n - Check HASS_HOST accessibility\n - Validate token permissions\n - Ensure WebSocket connection for real-time updates\n\n3. **Add-on Management Issues**\n - Verify Supervisor access\n - Check add-on compatibility\n - Validate system resources\n\n4. **HACS Integration Issues**\n - Verify HACS installation\n - Check HACS integration status\n - Validate repository access\n\n5. **Automation Issues**\n - Verify entity availability\n - Check trigger conditions\n - Validate service calls\n - Monitor execution logs\n\nProject Status\n\n✅ **Complete**\n- Entity, Floor, and Area access\n- Device control (Lights, Climate, Covers, Switches, Contacts)\n- Add-on management system\n- Package management through HACS\n- Advanced automation configuration\n- Basic stat', 'readme_raw_len': 8931, 'one_line_zh': '访问 Home Assistant 数据，控制灯光、开关和恒温器等设备', 'one_line_plain': 'Access Home Assistant data and control devices such as lights, switches and thermostats'}, {'id': 929, 'key': 'joinly-ai-joinly', 'kind': 'mcp', 'name': 'joinly-ai/joinly', 'one_line': '🐍 ☁️ - Send bots to browser-based meetings (Zoom, Teams, Google Meet) to gather live transcripts, speak text…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Make your meetings accessible to AI Agents', 'url': 'https://github.com/joinly-ai/joinly', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/joinly-ai/joinly', 'downloads': None, 'stars': 569, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/joinly-ai.png?size=64', 'official_desc': '🐍 ☁️ - Send bots to browser-based meetings (Zoom, Teams, Google Meet) to gather live transcripts, speak text and post in the meeting chat.', 'stars_own': 569, 'stars_creator': None, 'stars_own_repo': 'joinly-ai/joinly', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Make your meetings accessible to AI Agents 🤖 \n\n**joinly.ai** is a connector middleware designed to enable AI agents to join and actively participate in video calls. Through its MCP server, joinly.ai provides essential meeting tools and resources that can equip any AI agent with the skills to perform tasks and interact with you in real time during your meetings.\n\n> Want to dive right in? Jump to the Quickstart!\n> Want to know more? Visit our website!\n\n> [!IMPORTANT] \n> Don\'t want the hustle of setting everything up? Try our cloud first! ☁️🚀\n\n:sparkles: Features\n- **Live Interaction**: Lets your agents execute tasks and respond in real-time by voice or chat within your meetings\n- **Conversational flow**: Built-in logic that ensures natural conversations by handling interruptions and multi-speaker interactions\n- **Cross-platform**: Join Google Meet, Zoom, and Microsoft Teams (or any available over the browser)\n- **Bring-your-own-LLM**: Works with all LLM providers (also locally with Ollama)\n- **Choose-your-preferred-TTS/STT**: Modular design supports multiple services - Whisper/Deepgram for STT and Kokoro/ElevenLabs/Deepgram for TTS (and more to come...)\n- **100% open-source, self-hosted and privacy-first** :rocket:\n\n:video_camera: Demos\nGitHub\n \n> In this demo video, joinly answers the question \'What is Joinly?\' by accessing the latest news from the web. It then creates an issue in a GitHub demo repository.\nNotion\n \n> In this demo video, we connect joinly to our notion via MCP and let it edit the content of a page content live in the meeting. \n\nAny ideas what we should build next? Write us! :rocket:\n\n:zap: Quickstart\nRun joinly via Docker with a basic conversational agent client.\n\n> [!IMPORTANT]\n> **Prerequisites**: Docker installation\n\nCreate a new folder joinly or clone this repository (not mandatory for the following steps). In this directory, create a new .env file with a valid API key for the LLM provider you want to use, e.g. OpenAI:\n\n> [!TIP]\n> You can find the OpenAI API key here\n\n \n\n> [!NOTE]\n> See .env.example for complete configuration options including Anthropic (Claude) and Ollama setups. Replace the placeholder values with your actual API keys and adjust the model name as needed. Delete the placeholder values of the providers you don\'t use.\n\nPull the Docker image (~2.3GB since it packages browser and models):\n \n\nLaunch your meeting in Zoom, Google Meet or Teams and let joinly join the meeting using the meeting link as . Then, run the following command from the folder where you created the .env file:\n \n> :red_circle: Having trouble getting started? Let\'s figure it out together on our discord! \n\n:technologist: Run a custom client\nIn Quickstart, we ran the Docker Container directly as a client using --client. But we can also run it as a server and connect to it from outside the container, which allows us to connect other MCP servers. Here, we run a custom client using the joinly-client package and connect it to the joinly MCP server.\n\n> [!IMPORTANT]\n> **Prerequisites**: do the Quickstart (except the last command), install uv, and open two terminals\n\nStart the joinly server in the first terminal (note, we are not using --client here and publish port 8000 on localhost only):\n \n\n> [!WARNING]\n> The joinly MCP server has no authentication and accepts client-supplied configuration, so it is meant to run locally with a single trusted client. Bind it to localhost (as above) and do not expose the port to a network.\n\nWhile the server is running, start the example client implementation in the second terminal window to connect to it and join a meeting:\n \n\nAdd MCP servers to the client\nAdd the tools of any MCP server to the agent by providing a JSON configuration. The configuration file can contain multiple entries under "mcpServers" which will all be available as tools in the meeting (see fastmcp client docs for config syntax):\n\n \n\nAdd for example a Tavily config for web searching, then run the client using the config file, here named config.json:\n\n \n\n:wrench: Configurations\n\nConfigurations can be given via env variables and/or command line args. Here is a list of common configuration options, which can be used when starting the docker container:\n \n\nAlternatively, you can pass --name, --lang, and provider settings as command line arguments using joinly-client, which will override settings of the server:\n \n\nBasic Settings\n\nIn general, the docker image provides an MCP server which is started by default. But to quickly get started, we also include a client implementation that can be used via --client. Note, in this case no server is started and no other client can connect to it.\n\n \n\nProviders\n\nText-to-Speech\n \n\nTranscription\n \n\nDebugging\n \n\nGPU Support\n\nWe provide a Docker image with CUDA GPU support for running the transcription and TTS models on a GPU. To use it, you need to have the NVIDIA Container Toolkit installed and CUDA >= 12.6. Then pull the CUDA-enabled image:\n \n\nRun as client or server with the same commands as above, but use the joinly:{version}-cuda image and set --gpus all:\n \n\nBy default, the joinly image uses the Whisper model base for transcription, since it still runs reasonably fast on CPU. For cuda, it automatically defaults to distil-large-v3 for significantly better transcription quality. You can change the model by setting --stt-arg model_name= (e.g., --stt-arg model_name=large-v3). However, only the respective default models are packaged in the docker image, so it will start to download the model weights on container start.\n\n:test_tube: Create your own agent\n\nYou can also write your own agent and connect it to our joinly MCP server. See the code examples for the joinly-client package or the client_example.py if you want a starting point that doesn\'t depend on our framework.\n\nThe joinly MCP server provides following tools and resources:\n\nTools\n\n- **join_meeting** - Join meeting with URL, participant name, and optional passcode\n- **leave_meeting** - Leave the current meeting\n- **speak_text** - Speak text using TTS (requires text parameter)\n- **send_chat_message** - Send chat message (requires message parameter)\n- **mute_yourself** - Mute microphone\n- **unmute_yourself** - Unmute microphone\n- **get_chat_history** - Get current meeting chat history in JSON format\n- **get_participants** - Get current meeting participants in JSON format\n- **get_transcript** - Get current meeting transcript in JSON format, optionally filtered by minutes\n- **get_video_snapshot** - Get an image from the current meeting, e.g., view a current screenshare\n\nResources\n\n- **transcript://live** - Live meeting transcript in JSON format, including timestamps and speaker information. Subscribable for real-time updates when new utterances are added.\n\n:building_construction: Developing joinly.ai\n\nFor development we recommend using the development container, which installs all necessary dependencies. To get started, install the DevContainer Extension for Visual Studio Code, open the repository and choose **Reopen in Container**.\n\n \n\nThe installation can take some time, since it downloads all packages as well as models for Whisper/Kokoro and the Chromium browser. At the end, it automatically invokes the download_assets.py script. If you see errors like Missing kokoro-v1.0.onnx, run this script manually using:\n \n\nWe\'d love to see what you are using it for or building with it. Showcase your work on our discord\n:pencil2: Roadmap\n\n**Meeting**\n- [x] Meeting chat access\n- [ ] Camera in video call with status updates\n- [ ] Enable screen share during video conferences\n- [ ] Participant metadata and joining/leaving\n- [ ] Improve browser agent capabilities\n\n**Conversation**\n- [x] Speaker attribute for transcription\n- [ ] Improve client memory: reduce token usage, allow persistence across meetings\nevents\n- [ ] Improve End-of-Utterance/turn-taking detection\n- [ ] Human approval mechanism from inside the meeting\n\n**Integrations**\n- [ ] Showcase how to add agents using the A2A protoc', 'readme_raw_len': 8821, 'one_line_zh': '🐍 ☁️ - 派机器人加入浏览器会议（Zoom、Teams、Google Meet），收集实时转录、朗读文本…', 'one_line_plain': 'Send bots to browser-based meetings (Zoom, Teams, Google Meet) to gather transcripts and speak text'}, {'id': 4206, 'key': 'evalstate-mcp-hfspace', 'kind': 'mcp', 'name': 'evalstate/mcp-hfspace', 'one_line': '📇 ☁️ - Use Hugging Face Spaces for open-source image generation, chat, vision and more, with image, audio and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP Server to Use HuggingFace spaces, easy configuration and Claude Desktop mode.', 'url': 'https://github.com/evalstate/mcp-hfspace', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/evalstate/mcp-hfspace', 'downloads': None, 'stars': 389, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/evalstate.png?size=64', 'official_desc': '📇 ☁️ - Use Hugging Face Spaces for open-source image generation, chat, vision and more, with image, audio and text uploads and downloads.', 'stars_own': 389, 'stars_creator': None, 'stars_own_repo': 'evalstate/mcp-hfspace', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'mcp-hfspace MCP Server 🤗\n\n> [!TIP]\n>\n> You can access and configure Hugging Face MCP services directly at https://hf.co/mcp, including Gradio spaces.\n>\n> This project has been superceded by the official Hugging Face MCP Server and Gradio MCP Endpoints.\n> \n> Alternatively you can run hf-mcp-server locally as a STDIO Server, or with robust support for SSE, Streaming HTTP and Streaming HTTP JSON Mode. This also runs a local UI for selecting tools and endpoints and supports ToolListChangedNotifications too.\n\nhf.co/mcp\n\n \n\nmcp-hfspace\n\nRead the introduction here llmindset.co.uk/resources/mcp-hfspace/\n\nConnect to Hugging Face Spaces with minimal setup needed - simply add your spaces and go!\n\nBy default, it connects to black-forest-labs/FLUX.1-schnell providing Image Generation capabilities to Claude Desktop.\n\n \n\nGradio MCP Support\n\n> [!TIP]\n> Gradio 5.28 now has integrated MCP Support via SSE: https://huggingface.co/blog/gradio-mcp. Check out whether your target Space is MCP Enabled!\n\nInstallation\n\nNPM Package is @llmindset/mcp-hfspace.\n\nInstall a recent version of NodeJS for your platform, then add the following to the mcpServers section of your claude_desktop_config.json file:\n\n \n\nPlease make sure you are using Claude Desktop 0.78 or greater.\n\nThis will get you started with an Image Generator.\n\nBasic setup\n\nSupply a list of HuggingFace spaces in the arguments. mcp-hfspace will find the most appropriate endpoint and automatically configure it for usage. An example claude_desktop_config.json is supplied below.\n\nBy default the current working directory is used for file upload/download. On Windows this is a read/write folder at \\users\\ \\AppData\\Roaming\\Claude\\ Text Output: david bowie\n \n\n_If you are uploading something to Claude\'s context use the Paperclip Attachment button, otherwise specify the filename for the Server to send directly._\n\nWe can also supply a URL. For example : use paligemma to detect humans in https://e3.365dm.com/24/12/1600x900/skynews-taylor-swift-eras-tour_6771083.jpg?20241209000914 -> One person is detected in the image - Taylor Swift on stage.\n\nExample 3 - Text-to-Speech (Download Audio)\n\nIn _Claude Desktop Mode_, the audio file is saved in the WORK_DIR, and Claude is notified of the creation. If not in desktop mode, the file is returned as a base64 encoded resource to the Client (useful if it supports embedded Audio attachments).\n\n \n\nExample 4 - Speech-to-Text (Upload Audio)\n\nHere, we use hf-audio/whisper-large-v3-turbo to transcribe some audio, and make it available to Claude.\n\n \n\nExample 5 - Image-to-Image\n\nIn this example, we specify the filename for microsoft/OmniParser to use, and get returned an annotated Image and 2 separate pieces of text: descriptions and coordinates. The prompt used was use omniparser to analyse ./screenshot.png and use the analysis to produce an artifact that reproduces that screen. DawnC/Pawmatch is also good at this.\n\n \n\nExample 6 - Chat\n\nIn this example, Claude sets a number of reasoning puzzles for Qwen, and asks follow-up questions for clarification.\n\n \n\nSpecifying API Endpoint\n\nIf you need, you can specify a specific API Endpoint by adding it to the spacename. So rather than passing in Qwen/Qwen2.5-72B-Instruct you would use Qwen/Qwen2.5-72B-Instruct/model_chat.\n\nClaude Desktop Mode\n\nThis can be disabled with the option --desktop-mode=false or the environment variable CLAUDE_DESKTOP_MODE=false. In this case, content as returned as an embedded Base64 encoded Resource.\n\nRecommended Spaces\n\nSome recommended spaces to try:\n\nImage Generation\n\n- shuttleai/shuttle-3.1-aesthetic\n- black-forest-labs/FLUX.1-schnell\n- yanze/PuLID-FLUX\n- gokaygokay/Inspyrenet-Rembg (Background Removal)\n- diyism/Datou1111-shou_xin - Beautiful Pencil Drawings\n\nChat\n\n- Qwen/Qwen2.5-72B-Instruct\n- prithivMLmods/Mistral-7B-Instruct-v0.3\n\nText-to-speech / Audio Generation\n\n- fantaxy/Sound-AI-SFX\n- parler-tts/parler_tts\n\nSpeech-to-text\n\n- hf-audio/whisper-large-v3-turbo\n- (the openai models use unnamed parameters so will not work)\n\nText-to-music\n\n- haoheliu/audioldm2-text2audio-text2music\n\nVision Tasks\n\n- microsoft/OmniParser\n- merve/paligemma2-vqav2\n- merve/paligemma-doc\n- DawnC/PawMatchAI\n- DawnC/PawMatchAI/on_find_match_click - for interactive dog recommendations\n\nOther Features\n\nPrompts\n\nPrompts for each Space are generated, and provide an opportunity to input. Bear in mind that often Spaces aren\'t configured with particularly helpful labels etc. Claude is actually very good at figuring this out, and the Tool description is quite rich (but not visible in Claude Desktop).\n\nResources\n\nA list of files in the WORK_DIR is returned, and as a convenience returns the name as "Use the file..." text. If you want to add something to Claude\'s context, use the paperclip - otherwise specify the filename for the MCP Server. Claude does not support transmitting resources from within Context.\n\nPrivate Spaces\n\nPrivate Spaces are supported with a HuggingFace token. The Token is used to download and save generated content.\n\nUsing Claude Desktop\n\nTo use with Claude Desktop, add the server config:\n\nOn MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n\nKnown Issues and Limitations\n\nmcp-hfspace\n\n- Endpoints with unnamed parameters are unsupported for the moment.\n- Full translation from some complex Python types to suitable MCP formats.\n\nClaude Desktop\n\n- Claude Desktop 0.75 doesn\'t seem to respond to errors from the MCP Server, timing out instead. For persistent issues, use the MCP Inspector to get a better look at diagnosing what\'s going wrong. If something suddenly stops working, it\'s probably due to exhausting your HuggingFace ZeroGPU quota - try again after a short period, or set up your own Space for hosting.\n- Claude Desktop seems to use a hard timeout value of 60s, and doesn\'t appear to use Progress Notifications to manage UX or keep-alive. If you are using ZeroGPU spaces, large/heavy jobs may timeout. Check the WORK_DIR for results though; the MCP Server will still capture and save the result if it was produced.\n- Claude Desktops reporting of Server Status, logging etc. isn\'t great - use @modelcontextprotocol/inspector to help diagnose issues.\n\nHuggingFace Spaces\n\n- If ZeroGPU quotas or queues are too long, try duplicating the space. If your job takes less than sixty seconds, you can usually change the function decorator @spaces.GPU(duration=20) in app.py` to request less quota when running the job.\n- Passing HF_TOKEN will make ZeroGPU quotas apply to your (Pro) HF account\n- If you have a private space, and dedicated hardware your HF_TOKEN will give you direct access to that - no quota\'s apply. I recommend this if you are using for any kind of Production task.\n\nThird Party MCP Services', 'readme_raw_len': 6795, 'one_line_zh': '借助 Hugging Face Spaces 实现开源图像生成、对话、视觉等多项能力', 'one_line_plain': 'Create and share AI applications using Hugging Face Spaces for image, text, and audio tasks'}, {'id': 554, 'key': 'recursechat-mcp-server-apple-shortcuts', 'kind': 'mcp', 'name': 'recursechat/mcp-server-apple-shortcuts', 'one_line': '📇 🏠 🍎 - Integration with Apple Shortcuts', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/recursechat/mcp-server-apple-shortcuts', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/recursechat/mcp-server-apple-shortcuts', 'downloads': None, 'stars': 351, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/recursechat.png?size=64', 'official_desc': 'Apple Shortcuts MCP Server A Model Context Protocol (MCP) server that lets AI assistants like Claude control Apple Shortcuts automations.', 'stars_own': 351, 'stars_creator': None, 'stars_own_repo': 'recursechat/mcp-server-apple-shortcuts', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'Apple Shortcuts MCP Server 🤖\n\nA Model Context Protocol (MCP) server that lets AI assistants like Claude control Apple Shortcuts automations. This enables AI models to trigger shortcuts and automate tasks on macOS in a safe and controlled way.\n\n \n\n \n\nWhat is MCP? 🤔\n\nThe Model Context Protocol (MCP) is a system that lets AI apps, like Claude Desktop, connect to external tools and data sources. It gives a clear and safe way for AI assistants to work with local services and APIs while keeping the user in control.\n\nWhat does this server do? 🚀\n\nThe Apple Shortcuts MCP server:\n- Enables AI assistants to list available shortcuts\n- Allows running shortcuts by name with optional input parameters \n- Provides a simple interface for automation control\n\nPrerequisites 📋\n\nBefore you begin, ensure you have:\n\n- Node.js (v18 or higher)\n- Claude Desktop installed\n- macOS with Shortcuts app configured\n\nConfiguration to use Apple Shortcuts Server ⚙️\n\nHere\'s the Claude Desktop configuration to use the Apple Shortcuts server:\n \n\nBuild Apple Shortcuts Server and run locally 🛠️\n\n1. Clone this repository:\n\n \n\n2. Install dependencies:\n \n\n3. Build project\n \n\nHere\'s the Claude Desktop configuration to use the Apple Shortcuts server with a local build:\n \n\n \n\nUsage 🎯\n\nYou can ask Claude "list shortcuts" or run a specific shortcut with the shortcut name, for example "get word of the day" or "play a song".\n\nLicense ⚖️\n\nApache-2.0', 'readme_raw_len': 1419, 'one_line_zh': '📇 🏠 🍎 - 与 Apple Shortcuts 快捷指令集成', 'one_line_plain': 'Connect to Apple Shortcuts for integration'}, {'id': 4317, 'key': 'ttommyth-interactive-mcp', 'kind': 'mcp', 'name': 'ttommyth/interactive-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Interactive LLM workflows with local user prompts and chat directly in the MCP loop', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Vibe coding should have human in the loop interactive-mcp: Local, cross-platform MCP server for interact with your AI Agent', 'url': 'https://github.com/ttommyth/interactive-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ttommyth/interactive-mcp', 'downloads': None, 'stars': 351, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ttommyth.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Interactive LLM workflows with local user prompts and chat directly in the MCP loop.', 'stars_own': 351, 'stars_creator': None, 'stars_own_repo': 'ttommyth/interactive-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'interactive-mcp\n\n \n\n \n\n \n\nA MCP Server implemented in Node.js/TypeScript, facilitating interactive communication between LLMs and users. **Note:** This server is designed to run locally alongside the MCP client (e.g., Claude Desktop, VS Code), as it needs direct access to the user\'s operating system to display notifications and command-line prompts.\n\n_(Note: This project is in its early stages.)_\n\n**Want a quick overview?** Check out the introductory blog post: Stop Your AI Assistant From Guessing — Introducing interactive-mcp\n\nDemo Video\n\n \n \n \n \n \n\nTools\n\nThis server exposes the following tools via the Model Context Protocol (MCP):\n\n- request_user_input: Asks the user a question and returns their answer. Can display predefined options.\n- message_complete_notification: Sends a simple OS notification.\n- start_intensive_chat: Initiates a persistent command-line chat session.\n- ask_intensive_chat: Asks a question within an active intensive chat session.\n- stop_intensive_chat: Closes an active intensive chat session.\n\nDemo\n\nHere are demonstrations of the interactive features:\n\n| Normal Question | Completion Notification |\n| :--------------------------------------------------------: | :-----------------------------------------------------------------: |\n| | |\n\n| Intensive Chat Start | Intensive Chat End |\n| :------------------------------------------------------------------: | :--------------------------------------------------------------: |\n| | |\n\nUsage Scenarios\n\nThis server is ideal for scenarios where an LLM needs to interact directly with the user on their local machine, such as:\n\n- Interactive setup or configuration processes.\n- Gathering feedback during code generation or modification.\n- Clarifying instructions or confirming actions in pair programming.\n- Any workflow requiring user input or confirmation during LLM operation.\n\nClient Configuration\n\nThis section explains how to configure MCP clients to use the interactive-mcp server.\n\nBy default, user prompts will time out after 30 seconds. You can customize server options like timeout or disabled tools by adding command-line flags directly to the args array when configuring your client.\n\nPlease make sure you have the npx command available.\n\nUsage with Claude Desktop / Cursor\n\nAdd the following minimal configuration to your claude_desktop_config.json (Claude Desktop) or mcp.json (Cursor):\n\n \n\n**With specific version**\n\n \n\n**Example with Custom Timeout (30s):**\n\n \n\nUsage with VS Code\n\nAdd the following minimal configuration to your User Settings (JSON) file or .vscode/mcp.json:\n\n \n\nmacOS Recommendations\n\nFor a smoother experience on macOS using the default Terminal.app, consider this profile setting:\n\n- **(Shell Tab):** Under **"When the shell exits"** (**Terminal > Settings > Profiles > _[Your Profile]_ > Shell**), select **"Close if the shell exited cleanly"** or **"Close the window"**. This helps manage windows when the MCP server starts and stops.\n\nDevelopment Setup\n\nThis section is primarily for developers looking to modify or contribute to the server. If you just want to _use_ the server with an MCP client, see the "Client Configuration" section above.\n\nPrerequisites\n\n- **Node.js:** Check package.json for version compatibility.\n- **pnpm:** Used for package management. Install via npm install -g pnpm after installing Node.js.\n\nInstallation (Developers)\n\n1. Clone the repository:\n\n \n\n2. Install dependencies:\n\n \n\nRunning the Application (Developers)\n\n \n\nCommand-Line Options\n\nThe interactive-mcp server accepts the following command-line options. These should typically be configured in your MCP client\'s JSON settings by adding them directly to the args array (see "Client Configuration" examples).\n\n| Option | Alias | Description |\n| ----------------- | ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| --timeout | -t | Sets the default timeout (in seconds) for user input prompts. Defaults to 30 seconds. |\n| --disable-tools | -d | Disables specific tools or groups (comma-separated list). Prevents the server from advertising or registering them. Options: request_user_input, message_complete_notification, intensive_chat. |\n\n**Example:** Setting multiple options in the client config args array:\n\n \n\nDevelopment Commands\n\n- **Build:** pnpm build\n- **Lint:** pnpm lint\n- **Format:** pnpm format\n\nGuiding Principles for Interaction\n\nWhen interacting with this MCP server (e.g., as an LLM client), please adhere to the following principles to ensure clarity and reduce unexpected changes:\n\n- **Prioritize Interaction:** Utilize the provided MCP tools (request_user_input, start_intensive_chat, etc.) frequently to engage with the user.\n- **Seek Clarification:** If requirements, instructions, or context are unclear, **always** ask clarifying questions before proceeding. Do not make assumptions.\n- **Confirm Actions:** Before performing significant actions (like modifying files, running complex commands, or making architectural decisions), confirm the plan with the user.\n- **Provide Options:** Whenever possible, present the user with predefined options through the MCP tools to facilitate quick decisions.\n\nYou can provide these instructions to an LLM client like this:\n\n \n\nContributing\n\nContributions are welcome! Please follow standard development practices. (Further details can be added later).\n\nLicense\n\nMIT (See LICENSE file for details - if applicable, or specify license directly).', 'readme_raw_len': 5574, 'one_line_zh': '支持交互式 LLM 工作流，可读取本地用户输入并在 MCP 循环中直接对话', 'one_line_plain': 'Interactive LLM workflows with local user prompts and direct chat within the MCP loop'}, {'id': 866, 'key': 'chaindead-telegram-mcp', 'kind': 'mcp', 'name': 'chaindead/telegram-mcp', 'one_line': '🏎️ 🏠 - Telegram API integration: access user data, manage dialogs (chats, channels, groups), retrieve…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Telegram MCP for managing dialogs, messages, drafts, read statuses, and more.', 'url': 'https://github.com/chaindead/telegram-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/chaindead/telegram-mcp', 'downloads': None, 'stars': 349, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/chaindead.png?size=64', 'official_desc': '🏎️ 🏠 - Telegram API integration: access user data, manage dialogs (chats, channels, groups), retrieve messages and handle read status.', 'stars_own': 349, 'stars_creator': None, 'stars_own_repo': 'chaindead/telegram-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Telegram MCP server\n\nThe server is a bridge between the Telegram API and the AI assistants and is based on the Model Context Protocol.\n\n> [!IMPORTANT]\n> Ensure that you have read and understood the Telegram API Terms of Service before using this server.\n> Any misuse of the Telegram API may result in the suspension of your account.\n\nTable of Contents\n- What is MCP?\n- What does this server do?\n - Capabilities\n - Prompt examples\n - Message Management\n - Organization\n - Communication\n- Installation\n - Homebrew\n - NPX\n - From Releases\n - MacOS\n - Linux\n - Windows\n - From Source\n- Configuration\n - Authorization\n - Client Configuration\n - JSON Schema Version\n- Star History\n\nWhat is MCP?\n\nThe Model Context Protocol (MCP) is a system that lets AI apps, like Claude Desktop or Cursor, connect to external tools and data sources. It gives a clear and safe way for AI assistants to work with local services and APIs while keeping the user in control.\n\nWhat does this server do?\n\nCapabilities\n\n- [x] Get current account information (tool: tg_me)\n- [x] List dialogs with optional unread filter (tool: tg_dialogs)\n- [x] Mark dialog as read (tool: tg_read)\n- [x] Retrieve messages from specific dialog (tool: tg_dialog)\n- [x] Send draft messages to any dialog (tool: tg_send)\n\nPrompt examples\n\nHere are some example prompts you can use with AI assistants:\n\nMessage Management\n- "Check for any unread important messages in my Telegram"\n- "Summarize all my unread Telegram messages"\n- "Read and analyze my unread messages, prepare draft responses where needed"\n- "Check non-critical unread messages and give me a brief overview"\n\nOrganization\n- "Analyze my Telegram dialogs and suggest a folder structure"\n- "Help me categorize my Telegram chats by importance"\n- "Find all work-related conversations and suggest how to organize them"\n\nCommunication\n- "Monitor specific chat for updates about [topic]"\n- "Draft a polite response to the last message in [chat]"\n- "Check if there are any unanswered questions in my chats"\n\nInstallation\n\nHomebrew\n\nYou can install a binary release on macOS/Linux using brew:\n\n \n\nNPX\n\nYou can run the latest version directly using npx (supports macOS, Linux, and Windows):\n\n \n\nWhen using NPX, modify the standard commands and configuration as follows:\n\n- Authentication command becomes:\n \n\n- Claude MCP server configuration becomes:\n \n\nFor complete setup instructions, see Authorization and Client Configuration.\n\nFrom Releases\n\nMacOS\n\n \n\n> **Note:** The commands below install to /usr/local/bin. To install elsewhere, replace /usr/local/bin with your preferred directory in your PATH.\n\nFirst, download the archive for your architecture:\n\n \n\nThen install the binary:\n\n \n \n\nLinux\n \n\n> **Note:** The commands below install to /usr/local/bin. To install elsewhere, replace /usr/local/bin with your preferred directory in your PATH.\n\nFirst, download the archive for your architecture:\n\n \n\nThen install the binary:\n\n \n \n\nWindows\n\n \n\nWindows\n1. Download the latest release for your architecture:\n - Windows x64\n - Windows ARM64\n2. Extract the .zip file\n3. Add the extracted directory to your PATH or move telegram-mcp.exe to a directory in your PATH\n \n\nFrom Source\n\nRequirements:\n- Go 1.24 or later\n- GOBIN in PATH\n\n \n\nConfiguration\n\nAuthorization\n\nBefore you can use the server, you need to connect to the Telegram API.\n\n1. Get the API ID and hash from Telegram API\n2. Run the following command:\n > __Note:__\n > If you have 2FA enabled: add --password \n\n > __Note:__\n > If you want to override existing session: add --new\n\n \n\n 📩 Enter the code you received from Telegram to connect to the API.\n\n3. Done! Please give this project a ⭐️ to support its development.\n\nClient Configuration\n\nExample of Configuring Claude Desktop to recognize the Telegram MCP server.\n\n1. Open the Claude Desktop configuration file:\n - in MacOS, the configuration file is located at ~/Library/Application Support/Claude/claude_desktop_config.json\n - in Windows, the configuration file is located at %APPDATA%\\Claude\\claude_desktop_config.json\n\n > __Note:__\n > You can also find claude_desktop_config.json inside the settings of Claude Desktop app\n\n2. Add the server configuration\n \n for Claude desktop:\n \n\nJSON Schema Version\n\nSome MCP clients (e.g. VS Code) do not support JSON Schema Draft 2020-12 and will reject tools that use it. You can override the JSON Schema version by setting the --schema-version flag or the TG_SCHEMA_VERSION environment variable.\n\nCommon values:\n| Version | URL |\n|---------|-----|\n| Draft-07 (recommended for VS Code) | https://json-schema.org/draft-07/schema# |\n| Draft 2020-12 (default) | https://json-schema.org/draft/2020-12/schema |\n\nStar History', 'readme_raw_len': 4673, 'one_line_zh': '🏎️ 🏠 - Telegram API 集成：访问用户数据，管理对话（聊天、频道、群组），获取…', 'one_line_plain': 'Access Telegram user data and manage chats, channels, and groups'}, {'id': 865, 'key': 'carterlasalle-mac-messages-mcp', 'kind': 'mcp', 'name': 'carterlasalle/mac_messages_mcp', 'one_line': '🏠 🍎 - Query, analyze, send and receive iMessage conversations, with phone number validation, attachments,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/carterlasalle/mac_messages_mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/carterlasalle/mac_messages_mcp', 'downloads': None, 'stars': 333, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/carterlasalle.png?size=64', 'official_desc': 'ages MCP Read, search, and send macOS Messages from any local MCP client.** Quick start · Available tools · Agent skill · Command-line interface · Security · Changelog Use Claude, Codex, Cursor, VS Code, or any local MCP client to search, read, and send messages through the macOS Messages app.', 'stars_own': 333, 'stars_creator': None, 'stars_own_repo': 'carterlasalle/mac_messages_mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'Mac Messages MCP\n\n**Read, search, and send macOS Messages from any local MCP client.**\n\n \n \n \n \n \n \n\nQuick start · Available tools · Agent skill · Command-line interface · Security · Changelog\n\n \n\nUse Claude, Codex, Cursor, VS Code, or any local MCP client to search, read, and\nsend messages through the macOS Messages app.\n\nMac Messages MCP runs locally on your Mac. It opens the Messages and Contacts\ndatabases read-only, returns only the data a client asks for, and uses\nMessages.app automation only when the client explicitly calls the send tool.\n\n> [!IMPORTANT] This server is macOS-only. Reading messages requires Full Disk\n> Access. Sending requires a Mac signed into Messages plus permission for the\n> launching app to automate Messages.\n\nHow it works\n\n \n\nEverything the client reads comes from the local SQLite databases, opened\nread-only. Everything that leaves the machine goes out through Messages.app,\nunder an explicit send tool call. Message- and contact-derived text is fenced as\nuntrusted data on the way back to the model.\n\nCapabilities\n\n| Area | What Mac Messages MCP provides |\n|---|---|\n| Reading | Recent messages across all conversations or filtered by contact, group chat, date range, or unread state; paging with limit/offset and forward-cursor reads |\n| Conversations | Every conversation (1:1, business, and group) with kind, message and unread counts, last activity, and a blocking wait for new messages |\n| Search | Fuzzy message-body search across a time window or all history, scoped to a contact or conversation |\n| Context | Per-message service (iMessage/SMS/RCS), unread and delivery state, tapbacks, and threaded replies |\n| Contacts | Fuzzy contact lookup by approximate name, send-ready phone numbers, and contact creation |\n| Group chats | Named group chat discovery with stable chat IDs reused for reads and sends |\n| Sending | iMessage with SMS/RCS fallback, file attachments, optional human approval through MCP elicitation, and iMessage reachability checks |\n| Scheduling | Queue a message for later delivery while the server process stays alive |\n| Attachments | Metadata search, best-effort text/PDF content search, inline images (HEIC converted to PNG), and local paths for larger or non-image files |\n| Diagnostics | Messages and Contacts database permission checks from inside the MCP client |\n| Privacy | Read-only SQLite access, no message archive of its own, and a structural untrusted-output boundary |\n\nQuick start\n\n1. Install uv\n\n \n\nConfirm that the launcher is available:\n\n \n\nPython 3.10 or newer is required. uvx can provision a compatible Python and\ninstalls Mac Messages MCP in an isolated environment, so you do not need to\ncreate a virtual environment first.\n\n2. Grant macOS permissions\n\nOpen **System Settings → Privacy & Security → Full Disk Access** and enable the\napp that will launch the MCP server:\n\n- Claude Desktop, Cursor, VS Code, or the ChatGPT desktop app when configured in\n that app\n- Terminal, iTerm2, Ghostty, or another terminal when using Claude Code or Codex\n CLI from that terminal\n\nQuit and reopen the app after changing Full Disk Access. On the first contact\nlookup or send, macOS may separately ask for access to Contacts or permission to\ncontrol Messages. Allow those prompts.\n\nAlso make sure Messages.app is open, signed in, and already able to send a\nnormal message.\n\n3. Add the server to your MCP client\n\nThe server command is the same everywhere:\n\n \n\nChoose your client below.\n\nClaude Desktop\n\nOpen **Claude → Settings → Developer → Edit Config**, then add:\n\n \n\nPreserve any other servers already in claude_desktop_config.json, save the\nfile, and restart Claude Desktop.\n\nClaude Desktop also supports installable .mcpb extensions. See\nBuild the Claude Desktop extension if you\nwant to package this repository as one.\n\nClaude Code\n\nAdd it once at user scope so it is available in every project:\n\n \n\nVerify it:\n\n \n\nInside Claude Code, run /mcp to inspect the connection and tools.\n\nCodex CLI, Codex IDE extension, and ChatGPT desktop app\n\nCodex clients on the same Mac share MCP configuration. Add the server with:\n\n \n\nThen verify it:\n\n \n\nYou can also add it directly to ~/.codex/config.toml:\n\n \n\nRestart the desktop app or IDE extension after changing the configuration. In\nCodex CLI, use /mcp to view the active server.\n\nCursor\n\n \n\nOr open **Cursor Settings → Tools & MCP → New MCP Server** and use:\n\n \n\nRestart the server from Cursor\'s MCP settings after saving.\n\nVS Code / GitHub Copilot\n\nOpen the Command Palette and run **MCP: Add Server**. Choose **Command\n(stdio)**, enter uvx as the command, add mac-messages-mcp as the argument,\nand install it globally.\n\nOr add it from a terminal:\n\n \n\nThe equivalent user or workspace mcp.json entry is:\n\n \n\n> [!NOTE] VS Code uses a top-level servers object. Claude Desktop and Cursor\n> use mcpServers.\n\nOther stdio MCP clients\n\nUse this generic server definition:\n\n \n\nIf a GUI client reports that uvx cannot be found, run which uvx in Terminal\nand replace "uvx" with the returned absolute path. Homebrew commonly installs\nit at /opt/homebrew/bin/uvx on Apple silicon and /usr/local/bin/uvx on Intel\nMacs.\n\n4. Verify the connection\n\nAsk your client to call tool_check_db_access, then tool_check_addressbook.\nOnce both succeed, try prompts such as:\n\n \n\n \n\n \n\n \n\nThe first uvx launch can take longer while it downloads and caches Python\ndependencies.\n\n5. Optional: set the phone number region\n\nPhone numbers written in national format (06 39 98 00 01, (415) 555-1234)\nhave to be expanded to E.164 before they can be matched against the Messages\ndatabase, and that expansion needs to know which country they belong to. The\nserver reads your Mac\'s own region setting for this, so on a correctly\nconfigured Mac there is nothing to do.\n\nSet MAC_MESSAGES_REGION to an [ISO 3166-1 alpha-2][iso3166] code when your\nnumbers belong to a different region than your Mac is configured for — a French\nSIM on a Mac set to en_US, say:\n\n \n\nFor Claude Code:\n\n \n\nThe region is resolved once at startup, so restart the server after changing\nit. Resolution order: MAC_MESSAGES_REGION, then the macOS AppleLocale\npreference, then LC_ALL / LC_CTYPE / LANG, then US. Numbers already\nwritten in E.164 (+33639980001) are never reinterpreted and need none of\nthis.\n\n[iso3166]: https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2\n\nAvailable tools\n\n \n\n| Tool | Purpose | Side effect |\n| ---------------------------------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------- |\n| tool_get_recent_messages | Read recent messages, filtered by contact, group chat ID, date range, or unread state; page with limit/offset | Read-only |\n| tool_fuzzy_search_messages | Search message bodies by approximate text match, scoped to a contact or chat; defaults to 30 days, or hours=0 | Read-only |\n| tool_list_conversations | List every conversation with kind, message count, unread count, and last activity | Read-only |\n| tool_wait_for_new_messages | Block until a message newer than a ROWID cursor arrives, or the timeout expires | Read-only |\n| tool_find_contact | Fuzzy-match a name in Contacts and return phone numbers | Read-only |\n| tool_get_chats | List named group chats and their identifiers | Read-only |\n| tool_search_attachments | Find attachment metadata by date, contact, MIME type, and limit | Read-only |\n| tool_search_attachment_contents | Search inside attachment text/PDF contents by date, contact, and MIME type | Read-only |\n| tool_get_attachment | Fetch one attachment by ID, inline when supported or as a local path | Read-only |\n| tool_check_imessage_availability | Check likely iMessage availability for a phone number or email | Read-only |\n| tool_check_db_access | Diagnose access to ~/Library/Messages/chat.db | Read-only |\n| tool_check_contacts | Return a contact count and a small sample | Read-only |\n| tool_check_addressbook | Diagnose Contacts/AddressBook database access | Read-only', 'readme_raw_len': 18802, 'one_line_zh': '🏠 🍎 - 查询、分析并收发 iMessage 对话，支持手机号校验、附件…', 'one_line_plain': 'Query and send iMessages with phone validation and attachments'}, {'id': 4829, 'key': 'orgoai-bops', 'kind': 'repo', 'name': 'OrgoAI/bops', 'one_line': 'Bops: a team of AI bots that run your business ops, each with its own computer, email and phone number. bops.bot', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Bops: a team of AI bots that run your business ops, each with its own computer, email and phone number. bops.bot', 'url': 'https://github.com/OrgoAI/bops', 'source': 'github', 'source_url': 'https://github.com/OrgoAI/bops', 'downloads': None, 'stars': 326, 'installs': None, 'mentions': 1, 'score': 103.49, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-10', 'times_seen': 2, 'icon': 'https://github.com/OrgoAI.png?size=64', 'official_desc': 'Bops: a team of AI bots that run your business ops, each with its own computer, email and phone number. bops.bot', 'stars_own': 326, 'stars_creator': None, 'stars_own_repo': 'OrgoAI/bops', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx next', 'install_src': 'HEAD/README.md', 'install_conf': 'low', 'desc_src': 'github-api', 'readme_body': "Bops \n\n \n Business ops, run by bots. \n A team of AI bots that works for you on real computers. Chat with them like teammates, and they get the work done.\n \n\n \n Download for Mac \n  · \n Website \n  · \n Plans \n  · \n Roadmap \n  · \n Self-host \n \n\n \n \n \n \n\n \n Open code, one limit. Use, self-host, change and share Bops for free, at work or at home. \n The license (FSL) only rules out offering it as a competing product or hosted service, \n and each version becomes Apache 2.0, fully open source, two years after its release. Details \n \n\n \n \n Created by Nick Vasilescu ( @nickvasilescu ) \n Developed with Orgo \n \n\n \n \n \n \n \n \n \n \n \n\n \n\nBops is a Mac app that gives you a team of AI bots for your business ops: inbox, pipeline, invoices, reports. They work on cloud computers you can watch live and take over at any time. Reach them in the app and in Slack, Telegram and Discord, and on Pro and Max by text, call or email. They remember who you are and what they did yesterday.\n\n \n \n \n\n \n \n \n \n Watch or take over. Watch any screen live, take control, then hand it back. \n \n \n \n Asks before it acts. Reading runs at once. Sending, creating and paying wait for your OK. \n \n \n \n \n \n Your apps, your rules. About 1,400 apps, several accounts each. Each bot gets only the access you give it. \n \n \n \n Where your team works. Ask in Slack, Telegram or Discord, and the answer comes back there. \n \n \n \n\nWhat your bots can do\n\n- **Work on real computers.** Your bots work on a cloud computer from Orgo: 4 cores, 16 GB RAM and 4 screens, with Chrome, LibreOffice, GIMP and Inkscape. Watch any screen live, or take over. On Free and Pro all your bots share one computer. Max gives you up to 3.\n- **Work as a team.** A main bot (Boppy, unless you rename it) runs the team and hands work to the specialists you add. Bots talk to each other, keep threads and report back in the chat. Each workspace is its own team.\n- **Remember.** What you tell your bots, they remember, per workspace, on every channel.\n- **Use your apps.** Connect Gmail, Calendar and any of about 1,400 apps, with several accounts each. Reading runs at once. Sending, creating or paying asks you first. A bot set to Just do it sends and changes things without asking, and still asks before paying.\n- **Look things up.** Companies, people, work emails (checked before they're used), phones, company news, hiring and funding, and local businesses, for cents a lookup. A pricier lookup asks you first, and so does emailing someone a bot found, even on Just do it.\n- **Answer email.** On Pro and Max, your main bot has its own email address, ending in bops.bot. Email it a task, and it emails back when it's done.\n- **Take texts and calls.** On Pro and Max, your main bot has its own phone number. Text it like a person, or call it and talk live. It hands off work while you talk, and texts you the results. Only you can give it work: anyone else who calls gets a bot that takes a message.\n- **Talk in the app.** Call any bot by voice, or use the mic in the chat.\n- **Join your team's chat.** Add a bot to Slack, Telegram or Discord. It answers when it's mentioned and takes requests only from you.\n- **Work on your Mac, too.** A bot can do a task on your Mac in a Chrome of its own, so sites see your home connection. It can't touch your files or apps there until you turn on Full access in **Settings → This Mac**, which also gives its tasks a shell.\n- **Sign in safely.** Logins live in the Vault: passwords and 2FA keys stay in your Mac's Keychain, and no model ever sees them.\n- **Keep an eye on things.** Routines and reminders run on a schedule. A bot can watch a screen, like an inbox or a dashboard, and tell you when something there needs you.\n- **Make things.** Bots draw and edit pictures right in the chat, and files they link open in Bops.\n\nBops runs on your Mac: keep it on for routines, watches, and Telegram or Discord. While it's away, callers to your bot's number get a bot that takes a message.\n\nComing soon: WhatsApp, and a real phone for each bot that it drives like its computer.\n\nPlans\n\nEvery plan includes a Bops computer: 1 on Free and Pro, up to 3 on Max. On Free and Pro, all your bots share that one computer. AI credit pays for what your bots do, billed at cost.\n\n| | **Free** | **Pro** | **Max** |\n|---|---|---|---|\n| Price | $0 | $20 a month | $200 a month |\n| Computers | **1 computer included** 10 hours a month, 4 cores, 16 GB RAM, multi-screen | **1 computer included** Always on, 4 cores, 16 GB RAM, templates, multi-screen | **Up to 3 computers** Always on, 4 cores, 16 GB RAM each |\n| AI credit | $5 of AI credit, once | $20 of AI credit every month | $200 of AI credit every month |\n| Bots | As many as you like, all sharing that one computer | As many as you like, all sharing that one computer | As many as you like, across up to 3 computers |\n| Phone numbers and emails | No phone number or email | 1 phone number and 1 email | Up to 5 phone numbers and 5 emails |\n\n- Free's computer gets 10 hours a month. It sleeps when nobody is using it, so the hours last. Account and usage shows the hours used this month and when they start over.\n- Need more AI credit? Add some any time, $5 to $1,000, in Account and usage. It's a one-time charge, never automatic.\n- Pro and Max credit doesn't carry over from month to month. When credit runs out, tasks stop cleanly.\n- Pick or change a plan in the app, in Account and usage.\n\nRunning Bops yourself on your own keys? Bops plans don't apply: you pay each provider directly, and your Orgo plan sets how many computers you can run. See Run it yourself.\n\nDownload\n\n \n \n \n\n1. Download Bops from bops.bot. It runs on a Mac with Apple silicon.\n2. Open the DMG and drag Bops to Applications.\n3. Sign in with Google, with your email, or with Orgo. If you don't have an Orgo account yet, one is made for you.\n4. Say hi to Boppy. It takes it from there.\n\nEvery release is signed with Developer ID and notarized by Apple. When a newer version is out, Bops tells you, along with what's new in it.\n\nRoadmap\n\nWhat's coming next. Ideas are welcome: open an issue.\n\n- **Multi-harness support.** Today your bots run on OpenAI (its Agents API with Codex, and its computer tool). Soon you'll choose the agent harness they run on.\n- **Zero data retention.** An option where the AI providers your bots use don't store your requests or their replies.\n\nCredits\n\nBops was created by Nick Vasilescu (@nickvasilescu). It started as his open-source project and is now developed with Orgo. See AUTHORS and CITATION.cff.\n\nThanks to everyone who has reported a bug, sent a fix or shared an idea: see all contributors.\n\nBuilt with\n\n- Orgo: the bots' cloud computers\n- OpenAI: chat, tasks and voice\n- Honcho: long-term memory\n- Composio: connected apps\n- treg: business data\n- AgentMail: the bots' email\n- AgentPhone: phone numbers, texts and calls\n- TypeSafe: small yes or no judgments\n\nThe app itself is Electron and Next.js.\n\n---\n\nHow it works\n\n- **The app** (desktop/, Electron) starts the **Bops server** (Next.js: app/ and lib/server/) on your Mac, on port 3210. The server runs the team: chats, threads, routines, watches and the bots' tasks.\n- **The bots' computers** are Orgo cloud computers, launched from a template built from this repo (orgo/, with what runs on them in vm/).\n- **Tasks on the bots' computers** run on OpenAI's computer tool (Responses API). **Tasks on your Mac** run on OpenAI's Agents API, with the Codex CLI (codex exec-server) and a Chrome of the bots' own.\n- **Signed in with Orgo**, the app works through Bops Cloud (cloud/), the server Orgo runs for hosted Bops. Orgo's provider keys stay there, and your Mac gets only narrow ones: a key for your own mailboxes and a spend-capped key for tasks. Bops Cloud also takes the webhooks for texts, calls and Slack, and keeps your chats in your account.\n- **Self-hosted** (BOPS_SELF_HOSTED=1), the app calls each provider directly with the keys in .env.local and keeps its state in .data/", 'readme_raw_len': 20265, 'one_line_zh': 'Bops：一支运行你业务运维的 AI Bot 团队，每个都有自己的电脑、邮箱与电话号码', 'one_line_plain': 'Bops: a team of AI bots that run your business ops, each with its own computer, email and phone number. bops.bot'}]/2026-10-20 https://anclum.com/zh/[{'id': 5585, 'key': 'paperclipai-paperclip', 'kind': 'repo', 'name': 'paperclipai/paperclip', 'one_line': 'The open-source app everyone uses to manage agents at work', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'The open-source app everyone uses to manage agents at work', 'url': 'https://github.com/paperclipai/paperclip', 'source': 'github', 'source_url': 'https://github.com/paperclipai/paperclip', 'downloads': None, 'stars': 104653, 'installs': None, 'mentions': 5, 'score': 703.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/paperclipai.png?size=64', 'official_desc': 'The open-source app everyone uses to manage agents at work', 'stars_own': 104653, 'stars_creator': None, 'stars_own_repo': 'paperclipai/paperclip', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx paperclipai@latest', 'install_src': 'HEAD/README.md', 'install_conf': 'high', 'desc_src': 'github-api', 'readme_body': 'Quickstart ·\n Docs ·\n GitHub ·\n Discord ·\n Twitter ·\n Website \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n\n \n \n \n\n \n Sign up for the Paperclip Cloud waitlist → \n \n\n \n\nPaperclip is the app people use to manage AI agents for work.\n\nOpen-source orchestration for teams of AI agents.\n\n**If OpenClaw is an _employee_, Paperclip is the _company_.**\n\nPaperclip is a Node.js server and React UI that orchestrates a team of AI agents to run a business. Bring your own agents, assign goals, and track work and costs from one dashboard. Choose models and harnesses per agent while keeping your team\'s tasks, skills, permissions, and history in one place.\n\nIt looks like a task manager. Under the hood: org charts, budgets, governance, goal alignment, and agent coordination.\n\n**Manage business goals, not pull requests.**\n\n| | Step | Example |\n| ------ | --------------- | ------------------------------------------------------------------ |\n| **01** | Define the goal | _"Build the #1 AI note-taking app to $1M MRR."_ |\n| **02** | Hire the team | CEO, CTO, engineers, designers, marketers — any bot, any provider. |\n| **03** | Approve and run | Review strategy. Set budgets. Hit go. Monitor from the dashboard. |\n\n \n\n \n \n \n Works with \n OpenClaw \n Claude Code \n Codex \n Cursor + Cloud \n Gemini CLI \n \n \n OpenCode \n Pi \n Hermes + Gateway \n Grok Build \n Kimi Code \n \n \n\n If it can receive a heartbeat, it\'s hired. \n\n \n\nCustom processes, HTTP endpoints, and external adapter packages extend the roster. See the adapter overview for setup and capabilities.\n\n \n\nPaperclip is right for you if\n\n- ✅ You want to build **autonomous AI organizations**\n- ✅ You **coordinate many different agents** (OpenClaw, Codex, Claude, Cursor) toward a common goal\n- ✅ You have **20 simultaneous Claude Code terminals** open and lose track of what everyone is doing\n- ✅ You want agents running **autonomously 24/7**, but still want to audit work and chime in when needed\n- ✅ You want to **monitor costs** and enforce budgets\n- ✅ You want a process for managing agents that **feels like using a task manager**\n- ✅ You want to manage your autonomous businesses **from your phone**\n\n \n\nThe four pillars\n\nFour things have to work for an organization of AI agents to actually produce: the tasks, the org, the training, and the infrastructure. Paperclip is built around exactly those four pillars.\n\n \n \n \n \n \n\n| Pillar | Built for | What it covers |\n| --- | --- | --- |\n| **Agentic Task Manager** — Declare intent. Agents work. You verify the output. | Everyone, daily | Tasks, approvals & review gates · proactive agent coworkers · auditable routines & workflows · verify from diffs, screenshots & tests |\n| **Org Chart for Agents** — Roles, permissions & boundaries for humans and agents. | Managers | Mixed human + agent org chart · responsibilities, delegation, specialization · governance: who can do what · scoped secrets & company boundaries · connection permissions & responsible-user identities |\n| **Agent Employee Training** — Design, train & evaluate your AI employees. | Enablers | Skill Studio & shared org-wide skills · evals & saved test runs · active learning loops & quality metrics · performance reviews for agents · saved test inputs · skill version history & restore · reusable team templates |\n| **Agentic OS** — The infrastructure that makes the work run. | IT & platform | Cross-provider runtime: any model, any agent · sandboxing, integrations & MCP servers · SSO, GRC, RBAC & cost controls · data privacy, internal trace collection, compounding data value · personal & shared app connections · run history & opt-in tracing |\n\n \n\nFeatures\n\n \n \n \n 🔌 Bring Your Own Agent \nAny agent, any runtime, one org chart. If it can receive a heartbeat, it\'s hired.\n \n \n 🎯 Goal Alignment \nLink tasks and projects to your organization goals. Agents receive the goal context behind their work.\n \n \n 💓 Heartbeats \nAgents wake for assigned work, follow-up messages, or configured schedules. Delegation flows up and down the org chart.\n \n \n \n \n 💰 Cost Control \nCompany, agent, and project budgets . Track reported spend, get threshold alerts, and pause work at configured limits.\n \n \n 🏢 Multi-Organization \nOne deployment, many organizations. Separate tasks, agents, permissions, and activity histories for each.\n \n \n 🎫 Task Threads \nKeep conversations, plans, blockers, files, and run history attached to the work. Assign tasks to agents or people.\n \n \n \n \n 🛡️ Governance \nConfigure review and approval stages , approve hires, and pause, reassign, or stop work when needed.\n \n \n 📊 Org Chart \nHierarchies, roles, reporting lines. Your agents have a boss, a title, and a job description.\n \n \n 📱 Mobile Ready \nMonitor and manage your autonomous businesses from anywhere.\n \n \n \n \n 🔗 Apps & Connections \n Connect services such as GitHub, Notion, and Railway, or your own MCP server. Set gateway actions to Allowed, Ask first, or Off.\n \n \n 👥 Shared Agents, Personal Accounts \nChoose who can use a connection and which agents can access it . Managed GitHub operations can use the account of the person directing the work.\n \n \n 🧠 Skills & Skill Studio \nInstall or write shared skills , test them with saved inputs, inspect results, and restore earlier versions.\n \n \n \n \n 📅 Scheduled Routines \nRun recurring work on a schedule or trigger it through an API or webhook. Each run has a task, an owner, and a history.\n \n \n 📎 Artifacts & Feedback \nFind the files and documents agents produce . Preview supported formats and leave comments on specific passages in documents.\n \n \n 📦 Ready-Made Teams \nPreview and install teams with roles, skills, projects, and routines. Choose their runtimes and make the setup your own.\n \n \n \n\nExperimental **Agent Chat** and **chat/email connectors** add conversations with agents in Paperclip and through configured services such as Slack, Discord, Telegram, and AgentMail. Enable the relevant instance settings to try them.\n\n \n\nProblems Paperclip solves\n\n| Without Paperclip | With Paperclip |\n| ------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- |\n| ❌ You have 20 Claude Code tabs open and can\'t track which one does what. On reboot you lose everything. | ✅ Tasks are ticket-based, conversations are threaded, sessions persist across reboots. |\n| ❌ You manually gather context from several places to remind your bot what you\'re actually doing. | ✅ Context flows from the task up through the project and company goals — your agent always knows what to do and why. |\n| ❌ Folders of agent configs are disorganized and you\'re re-inventing task management, communication, and coordination between agents. | ✅ Paperclip gives you org charts, ticketing, delegation, and governance out of the box — so you run a company, not a pile of scripts. |\n| ❌ Runaway loops waste hundreds of dollars of tokens and max your quota before you even know what happened. | ✅ Spend tracking, budget alerts, and automatic pauses help you control the cost of ongoing work. |\n| ❌ You have recurring jobs (customer support, social, reports) and have to remember to manually kick them off. | ✅ Routines create assigned tasks on a schedule, with outputs and run history you can inspect. |\n| ❌ You have an idea, you have to find your repo, fire up Claude Code, keep a tab open, and babysit it. | ✅ Add a task in Paperclip. Your coding agent works on it until it\'s done. Management reviews their work. |\n\n \n\nWhy Paperclip is special\n\nPaperclip handles the hard orchestration details correctly.\n\n| | |\n| --------------------------------- | ------------------------------------------------------------------------------------------------------------- |\n| **Atomic task checkout.** | A single assignee and execution locks prevent competing runs from claiming the same task. |\n| **Persistent work context.** ', 'readme_raw_len': 21727, 'one_line_zh': '用于在工作场景中管理 Agent 的开源应用。', 'one_line_plain': 'Manage agents at work with this open-source app'}, {'id': 4247, 'key': 'klavis-ai-klavis-tree-main-mcp-servers-youtube', 'kind': 'mcp', 'name': 'Klavis-AI/YouTube', 'one_line': '🐍 📇 - Extract and convert YouTube video information', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Klavis AI: MCP integration platforms that let AI agents use tools reliably at any scale', 'url': 'https://github.com/Klavis-AI/klavis/tree/main/mcp_servers/youtube', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Klavis-AI/klavis/tree/main/mcp_servers/youtube', 'downloads': None, 'stars': 5817, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Klavis-AI.png?size=64', 'official_desc': '🐍 📇 - Extract and convert YouTube video information.', 'stars_own': 5817, 'stars_creator': None, 'stars_own_repo': 'Klavis-AI/klavis', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '🎯 Choose Your Solution\n\n \n \n \n \n \n \n Strata \n Intelligent connectors for your AI agent, optimize context window \n \n \n \n \n \n \n \n \n \n \n \n MCP Integrations \n 100+ prebuilt integrations out-of-the-box, with OAuth support \n \n \n \n \n \n \n \n \n \n \n \n MCP Sandbox \n scalable MCP environments for LLM training and RL \n \n \n \n \n \n \n \n \n \n \n \n\nQuick Start\n\nOption 1: Cloud-hosted - klavis.ai\n\nQuickstart guide →\n\nOption 2: Self-host\n\n \n\nOption 3: SDK\n\n \n\n \n\nOption 4: REST API\n\n \n\nResources\n\n- 📖 Documentation\n- 💬 Discord Community\n- 🐛 Report Issues\n- 🌐 Klavis AI Website\n\n---\n\n \n Made with ❤️ by the Klavis Team', 'readme_raw_len': 597, 'one_line_zh': '提取并转换 YouTube 视频信息。', 'one_line_plain': 'Extract and convert YouTube video information'}, {'id': 375, 'key': 'samuelgursky-davinci-resolve-mcp', 'kind': 'mcp', 'name': 'samuelgursky/davinci-resolve-mcp', 'one_line': '🐍 - DaVinci Resolve integration for video editing, color grading, media management and project control', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server integration for DaVinci Resolve Studio', 'url': 'https://github.com/samuelgursky/davinci-resolve-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/samuelgursky/davinci-resolve-mcp', 'downloads': None, 'stars': 3646, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/samuelgursky.png?size=64', 'official_desc': '🐍 - DaVinci Resolve integration for video editing, color grading, media management and project control.', 'stars_own': 3646, 'stars_creator': None, 'stars_own_repo': 'samuelgursky/davinci-resolve-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'DaVinci Resolve MCP Server\n\nEnglish | 简体中文\n\n \n \n \n -blue.svg)\n -18%20tools-blueviolet.svg)\n \n \n \n \n\nA Model Context Protocol (MCP) server that lets AI assistants control DaVinci Resolve Studio through the official Scripting API. It provides full API coverage plus guarded workflow helpers for editing, media pool organization, render setup, review markers, grading, Fusion, Fairlight, project lifecycle tasks, extension authoring, and source-safe media analysis.\n\n \n\nA local browser control panel ships with the server for inspecting Resolve state, running source-safe analysis, drilling into analyzed clips and shots, and editing analysis output inline. See the Control Panel Guide for the full tour.\n\nQuick Start\n\n \n\nBefore connecting, open DaVinci Resolve Studio and set **Preferences > General > External scripting using** to **Local**. (On the **free edition** that preference does not help — see Free edition below.) The npm launcher installs a managed copy under your user application-data directory, then runs the universal Python installer. The installer creates a virtual environment, detects Resolve paths, and can configure Claude Desktop, Claude Code, Cursor, VS Code, Windsurf, Zed, Continue, Cline, Roo Code, OpenCode, Codex CLI, and JetBrains IDEs.\n\nFor source installs:\n\n \n\nFor platform paths, client-specific config, and manual setup, see Installation and Configuration.\n\nThe installer and server check the latest GitHub release for MCP updates. Checks are best-effort and throttled; the server never blocks MCP startup for a prompt. The installer can prompt, snooze, ignore a release, disable checks, or apply an opt-in safe auto-update for clean git checkouts.\n\nFree edition (in-app bridge)\n\nBlackmagic gates *external* scripting to Studio: on the free edition\nscriptapp("Resolve") refuses a foreign process, whatever the preference says.\nThrough Resolve 21.0.x the **Workspace ▸ Scripts** menu was not gated — a script\nlaunched from it is handed the live resolve object (measured on free 21.0.3.7) —\nso the server can reach the free edition through a small script that runs *inside*\nResolve and re-exports it over an authenticated loopback listener. **Resolve 21.1\nmoved Python scripting to Studio.** On free 21.1 the Scripts menu no longer lists\n.py files at all (reported on Fedora 44 in #203; a Lua script in the same folder\nlists normally). Whether the Console still runs Python there is unconfirmed, so\ntreat the bridge as a 21.0.x path until that is measured.\n\n \n\nThe installer and MCP client use ~/.config/davinci-resolve-mcp/bridge.json\nby default. To keep the authenticated bridge config elsewhere, set\nDAVINCI_RESOLVE_BRIDGE_CONFIG while running the installer and in the MCP\nclient environment; both sides will use that path.\n\nOnce that listener is running it is used **automatically** whenever external\nscripting is unavailable — no environment variable required. Setting\nDAVINCI_RESOLVE_BRIDGE=1 *forces* the bridge instead: it becomes the only\ntransport tried, so a bridge that stops answering reports its own fault rather\nthan quietly falling back to another transport. Use it when the bridge is the\npath you intend to depend on.\n\nOn **macOS**, Resolve looks for Python 3 in exactly two places: the\nPYTHON3HOME environment variable, then /usr/local/bin/python3. Homebrew,\npyenv, uv and conda land in neither, so the script silently never appears in the\nmenu. A python.org install works because its installer creates\n/usr/local/bin/python3 — but you do not need one: point Resolve at the\ninterpreter you already have, no sudo required.\n\n \n\nUse launchctl setenv, not export — Resolve is launched from the Dock and\nnever sees your shell\'s environment. Restart Resolve afterwards. A Lua canary is\ninstalled alongside so you can tell "Python not detected" apart from a wrong\nfolder.\n\nTwo things that bite (#182). The prefix must contain **both**\nlib/libpython3.X.dylib and bin/python3 under that exact **unversioned**\nname — Homebrew\'s framework builds often ship only bin/python3.13, which is\nhalf a Python as far as Resolve is concerned, and the installer\'s preflight now\nsays so instead of reporting a usable prefix. And launchctl setenv **does not\nsurvive a reboot**; if scripts stop listing weeks later with no error, that is\nwhy. For something persistent, put an interpreter where Resolve already looks\n(this one needs sudo, and check that /usr/local/bin does not precede your\nnormal Python on PATH):\n\n \n\nValidated on free 21.0.3.7 and Studio 19.1.3.7, both macOS. The Windows paths\nadded in v2.70.1 (issue #106) shipped unverified; reports on free 21.0.1.11\n(issue #109) and free 21.0.3.7 (issue #112) have since shown the bridge\ninstalling, listing and serving from **both** %PROGRAMDATA% and %APPDATA% on\nWindows 11, so those paths are now confirmed rather than assumed. Linux is\nconfirmed as well: a report on free 20.3.2.9 (issue #129, Fedora 43) shows the\nbridge installing to ~/.local/share/DaVinciResolve/Fusion/Scripts/Utility,\nlisting against the system Python — Linux has none of this discovery problem —\nand serving end-to-end. No platform now rests on an assumption: macOS was\nvalidated directly, Windows and Linux on user reports.\n\nNote that the bridge holds its port for as long as it serves. Before v2.70.3 a\nWindows bridge could outlive Resolve and block the next session\'s listener; if\nyou are on an older build and a bridge stops answering, check for a stale\nfuscript.exe still holding the port.\n\nThis is the documented in-app path, not a licence circumvention, but Blackmagic\ncould close it — treat it as a supported-until-it-is-not tier. Loopback only,\nHMAC-signed requests, one-use nonces.\n\nLocal Control Panel\n\nLaunch the single-user local control panel from the repository root:\n\n \n\nThe command starts a loopback-only server and opens the control panel in your browser at a URL that carries a per-launch access token (http://127.0.0.1:8765/#token=…) — use that exact URL; the panel refuses requests without it. To have an AI coding agent do this, ask: **"Open the Resolve MCP control panel for this repo."** Agents should use venv/bin/python -m src.control_panel unless your Python environment is already active. Persisted analysis jobs refresh the local search index automatically after successful slices; the manual Build Index action is for rebuilding from existing reports.\n\nServer Modes\n\n| Mode | Entry point | Tools | Best for |\n|------|-------------|-------|----------|\n| Compound | src/server.py | 37 | Default mode for most assistants. Related Resolve operations are grouped behind action parameters to keep context usage low. |\n| Full / granular | src/server.py --full or src/resolve_mcp_server.py | 390 | Power users who want one MCP tool per Resolve API method. |\n\nThe compound server is recommended unless you specifically need the granular one-tool-per-method surface.\n\nAdvanced server — beyond the scripting API (optional, Node)\n\nThe same package ships a second, optional MCP server: **davinci-resolve-advanced-mcp** (bin\nbin/davinci-resolve-advanced-mcp.mjs). Where the Python server drives a *live* Resolve over the\nsanctioned scripting API, the advanced server does what the API **can\'t** — it reads and edits Resolve\n**files** (.drp / .drt / .drx) and applies DB/XML-level changes **with no Resolve running**, so it\nruns cloud *or* local. 18 tools: drp, drt, drx (per-clip grade codec **plus a deterministic,\noffline grading/QC catalog** — within-camera + cross-camera skin (v2 skin-line metric) + b-roll +\nneutral-patch WB matching, match-to-reference, saturation/black-balance, contrast-normalize, ASC CDL\nimport, lossless grade-transfer + season-look authoring, named-LUT attach, scope reads + intent tags,\nverify-grade, display-referred frame extraction, broadcast-legal QC), offline_ref,\nconform (frame-oracle conform/relink QC + lineage), color_trace (carry grades across a re-conform),\nfusion, audio_plan, fairlight (bus routing), audio, project_read, project_db, pipeline\n(a **DB-as-truth pipeline**: compile YAML', 'readme_raw_len': 29881, 'one_line_zh': '🐍 - DaVinci Resolve 集成：视频剪辑、调色、媒体管理与项目控制', 'one_line_plain': 'Integrate DaVinci Resolve for video editing, color grading, media management and project control'}, {'id': 8059, 'key': 'skillsmp-com-creators-dmtrkovalenko-fframes-skills-fframes-video', 'kind': 'skill', 'name': 'fframes-video', 'one_line': 'Create, animate, review and render videos in code with fframes (Rust, SVG scenes, ffmpeg encoding, Skia GPU…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/dmtrkovalenko/fframes/skills-fframes-video', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/dmtrkovalenko/fframes/skills-fframes-video', 'downloads': None, 'stars': 2774, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/dmtrKovalenko.png?size=64', 'official_desc': 'Create, animate, review and render videos in code with fframes (Rust, SVG scenes, ffmpeg encoding, Skia GPU rendering). Use whenever the user wants a video, animation, motion graphic, explainer, promo, social clip, title card, podcast visual, lyric/caption video or any rendered .mp4/.webm made progr', 'stars_own': None, 'stars_creator': 2870, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'dmtrkovalenko/fframes', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '用 fframes 以代码创建、制作动画、评审并渲染视频（Rust、SVG 场景、ffmpeg 编码与 Skia GPU…）', 'one_line_plain': 'Create, animate and render videos in code using fframes (Rust, SVG scenes, ffmpeg encoding, Skia GPU…)'}, {'id': 5707, 'key': 'skillsmp-com-creators-elvisun-newsjack-skills-coverage-reel', 'kind': 'skill', 'name': 'coverage-reel', 'one_line': 'Turn a user-owned coverage CSV and reviewed press-clip captures into an honest, source-linked earned-media…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/elvisun/newsjack/skills-coverage-reel', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/elvisun/newsjack/skills-coverage-reel', 'downloads': None, 'stars': 1542, 'installs': None, 'mentions': 5, 'score': 503.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/elvisun.png?size=64', 'official_desc': "Turn a user-owned coverage CSV and reviewed press-clip captures into an honest, source-linked earned-media dashboard and a motion-designed highlight reel (MP4) that scrolls each real article to the client's sentence and highlights it. Use for coverage reports, earned-media dashboards, campaign recap", 'stars_own': None, 'stars_creator': 1623, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'elvisun/newsjack', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '把覆盖数据与已审阅的媒体剪报整理成可溯源的真实报道合集。', 'one_line_plain': 'Turn a user-owned coverage CSV and reviewed press-clip captures into an honest, source-linked earned-media…'}, {'id': 528, 'key': 'kimtaeyoon83-mcp-server-youtube-transcript', 'kind': 'mcp', 'name': 'kimtaeyoon83/mcp-server-youtube-transcript', 'one_line': '📇 ☁️ - Fetch YouTube subtitles and transcripts for AI analysis', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'This is an MCP server that allows you to directly download transcripts of YouTube videos.', 'url': 'https://github.com/kimtaeyoon83/mcp-server-youtube-transcript', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/kimtaeyoon83/mcp-server-youtube-transcript', 'downloads': None, 'stars': 603, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/kimtaeyoon83.png?size=64', 'official_desc': '📇 ☁️ - Fetch YouTube subtitles and transcripts for AI analysis.', 'stars_own': 603, 'stars_creator': None, 'stars_own_repo': 'kimtaeyoon83/mcp-server-youtube-transcript', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'YouTube Transcript Server\n \n\n \n\nA Model Context Protocol server that enables retrieval of transcripts from YouTube videos. This server provides direct access to video captions and subtitles through a simple interface.\n\n \n\nInstalling via Smithery\n\nTo install YouTube Transcript Server for Claude Desktop automatically via Smithery:\n\n \n\nComponents\n\nTools\n\n- **get_transcript**\n - Extract transcripts from YouTube videos\n - Inputs:\n - url (string, required): YouTube video URL, Shorts URL, or video ID\n - lang (string, optional, default: "en"): Language code for transcript (e.g., \'ko\', \'en\'). Automatically falls back to available languages if requested language is not found.\n - include_timestamps (boolean, optional, default: false): Include timestamps in output (e.g., \'[0:05] text\')\n - strip_ads (boolean, optional, default: true): Filter out sponsorships, ads, and promotional content from transcript based on chapter markers\n\n- **analyze_video** (optional, requires a TwelveLabs API key)\n - Analyze a video with TwelveLabs Pegasus, a video-understanding model. Unlike get_transcript, this reasons over what is *shown* on screen, so it produces useful summaries and answers even for videos with little or no speech (demos, gameplay, b-roll, music videos).\n - Inputs:\n - url (string, required): A publicly reachable **direct video URL** (e.g. an .mp4/.mov/.webm link or a pre-signed URL). TwelveLabs fetches the file server-side, so a YouTube watch page URL will not work — it serves HTML, not a raw video stream.\n - prompt (string, optional): Instruction or question for the model (e.g. "Summarize this video in 3 sentences" or "What products are shown?"). Defaults to a general summary.\n - model (string, optional, default: "pegasus1.2"): Pegasus model to use ("pegasus1.2" or "pegasus1.5").\n - max_tokens (number, optional, default: 2048): Maximum response length in tokens.\n - Requires the TWELVELABS_API_KEY environment variable. The transcript tool works without it; analysis is fully opt-in. Grab a free key at twelvelabs.io — there\'s a generous free tier.\n\nKey Features\n\n- Support for multiple video URL formats (including YouTube Shorts)\n- Language-specific transcript retrieval with automatic fallback\n- Optional timestamps for referencing specific moments\n- Built-in ad/sponsorship filtering (enabled by default)\n- Zero external dependencies for transcript fetching\n- Detailed metadata in responses\n\nConfiguration\n\nTo use with Claude Desktop, add this server configuration:\n\n \n\nTo enable the optional analyze_video tool, add your TwelveLabs API key:\n\n \n\nInstall via tool\n\nmcp-get A command-line tool for installing and managing Model Context Protocol (MCP) servers.\n\n \n\nAwesome-mcp-servers \nawesome-mcp-servers A curated list of awesome Model Context Protocol (MCP) servers.\n\nDevelopment\n\nPrerequisites\n\n- Node.js 18 or higher\n- npm or yarn\n\nSetup\n\nInstall dependencies:\n \n\nBuild the server:\n \n\nFor development with auto-rebuild:\n \n\nTesting\n\n \n\nDebugging\n\nSince MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector for development:\n\n \n\nRunning evals\n\nThe evals package loads an mcp client that then runs the index.ts file, so there is no need to rebuild between tests. You can load environment variables by prefixing the npx command. Full documentation can be found here.\n\n \nError Handling\n\nThe server implements robust error handling for common scenarios:\n- Invalid video URLs or IDs\n- Unavailable transcripts\n- Language availability issues\n- Network errors\n\nUsage Examples\n\n1. Get transcript by video URL:\n \n\n2. Get transcript by video ID:\n \n\n3. Get transcript from YouTube Shorts:\n \n\n4. Get transcript with timestamps:\n \n\n5. Get raw transcript without ad filtering:\n \n\n6. How to Extract YouTube Subtitles in Claude Desktop App\n \n\nSecurity Considerations\n\nThe server:\n- Validates all input parameters\n- Handles YouTube API errors gracefully\n- Implements timeouts for transcript retrieval\n- Provides detailed error messages for troubleshooting\n\nLicense\n\nThis MCP server is licensed under the MIT License. See the LICENSE file for details.', 'readme_raw_len': 4102, 'one_line_zh': '📇 ☁️ - 获取 YouTube 字幕与文字记录，供 AI 分析', 'one_line_plain': 'Fetch YouTube subtitles and transcripts for AI analysis'}, {'id': 4173, 'key': 'anaisbetts-mcp-youtube', 'kind': 'mcp', 'name': 'anaisbetts/mcp-youtube', 'one_line': '📇 ☁️ - Fetch YouTube subtitles', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model-Context Protocol Server for YouTube', 'url': 'https://github.com/anaisbetts/mcp-youtube', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/anaisbetts/mcp-youtube', 'downloads': None, 'stars': 551, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/anaisbetts.png?size=64', 'official_desc': '— A Model-Context Protocol Server for YouTube ☆`550`', 'stars_own': 551, 'stars_creator': None, 'stars_own_repo': 'anaisbetts/mcp-youtube', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'YouTube MCP Server\n\nUses yt-dlp to download subtitles from YouTube and connects it to claude.ai via Model Context Protocol. Try it by asking Claude, "Summarize the YouTube video >". Requires yt-dlp to be installed locally e.g. via Homebrew.\n\nHow do I get this working?\n\n1. Install yt-dlp (Homebrew and WinGet both work great here)\n1. Now, install this via mcp-installer, use the name @anaisbetts/mcp-youtube', 'readme_raw_len': 407, 'one_line_zh': '📇 ☁️ - 获取 YouTube 字幕', 'one_line_plain': 'Fetch YouTube subtitles'}, {'id': 5589, 'key': 'abubakrchan-product-launch-motion', 'kind': 'repo', 'name': 'AbubakrChan/product-launch-motion', 'one_line': 'Claude Code skill for Dribbble-level product launch videos from code', 'one_line_ok': 1, 'why_bad': None, 'fields': '["Agent 技能"]', 'desc_raw': 'Claude Code skill for Dribbble-level product launch videos from code. One shape that never cuts, springs, a cursor that drives every change, beat-synced SFX, 60 fps motion blur. v2: use with Claude Opus 5.5.', 'url': 'https://github.com/AbubakrChan/product-launch-motion', 'source': 'github', 'source_url': 'https://github.com/AbubakrChan/product-launch-motion', 'downloads': None, 'stars': 323, 'installs': None, 'mentions': 2, 'score': 203.38, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/AbubakrChan.png?size=64', 'official_desc': 'Claude Code skill for Dribbble-level product launch videos from code. One shape that never cuts, springs, a cursor that drives every change, beat-synced SFX, 60 fps motion blur. v2: use with Claude Opus 5.5.', 'stars_own': 323, 'stars_creator': None, 'stars_own_repo': 'AbubakrChan/product-launch-motion', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx skills', 'install_src': 'HEAD/README.md', 'install_conf': 'low', 'desc_src': 'github-api', 'readme_body': "Product Launch Motion — a Claude Code Skill\n\nPaste your website URL. Get a product launch video. That's it.\n\n \n\nAlready installed it? Get the latest version with npx skills update.\n\n \n\n**No Claude Code? Get the film made for you: a polished launch video in 30 minutes, from $20 → launchvideos.app**\n\nThen, in Claude Code:\n\n> Make a launch video for https://your-website.com\n\nClaude reads your site, writes the story, directs every scene and renders a 60 fps MP4 with\nmusic, synced sound effects and real motion blur. No editing, no After Effects, no templates.\nThe film above started the same way: a URL, then a few rounds of notes.\n\n \n v2 is here. Use it with Claude Opus 5.5 to get results like these films. \n\nThis skill has been rebuilt on a newer, simpler stack: plain HTML and closed-form springs\ninstead of GSAP and HyperFrames, music-first timing instead of voiceover, and Playwright\nsubframes with ffmpeg motion blur. **Use it with Claude Opus 5.5 to get results like these\nfilms.** The original v1 (190 ⭐, thank you) is kept in legacy/.\n\n \n\n> 🔊 **Download it and turn the sound on.** The preview above is silent. The real film is cut to the beat\n> of its music, and every click, whoosh and number roll lands on a drum hit.\n> **Download the film (1080p, 60 fps, with sound)**\n> to watch it as intended.\n\n30 s, made for cheerful.ai. No After Effects, no stock templates,\nno AI video model: every frame is HTML computed from time. Bonus: the same film with a\nWebGL glass pass\n(refractive 3D shapes floating through every scene).\n\n \n \n \n\n**▶ One shape, never cut**\n(14 s seamless loop, 1440×1440, with sound)\n\nThe idea the whole skill is built on. One element morphs through eleven UI states:\nbutton → loader → check → dynamic island → video reel → slider → toggle → tabs → chart →\n⌘K palette → toast → button. Its content swaps with a short blur, the cursor does every\nclick and drag, and the last frame is the first frame.\n\n \n\n---\n\nWhat makes it look expensive\n\nThese are the rules the skill enforces. Each one came from a review note on a real film\n(references/prompts.md has the prompts and notes, verbatim).\n\n- **One shape, never cut.** Every state is the same element morphing size, radius and\n colour. Transitions are morphs, camera pushes, or zooms into an element that *becomes*\n the next scene.\n- **Blur content swaps.** Text inside a morphing container exits short and early, then the\n new text springs in. Nothing overlaps.\n- **Springs, a tiny overshoot at most.** Closed-form damped springs, never bouncy easing.\n Toggle knobs and tab indicators stretch because their two edges ride different springs.\n- **A cursor drives everything.** Real clicks with press-scale and ripple, real drags that\n spring back on release. The product's own agent can take over as a second cursor.\n- **The camera frames every state.** An empty frame is a bug. Pull-backs reveal scale: one\n email becomes a wall of hundreds.\n- **Music first.** The beat grid is measured with numpy, the drop lands on the big moment,\n and every synthesized UI sound is placed by its own measured peak, so it hits the beat.\n- **Real data, original scenes.** Real numbers on odometers, customer logos slammed one per\n beat, and nothing copied from the product's website.\n- **You get a director.** It makes the creative calls itself and logs each one as a\n DECISION: line you can overturn in one reply.\n\nWhat's in the box\n\n| Path | What it is |\n|---|---|\n| SKILL.md | The creative direction and the process Claude follows |\n| references/prompts.md | The original prompts and every review note, verbatim. Swap the product and reuse them |\n| references/technique.md | Springs, camera, cursor, odometer, audio and render code patterns |\n| legacy/ | The v1 skill: voiceover-led films with GSAP + HyperFrames, 34 traps |\n\nRequirements\n\n| Need | For |\n|---|---|\n| Claude Code with **Opus 5.5** | Directing and building the film. Smaller models follow the rules but miss the detail |\n| Node ≥ 20 + Playwright | Rendering |\n| FFmpeg | Motion blur, encoding, loudness checks |\n| Python 3 + numpy | Beat grid and synthesized SFX |\n| A ~120 BPM royalty-free song | Optional. Check each track's licence; some free libraries mix in restricted items |\n\nEverything runs locally and nothing costs money.\n\nHow it works\n\n \n\nBecause every frame is a pure function of time, renders are deterministic: seek to any\nmoment and you get the same pixels, which is what makes per-beat QA and parallel rendering\npossible.\n\nv1 → v2\n\n| | v1 (legacy) | v2 |\n|---|---|---|\n| Timing | Locked to voiceover words | Locked to the music's measured beat grid |\n| Motion | GSAP timelines | Closed-form springs, pure seek(t) |\n| Renderer | HyperFrames | Playwright subframes + ffmpeg tmix motion blur |\n| Structure | Shots and transitions | One shape that never cuts |\n| Sound | Library SFX, levelled | Synthesized SFX placed by measured peak |\n| Depth | CSS camera rig | Camera springs + optional WebGL glass |\n\nv1 is still the better choice for a narrated explainer. It lives on in legacy/.\n\nUpdating\n\n- **Installed v1 (before 13 October 2026)?** Run npx skills update. Your product-launch-motion skill is\n replaced by v2.\n- **Installed v2 on 13 or 14 October 2026?** It was briefly named motion-launch-video, so update can't\n find it. Reinstall once:\n \n- **Want to keep v1 for narrated explainers?** Install it alongside v2:\n npx skills add AbubakrChan/product-launch-motion/legacy\n\nMade by\n\nMuhammad Abubakr, who builds Cheerful. Made a film with this skill?\nTag me on X and I'll share it.\n\n \n \n\nLicense\n\nMIT for the skill and references — see LICENSE.\n\n**The sample films do not travel.** They were made for Cheerful and are theirs; see\nNOTICE for that and for third-party credits (music, footage, fonts).", 'readme_raw_len': 5744, 'one_line_zh': '从代码生成 Dribbble 级产品发布视频的 Claude Code 技能', 'one_line_plain': 'Claude Code skill that lets you create Dribbble-level product launch videos from code'}, {'id': 1908, 'key': 'cyberchitta-llm-context-py', 'kind': 'mcp', 'name': 'cyberchitta/llm-context.py', 'one_line': '🐍 🏠 - Share code context with LLMs via MCP or the clipboard', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Share code with LLMs via Model Context Protocol or clipboard. Rule-based customization enables easy switching between different tasks (like code review and documentation). Includes smart code outlining.', 'url': 'https://github.com/cyberchitta/llm-context.py', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/cyberchitta/llm-context.py', 'downloads': None, 'stars': 307, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/cyberchitta.png?size=64', 'official_desc': '🐍 🏠 - Share code context with LLMs via MCP or the clipboard.', 'stars_own': 307, 'stars_creator': None, 'stars_own_repo': 'cyberchitta/llm-context.py', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'LLM Context\n\n \n \n \n\n**Context curation your coding agent does for itself.** lc-init installs a skill that teaches the agent to work out which files a task actually needs, write that down as a composable rule, check the rule against the codebase, and pack the result — for its own context, for a chat you paste into, or for a sub-agent it dispatches.\n\nGetting the right context into an LLM is friction-heavy: finding and copying files by hand wastes time, too much context hits token limits, too little misses what matters, and follow-up file requests mean more manual fetching. The usual answers are to send everything, or to have a person curate by hand. A rule describes the selection once, and it is a thing an agent can author, verify and reuse — the tooling handles packing, follow-up fetches, and change tracking.\n\nDocumentation lives in the skill\n\nThe full documentation is the **lc-curate-context skill**, installed into your project by lc-init. It is written to be read by an agent, and it is the only copy — this README is a landing page, not a manual.\n\n| File | Contents |\n| --- | --- |\n| SKILL.md | Writing a rule; the workflow; packing for a sub-agent |\n| COMMANDS.md | CLI and MCP reference, including output routing |\n| SYNTAX.md | Rule file schema and every field |\n| PATTERNS.md | Reusable rule shapes |\n| EXAMPLES.md | Worked examples |\n| TROUBLESHOOTING.md | Failure cases |\n\nFind them at .claude/skills/lc-curate-context/ after lc-init. In Claude Code the skill loads automatically; elsewhere, read the files directly.\n\nInstallation\n\n \n\nUpgrading: uv tool upgrade llm-context, then any lc-* command refreshes the skill, rules and templates in place.\n\nThree ways to use it\n\n**Your coding agent, curating for itself** — this is the primary path. After lc-init the agent has the lc-curate-context skill, so "get me focused context for the auth refactor" becomes a rule it writes and verifies without you naming files.\n\n \n\nlc-preview reports the exact file lists, the size, and — via the code graph — files defining symbols the selection uses but does not include. That last section is how the agent catches the module it forgot, before spending a turn on a wrong answer.\n\n**A sub-agent, via a pipe** — a dispatcher writes the prompt, llm-context supplies the files.\n\n \n\n-r routes output to stdout; without it lc-context copies to the clipboard, so a pipe or redirect gets nothing but log lines. These commands take no bare positional rule name — always -r. -a renders the pack\'s fetch instructions as shell commands the child runs itself, so from the repo root it can call lc-missing against the pack\'s timestamp for anything left out. See SKILL.md "Packing for a Sub-Agent".\n\n**A chat, via MCP or the clipboard** — for models that aren\'t driving a terminal.\n\n \n\nWith MCP the model pulls files it wasn\'t given and notices ones that changed underneath it, through lc_missing, lc_changed, lc_outlines and lc_preview. Without it, lc-select then lc-context puts the pack on your clipboard to paste anywhere.\n\nRules in one minute\n\nA rule is YAML frontmatter plus optional markdown:\n\n \n\nYou rarely write one by hand — the skill does, and lc-preview is how it checks its work. Rules compose, and are named by category: prm- produces a context, flt- controls file inclusion, ins- supplies guidelines, sty- enforces coding standards, exc- configures excerpting. Files you expect to edit go in full-files; supporting code goes in excerpted-files, where it is reduced to signatures and definitions. See SYNTAX.md and PATTERNS.md.\n\nWhat a generated context contains\n\nComplete contents for full files, structural excerpts for the rest, a filtered file listing marking what is and isn\'t included, and a timestamp that lc-missing and lc-changed resolve against.\n\nA pack is always partial — the listing is filtered by your .gitignore files and by the rule before anything is marked excluded, so files can exist that it never mentions. The header reports how many files are full, outlined and excerpted, so a consumer can check what it actually received rather than trusting the rule.\n\nLearn More\n\n- Design Philosophy — why llm-context exists\n- Real-world Examples — using full context effectively\n\nLicense\n\nApache License, Version 2.0. See LICENSE for details.\n\n---\n\nDeveloped in collaboration with several Claude models and Groks, using LLM Context itself to share code during development. All code is heavily human-curated by @restlessronin.', 'readme_raw_len': 4444, 'one_line_zh': '通过 MCP 或剪贴板与 LLM 共享代码上下文', 'one_line_plain': 'Share code context with LLMs via MCP or the clipboard'}, {'id': 323, 'key': 'burningion-video-editing-mcp', 'kind': 'mcp', 'name': 'burningion/video-editing-mcp', 'one_line': '🐍 - Add, analyze, search and generate video edits from your Video Jungle collection', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP Interface for Video Jungle', 'url': 'https://github.com/burningion/video-editing-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/burningion/video-editing-mcp', 'downloads': None, 'stars': 294, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/burningion.png?size=64', 'official_desc': '🐍 - Add, analyze, search and generate video edits from your Video Jungle collection.', 'stars_own': 294, 'stars_creator': None, 'stars_own_repo': 'burningion/video-editing-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Video Editor MCP server\n\n \n\nSee a demo here: https://www.youtube.com/watch?v=KG6TMLD8GmA\n\nUpload, edit, search, and generate videos from everyone\'s favorite LLM and Video Jungle.\n\nYou\'ll need to sign up for an account at Video Jungle in order to use this tool, and add your API key.\n\n \n\nComponents\n\nResources\n\nThe server implements an interface to upload, generate, and edit videos with:\n- Custom vj:// URI scheme for accessing individual videos and projects\n- Each project resource has a name, description\n- Search results are returned with metadata about what is in the video, and when, allowing for edit generation directly\n\nPrompts\n\nComing soon.\n\nTools\n\nThe server implements a few tools:\n- add-video\n - Add a Video File for analysis from a URL. Returns an vj:// URI to reference the Video file\n- create-videojungle-project\n - Creates a Video Jungle project to contain generative scripts, analyzed videos, and images for video edit generation\n- edit-locally\n - Creates an OpenTimelineIO project and downloads it to your machine to open in a Davinci Resolve Studio instance (Resolve Studio _must_ already be running before calling this tool.) \n- generate-edit-from-videos\n - Generates a rendered video edit from a set of video files\n- generate-edit-from-single-video\n - Generate an edit from a single input video file\n- get-project-assets\n - Get assets within a project for video edit generation.\n- search-videos\n - Returns video matches based upon embeddings and keywords\n- update-video-edit\n - Live update a video edit\'s information. If Video Jungle is open, edit will be updated in real time.\n\nUsing Tools in Practice\n\nIn order to use the tools, you\'ll need to sign up for Video Jungle and add your API key.\n\n**add-video**\n\nHere\'s an example prompt to invoke the add-video tool:\n\n \n\nThis will download a video from a URL, add it to your library, and analyze it for retrieval later. Analysis is multi-modal, so both audio and visual components can be queried against.\n\n**search-videos**\n\nOnce you\'ve got a video downloaded and analyzed, you can then do queries on it using the search-videos tool:\n\n \n\nSearch results contain relevant metadata for generating a video edit according to details discovered in the initial analysis.\n\n**search-local-videos**\n\nYou must set the environment variable LOAD_PHOTOS_DB=1 in order to use this tool, as it will make Claude prompt to access your files on your local machine.\n\nOnce that\'s done, you can search through your Photos app for videos that exist on your phone, using Apple\'s tags.\n\nIn my case, when I search for "Skateboard", I get 1903 video files.\n\n \n\n**generate-edit-from-videos**\n\nFinally, you can use these search results to generate an edit:\n\n \n\n(Currently), the video edits tool relies on the context within the current chat. \n\n**generate-edit-from-single-video**\n\nFinally, you can cut down an edit from a single, existing video:\n\n \n\nConfiguration\n\nYou must login to Video Jungle settings, and get your API key. Then, use this to start Video Jungle MCP:\n\n \n\nTo allow this MCP server to search your Photos app on MacOS:\n\n \nQuickstart\n\nInstall\n\nInstalling via Smithery\n\nTo install Video Editor for Claude Desktop automatically via Smithery:\n\n \n\nClaude Desktop\n\nYou\'ll need to adjust your claude_desktop_config.json manually:\n\nOn MacOS: ~/Library/Application\\ Support/Claude/claude_desktop_config.json\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n \n Published Server Configuration \n \n \n \n Development/Unpublished Servers Configuration \n \n \n\n With local Photos app access enabled (search your Photos app):\n\n \n\n \n\nBe sure to replace the directories with the directories you\'ve placed the repository in on **your** computer.\n\nDevelopment\n\nBuilding and Publishing\n\nTo prepare the package for distribution:\n\n1. Sync dependencies and update lockfile:\n \n\n2. Build package distributions:\n \n\nThis will create source and wheel distributions in the dist/ directory.\n\n3. Publish to PyPI:\n \n\nNote: You\'ll need to set PyPI credentials via environment variables or command flags:\n- Token: --token or UV_PUBLISH_TOKEN\n- Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD\n\nMCP Server Registry\n\n \n\nDebugging\n\nSince MCP servers run over stdio, debugging can be challenging. For the best debugging\nexperience, we strongly recommend using the MCP Inspector.\n\nYou can launch the MCP Inspector via npm with this command:\n\n(Be sure to replace YOURDIRECTORY and YOURAPIKEY with the directory this repo is in, and your Video Jungle API key, found in the settings page.)\n\n \n\nUpon launching, the Inspector will display a URL that you can access in your browser to begin debugging.\n\nAdditionally, I\'ve added logging to app.log in the project directory. You can add logging to diagnose API calls via a:\n\n \n\nA reasonable way to follow along as you\'re workin on the project is to open a terminal session and do a:', 'readme_raw_len': 4884, 'one_line_zh': '🐍 - 对 Video Jungle 收藏中的视频进行添加、分析、搜索和剪辑生成', 'one_line_plain': 'Add, analyze, search and generate video edits from your Video Jungle collection'}, {'id': 7809, 'key': 'faviovazquez-showtime', 'kind': 'repo', 'name': 'FavioVazquez/showtime', 'one_line': 'A local video studio for your coding agent', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '["Agent 技能"]', 'desc_raw': '', 'url': 'https://github.com/FavioVazquez/showtime', 'source': 'github', 'source_url': 'https://github.com/FavioVazquez/showtime', 'downloads': None, 'stars': 265, 'installs': None, 'mentions': 5, 'score': 503.52, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-16', 'last_seen': '2026-10-19', 'times_seen': 4, 'icon': 'https://github.com/FavioVazquez.png?size=64', 'official_desc': 'A local video studio for your coding agent. Describe a video in one sentence: your agent directs, your machine renders. Claude Code, Codex, Cursor, Devin and more. No API keys, no uploads.', 'stars_own': 265, 'stars_creator': None, 'stars_own_repo': 'FavioVazquez/showtime', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx skills', 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '面向编码 Agent 的本地视频工作室。', 'one_line_plain': 'Edit and run code locally with a video studio built for coding agents'}, {'id': 4299, 'key': 'seym0n-tiktok-mcp', 'kind': 'mcp', 'name': 'Seym0n/tiktok-mcp', 'one_line': '📇 ☁️ - Interact with TikTok videos', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol (MCP) with TikTok integration', 'url': 'https://github.com/Seym0n/tiktok-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Seym0n/tiktok-mcp', 'downloads': None, 'stars': 203, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Seym0n.png?size=64', 'official_desc': '— Model Context Protocol (MCP) with TikTok integration ☆`201`', 'stars_own': 203, 'stars_creator': None, 'stars_own_repo': 'Seym0n/tiktok-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "TikTok MCP\n\n \n\nThe TikTok MCP integrates TikTok access into Claude AI and other apps via TikNeuron. This TikTok MCP allows you to\n- analyze TikTok videos to determine virality factors\n- get content from TikTok videos\n- chat with TikTok videos\n\nAvailable Tools\n\ntiktok_get_subtitle\n\n**Description:** \nGet the subtitle (content) for a TikTok video url. This is used for getting the subtitle, content or context for a TikTok video. If no language code is provided, the tool will return the subtitle of automatic speech recognition.\n\n**Input Parameters:**\n- tiktok_url (required): TikTok video URL, e.g., https://www.tiktok.com/@username/video/1234567890 or https://vm.tiktok.com/1234567890\n- language_code (optional): Language code for the subtitle, e.g., en for English, es for Spanish, fr for French, etc.\n\ntiktok_get_post_details\n\n**Description:** \nGet the details of a TikTok post. Returns the details of the video like:\n- Description\n- Video ID\n- Creator username\n- Hashtags\n- Number of likes, shares, comments, views and bookmarks\n- Date of creation\n- Duration of the video\n- Available subtitles with language and source information\n\n**Input Parameters:**\n- tiktok_url (required): TikTok video URL, e.g., https://www.tiktok.com/@username/video/1234567890 or https://vm.tiktok.com/1234567890, or just the video ID like 7409731702890827041\n\ntiktok_search\n\n**Description:** \nSearch for TikTok videos based on a query. Returns a list of videos matching the search criteria with their details including description, video ID, creator, hashtags, engagement metrics, date of creation, duration and available subtitles, plus pagination metadata for continuing the search.\n\n**Input Parameters:**\n- query (required): Search query for TikTok videos, e.g., 'funny cats', 'dance', 'cooking tutorial'\n- cursor (optional): Pagination cursor for getting more results\n- search_uid (optional): Search session identifier for pagination\n\nInstall via MCPB (Easiest)\n\nThe easiest way to install the TikTok MCP in Claude Desktop is via the MCPB bundle — no cloning or building required.\n\n1. Download the latest tiktok-mcp.mcpb from the Releases page\n2. Open the .mcpb file — Claude Desktop will show an installation dialog\n3. Enter your TikNeuron API Key when prompted\n\nThat's it. Claude Desktop handles the rest automatically.\n\nRequirements\n\nFor this TikTok MCP, you need\n- NodeJS v18 or higher (https://nodejs.org/)\n- Git (https://git-scm.com/)\n- TikNeuron Account and MCP API Key (https://tikneuron.com/tools/tiktok-mcp)\n\nSetup\n\n1. Clone the repository\n \n\n2. Install dependencies\n \n\n3. Build project\n \n\nThis creates the file build\\index.js\n\nUsing in Claude AI\n\nAdd the following entry to mcpServers:\n\n \n\nand replace path with the path to TikTok MCP and XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX with TIkNeuron API Key\n\nso that mcpServers will look like this:", 'readme_raw_len': 2832, 'one_line_zh': '与 TikTok 视频进行交互', 'one_line_plain': 'Interact with TikTok videos'}, {'id': 3427, 'key': 'lfnovo-content-core', 'kind': 'mcp', 'name': 'lfnovo/content-core', 'one_line': '🐍 🏠 - Extract content from web pages, PDFs, Word documents, YouTube transcripts, audio and video with…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Extract what matters from any media source', 'url': 'https://github.com/lfnovo/content-core', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/lfnovo/content-core', 'downloads': None, 'stars': 175, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/lfnovo.png?size=64', 'official_desc': '🐍 🏠 - Extract content from web pages, PDFs, Word documents, YouTube transcripts, audio and video with automatic engine selection and structured JSON output.', 'stars_own': 175, 'stars_creator': None, 'stars_own_repo': 'lfnovo/content-core', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Content Core\n\n \n \n \n \n \n \n \n \n\nExtract, process, and summarize content from URLs, files, and text through a unified async Python API, CLI, or MCP server.\n\nSupported Formats\n\n| Category | Formats |\n|----------|---------|\n| Web | URLs, HTML pages, YouTube videos, Reddit posts |\n| Documents | PDF, DOCX, PPTX, XLSX, ODT, ODS, ODP, EPUB, HTML, Markdown, plain text |\n| Media | MP3, WAV, M4A, FLAC, OGG (audio); MP4, AVI, MOV, MKV (video) |\n\nQuick Start\n\n \n\n \n\nOr with zero install:\n\n \n\nCLI Usage\n\nContent Core provides a unified content-core command with subcommands for extraction, summarization, and MCP server.\n\nExtract\n\n \n\nSummarize\n\n \n\nMCP Server\n\n \n\nConfiguration\n\n \n\nConfig is stored in ~/.content-core/config.toml. Priority: command flags > env vars > config file > defaults.\n\nZero-Install with uvx\n\nAll commands work without installation using uvx:\n\n \n\nPython API\n\nExtraction\n\n \n\nSummarization\n\n \n\nConfiguration\n\n \n\nMCP Integration\n\nContent Core includes a Model Context Protocol (MCP) server for use with Claude Desktop and other MCP-compatible applications.\n\n \n \n \n\nAdd to your claude_desktop_config.json:\n\n \n\nThe MCP server exposes two tools: extract_content and summarize_content. Both return plain text.\n\nFor detailed setup, see the MCP documentation.\n\nAgent Skill (Claude Code & Codex)\n\nContent Core ships an Agent Skill that teaches AI agents how to use it for extracting content from external sources. This repository is also a plugin marketplace, so the skill installs natively in both harnesses.\n\n**Claude Code** — add the marketplace and install the plugin:\n\n \n\n**Codex** — the repository carries a Codex plugin manifest (.codex-plugin/plugin.json) and marketplace catalog (.agents/plugins/marketplace.json) pointing at the same skill.\n\n**Manual fallback** — copy the skill file directly into your project:\n\n \n\nOnce installed, the agent can use content-core to extract content from URLs, documents, and media files — either via CLI (uvx content-core) or MCP if configured.\n\nAI Providers\n\nContent Core uses Esperanto to support multiple LLM and STT providers. Switch providers by changing the config — no code changes needed:\n\n \n\nSupported providers include OpenAI, Anthropic, Google, Groq, DeepSeek, Ollama, and more. See the Esperanto documentation for the full list.\n\nConfiguration\n\nContent Core uses ContentCoreConfig powered by pydantic-settings. Settings are resolved in priority order: constructor args > env vars (CCORE_*) > config file (~/.content-core/config.toml) > defaults.\n\nEnvironment Variables\n\n| Variable | Description | Default |\n|----------|-------------|---------|\n| CCORE_URL_ENGINE | URL extraction engine (auto, simple, firecrawl, jina, crawl4ai) | auto |\n| CCORE_DOCUMENT_ENGINE | Document extraction engine (auto, simple, docling) — docling raises ConfigurationError if the extra is not installed; auto falls back silently | auto |\n| CCORE_AUDIO_CONCURRENCY | Concurrent audio transcriptions (1-10) | 3 |\n| CCORE_AUDIO_SEGMENT_MINUTES | Length of the segments long audio is split into; 0 sends the file whole | 10 |\n| CRAWL4AI_API_URL | Crawl4AI Docker API URL (omit for local browser mode) | - |\n| CRAWL4AI_API_TOKEN | Bearer token for the Crawl4AI Docker API (required by Crawl4AI >= 0.9.0) | - |\n| FIRECRAWL_API_URL | Custom Firecrawl API URL for self-hosted instances or Firecrawl-compatible backends (e.g. fastCRW) | - |\n| CCORE_FIRECRAWL_PROXY | Firecrawl proxy mode (auto, basic, stealth) | auto |\n| CCORE_FIRECRAWL_WAIT_FOR | Wait time in ms before extraction | 3000 |\n| CCORE_LLM_PROVIDER | LLM provider for summarization | - |\n| CCORE_LLM_MODEL | LLM model for summarization | - |\n| CCORE_STT_PROVIDER | Speech-to-text provider | - |\n| CCORE_STT_MODEL | Speech-to-text model | - |\n| CCORE_STT_TIMEOUT | Speech-to-text timeout in seconds | - |\n| CCORE_YOUTUBE_LANGUAGES | Preferred YouTube transcript languages | - |\n| CCORE_YOUTUBE_COOKIES_FILE | Path to a Netscape cookies.txt for YouTube transcripts (missing file raises ConfigurationError) | - |\n| CCORE_YOUTUBE_PROXY | Proxy URL for YouTube transcripts, e.g. http://user:pass@host:port | - |\n\nAPI keys for external services are set via their standard environment variables (e.g., OPENAI_API_KEY, FIRECRAWL_API_KEY, JINA_API_KEY).\n\nProxy Configuration\n\nContent Core reads standard HTTP_PROXY / HTTPS_PROXY / NO_PROXY environment variables automatically. No additional configuration is needed.\n\nYouTube on Blocked Networks\n\nIf YouTube extraction fails with IpBlocked/RequestBlocked on a flagged IP (cloud hosts, VPNs), either point CCORE_YOUTUBE_COOKIES_FILE at a cookies.txt exported from a logged-in browser (re-export it when the cookies expire), or set CCORE_YOUTUBE_PROXY to a **residential** proxy — datacenter proxies get blocked or CAPTCHA'd. See docs/usage.md.\n\nOptional Dependencies\n\n \n\nUsing with LangChain\n\nWhen installed with the langchain extra, Content Core provides LangChain-compatible tool wrappers:\n\n \n\nDocumentation\n\n- Usage Guide -- Python API details, configuration, and examples\n- Processors -- How content extraction works for each format\n- MCP Server -- Claude Desktop and MCP integration\n\nDevelopment\n\n \n\nLicense\n\nThis project is licensed under the MIT License.\n\nContributing\n\nContributions are welcome! Please see our Contributing Guide for details.", 'readme_raw_len': 5279, 'one_line_zh': '从网页、PDF、Word 文档、YouTube 字幕、音频和视频中提取内容', 'one_line_plain': 'Content extraction tool lets users pull text from web pages, PDFs, Word documents, YouTube transcripts, and audio or video files'}, {'id': 3165, 'key': 'video-creator-ffmpeg-mcp-git', 'kind': 'mcp', 'name': 'video-creator/ffmpeg-mcp', 'one_line': 'Local video search, trimming, stitching and playback through conversation, using the ffmpeg command line', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/video-creator/ffmpeg-mcp.git', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/video-creator/ffmpeg-mcp.git', 'downloads': None, 'stars': 153, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/video-creator.png?size=64', 'official_desc': 'he following tools: find_video_path The parameters are directory and file name, file name can be complete, or is not suffixed, recursive search in the directory, return the full path get_video_info The parameters are video path, return the video info, linkes duration/fps/codec/width/height.', 'stars_own': 153, 'stars_creator': None, 'stars_own_repo': 'video-creator/ffmpeg-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'FFmpeg-MCP\nUsing ffmpeg command line to achieve an mcp server, can be very convenient, through the dialogue to achieve the local video search, tailoring, stitching, playback and other functions\n\n \n \n \n\nSupport Tools\nThe server implements the following tools: \n- find_video_path\n The parameters are directory and file name, file name can be complete, or is not suffixed, recursive search in the directory, return the full path\n- get_video_info\n The parameters are video path, return the video info, linkes duration/fps/codec/width/height.\n- clip_video\n The parameter is the file path, start time, end time or duration, and returns the trimmed file path\n- concat_videos\n The parameters are the list of files, the output path, and if the video elements in the list of files, such as width, height, frame rate, etc., are consistent, quick mode synthesis is automatically used\n- play_video\n Play video/audio with ffplay, support many format, like mov/mp4/avi/mkv/3gp, video_path: video path speed: play rate loop: play count\n- overlay_video\n Two video overlay. \n background_video: backgroud video path \n overlay_video: front video path \n output_path: output video path \n position: relative location \n dx: x offset \n dy: y offset \n- scale_video\n Video scale. \n video_path: in video path \n width: out video width, -2 keep aspect \n height: out video height, -2 keep aspect \n output_path: output video path \n- extract_frames_from_video\n Extract images from a video. \n Parameters: \n video_path (str): The path to the video. \n fps (int): Extract one frame every specified number of seconds. If set to 0, extract all frames; if set to 1, extract one frame per second. \n output_folder (str): The directory where the images will be saved. \n format (int): The format of the extracted images; 0: PNG, 1: JPG, 2: WEBP. \n total_frames (int): The maximum number of frames to extract. If set to 0, there is no limit \n \nMore features are coming\n\nInstallation procedure\n1. Download project\n \n\n2. Configuration in Cline\n \nNote: the value:/Users/XXX/Downloads/ffmpeg in args need to replace the actual download ffmpeg-mcp directory\n\nSupported platforms\nCurrently, only macos platforms are supported, including ARM64 or x86_64', 'readme_raw_len': 2201, 'one_line_zh': '通过对话完成本地视频的检索、裁剪、拼接与播放，底层使用 ffmpeg 命令行。', 'one_line_plain': 'Local video search, trimming, stitching and playback through conversation lets you edit and play videos by typing commands'}, {'id': 3448, 'key': 'mikechao-brave-search-mcp', 'kind': 'mcp', 'name': 'mikechao/brave-search-mcp', 'one_line': "📇 ☁️ - Web, image, news, video and local point-of-interest search using Brave's Search API", 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'An MCP Server implementation that integrates the Brave Search API, providing, Web Search, Local Points of Interest Search, Image Search, Video Search, News Search and LLM Context Search capabilities', 'url': 'https://github.com/mikechao/brave-search-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/mikechao/brave-search-mcp', 'downloads': None, 'stars': 130, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/mikechao.png?size=64', 'official_desc': "📇 ☁️ - Web, image, news, video and local point-of-interest search using Brave's Search API.", 'stars_own': 130, 'stars_creator': None, 'stars_own_repo': 'mikechao/brave-search-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Brave Search MCP Server\n\nAn MCP Server implementation that integrates the Brave Search API, providing, Web Search, Local Points of Interest Search, Video Search, Image Search, News Search and LLM Context Search capabilities\n\n \n \n \n\nFeatures\n\n- **Web Search**: Perform a regular search on the web\n- **Image Search**: Search the web for images.\n- **News Search**: Search the web for news\n- **Video Search**: Search the web for videos\n- **Local Points of Interest Search**: Search for local physical locations, businesses, restaurants, services, etc\n- **LLM Context Search**: Fetch and extract full web page content optimized for reading and synthesizing sources\n\nTools\n\n- **brave_web_search**\n - Execute web searches using Brave's API\n - Inputs:\n - query (string): The term to search the internet for\n - count (number, optional): The number of results to return (max 20, default 10)\n - offset (number, optional, default 0): The offset for pagination\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_image_search**\n - Get images from the web relevant to the query\n - Inputs:\n - query (string): The term to search the internet for images of\n - count (number, optional): The number of images to return (max 50, default 10)\n\n- **brave_news_search**\n - Searches the web for news\n - Inputs:\n - query (string): The term to search the internet for news articles, trending topics, or recent events\n - count (number, optional): The number of results to return (max 20, default 10)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_local_search**\n - Search for local businesses, services and points of interest\n - Falls back to brave_web_search on the initial page if no location results are found\n - Inputs:\n - query (string): Local search term\n - count (number, optional): The number of results to return (max 20, default 5)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n\n- **brave_video_search**\n - Search the web for videos\n - Inputs:\n - query: (string): The term to search for videos\n - count: (number, optional): The number of videos to return (max 20, default 10)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_llm_context_search**\n - Pre-extracted web content optimized for AI agents, LLM grounding, and RAG pipelines.\n - Uses Brave's balanced context threshold mode in compact mode and disables Brave relevance filtering in full mode.\n - Inputs:\n - query (string): The search query. Maximum 400 characters and 50 words.\n - url (string, optional): Optional URL to target. When provided, query and URL are combined for retrieval and only snippets from this exact URL are returned.\n - count (number, optional, default 8): The maximum number of search results considered. Minimum 1, maximum 50.\n - maximumNumberOfUrls (number, optional, default 8): The maximum number of URLs to include in the response. Minimum 1, maximum 50.\n - maximumNumberOfTokens (number, optional, default 2048): The approximate maximum number of tokens in the returned context. Minimum 1024, maximum 32768.\n - maximumNumberOfSnippets (number, optional, default 16): The maximum number of snippets across all URLs. Minimum 1, maximum 100.\n - maximumNumberOfTokensPerUrl (number, optional, default 512): The maximum number of tokens per URL. Minimum 512, maximum 8192.\n - maximumNumberOfSnippetsPerUrl (number, optional, default 2): The maximum number of snippets per URL. Minimum 1, maximum 100.\n - responseMode (enum, optional, default compact): compact applies Brave's balanced relevance filtering plus local snippet filtering/truncation. full disables Brave's relevance filtering and returns raw snippets without local filtering or truncation.\n - maxSnippetChars (number, optional, default 400): Maximum characters per snippet in compact mode. Minimum 80, maximum 4000.\n - maxOutputChars (number, optional, default 8000): Approximate maximum serialized response size in compact mode. Minimum 1000, maximum 100000.\n\nOpenAI Apps & MCP Apps Support\n\n \n \n \n \n \n ▶️ Click to watch the demo video \n \n\nThere is now support for OpenAI Apps and MCP Apps in this MCP Server. When UI mode is enabled for each tool there is a corresponding UI widget that let's you control what gets added to the model's context. See the directions in usage with ChatGPT section.\n\nConfiguration\n\nGetting an API Key\n\n1. Sign up for a Brave Search API account\n2. Choose a plan\n3. Generate your API key from the developer dashboard\n\nRuntime modes\n\nBy default the MCP server runs in stdio mode.\n\n \n\nTo enable Streamable HTTP mode:\n\n \n\nBy default the server listens on port 3001.\nThe URL is:\n\n \n\nThere are two configuration modes:\n\n- Env mode: BRAVE_MCP_CONFIG is unset. Feature toggles come from environment variables exactly as in previous releases.\n- File mode: BRAVE_MCP_CONFIG=/path/to/config.toml is set. The TOML file becomes the single source of truth for feature settings, and overlapping feature env vars are ignored with warnings.\n\nEnvironment-only settings\n\nThese settings are always read from the process environment, regardless of mode:\n\n- BRAVE_API_KEY (required): Brave Search API key.\n- PORT (optional): HTTP port (default: 3001).\n- HOST (optional): Interface to bind to (default: 0.0.0.0).\n- BRAVE_MCP_CONFIG (optional): Absolute or relative path to a TOML config file for feature settings.\n\nEnv mode feature settings\n\nWhen BRAVE_MCP_CONFIG is not set, these feature env vars are supported:\n\n- ALLOWED_HOSTS (HTTP mode only): Comma-separated list of allowed hostnames for Host header validation.\n - Example: ALLOWED_HOSTS=localhost,127.0.0.1,my-app.ngrok-free.app\n - Use hostnames only (no scheme/path), e.g. my-app.ngrok-free.app not https://my-app.ngrok-free.app/mcp\n- BRAVE_MCP_POLICY_FILE: JSON policy file path.\n- BRAVE_MCP_POLICY_REDACT: true to redact matched text instead of blocking it.\n- BRAVE_MCP_REQUEST_LIMIT: Positive integer request cap.\n- BRAVE_MCP_WINDOW_SECONDS: Non-negative integer rolling window size.\n- BRAVE_MCP_COOLDOWN_SECONDS: Non-negative integer cooldown after the limit is exceeded.\n- BRAVE_MCP_AUDIT_LOG: true to emit audit logs.\n- BRAVE_MCP_AUDIT_LOG_RAW: true to include raw query text in audit logs.\n- BRAVE_MCP_REQUIRE_JUSTIFICATION: true to reject tool calls without a justification string.\n\nExamples:\n\n \n\n \n\nFile mode (BRAVE_MCP_CONFIG)\n\nWhen BRAVE_MCP_CONFIG is set, the file controls feature configuration, including the HTTP host allowlist.\n\n \n\nNotes:\n\n- BRAVE_API_KEY, PORT, and HOST remain environment-only even in file mode.\n- If you set overlapping feature env vars such as BRAVE_MCP_REQUEST_LIMIT or ALLOWED_HOSTS alongside BRAVE_MCP_CONFIG, startup warns that they are being ignored.\n- Unknown TOML keys also emit warnings so typos like [guardrails] are visible before you debug runtime behavior.\n\nTo validate a config file without starting the server, use the packaged entrypoint after building t", 'readme_raw_len': 10161, 'one_line_zh': '通过 Brave 搜索 API 进行网页、图片、新闻、视频及本地兴趣点搜索', 'one_line_plain': 'Brave Search API lets users search the web, images, news, videos, and local points of interest'}, {'id': 3147, 'key': 'guimatheus92-mcp-video-analyzer', 'kind': 'mcp', 'name': 'guimatheus92/mcp-video-analyzer', 'one_line': '', 'one_line_ok': 0, 'why_bad': 'ok', 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server that turns any video — YouTube, Instagram, TikTok, Loom, X, Vimeo, direct URLs, local files — into transcripts, key frames, OCR text, and metadata for AI agents.', 'url': 'https://github.com/guimatheus92/mcp-video-analyzer', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/guimatheus92/mcp-video-analyzer', 'downloads': None, 'stars': 95, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/guimatheus92.png?size=64', 'official_desc': '[](https://glama.ai/mcp/servers/guimatheus92/mcp-video-analyzer) 📇 🏠 🍎 🪟 🐧 - Video analys', 'stars_own': 95, 'stars_creator': None, 'stars_own_repo': 'guimatheus92/mcp-video-analyzer', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'mcp-video-analyzer \n\n Turn any video — YouTube, Instagram, TikTok, Loom, X, Vimeo, direct links, local files — into transcripts, key frames, OCR text, and metadata for AI agents. \n\n \n \n \n \n \n \n\n \n \n \n\nNo existing video MCP combines **transcripts + visual frames + metadata** in one tool. This one does — across Loom, the major yt-dlp platforms (YouTube/Vimeo/TikTok/Instagram/X/Twitch/Dailymotion/Facebook), direct video URLs, and local files.\n\n> **Want a full pipeline, not just a tool?** social-knowledge-base is built on top of this server — it downloads whole Instagram creator accounts (reels, stories, highlights), transcribes them, and turns the result into a searchable, RAG-queryable knowledge base with AI-generated notes. Use this MCP when you want per-video analysis inside an agent; use social-knowledge-base when you want to archive and query an entire account.\n\nInstallation\n\nPrerequisites\n\n- **Node.js 22.12+** — required to run the server via npx\n- **yt-dlp** — **required** for YouTube/Vimeo/TikTok/Instagram/X/Twitch/Dailymotion/Facebook URLs; optional for everything else (improves Loom download quality). Install with pip install yt-dlp\n- **Chrome/Chromium** (optional) — fallback for frame extraction if yt-dlp is unavailable\n\n> Without yt-dlp or Chrome, direct URLs and local files still get frames — the bundled ffmpeg-static does the extraction, and Loom falls back to its own CDN download. Platform URLs (YouTube etc.) degrade to a clear "install yt-dlp" warning. Transcripts, metadata, and comments never require either.\n\nThere are three ways in: the **/video plugin** (Claude Code — slash command + MCP server auto-configured), a plain **MCP server** config (any MCP client), or the **portable skill + CLI** (Codex, Cursor, Copilot, and any agent with a shell — no MCP required).\n\nClaude Code — /video plugin (recommended)\n\n \n\nThis adds the /video slash command **and** auto-registers the MCP server — no claude mcp add needed:\n\n \n\nOther agents — Codex, Cursor, Copilot, Gemini CLI, …\n\n \n\nInstalls the video skill (Agent Skills format) into every agent detected on your machine. Agents without the MCP server configured fall back to the bundled CLI automatically — zero configuration.\n\nClaude Code (MCP only)\n\n \n\nThen restart Claude Code or start a new conversation.\n\nVS Code / Cursor\n\nAdd to your MCP settings file:\n\n- **VS Code**: File → Preferences → Settings → search "MCP" or edit ~/.vscode/mcp.json / %APPDATA%\\Code\\User\\mcp.json (Windows)\n- **Cursor**: Settings → MCP Servers → Add\n\n \n\nThen reload the window (Ctrl+Shift+P → "Developer: Reload Window").\n\nClaude Desktop\n\nAdd to your Claude Desktop config file:\n\n- **macOS**: ~/Library/Application Support/Claude/claude_desktop_config.json\n- **Windows**: %APPDATA%\\Claude\\claude_desktop_config.json\n\n \n\nThen restart Claude Desktop.\n\nCLI (one-shot, no MCP client)\n\nThe same engine is exposed as a one-shot command — this is what the video skill uses on agents without MCP, and it works standalone in any terminal:\n\n \n\nstdout is a single JSON document — metadata, transcript, ocrResults, timeline, warnings, frameCount, and frames as { time, filePath, mimeType } entries pointing at JPEG key frames copied to --out (default: the per-user cache dir — %LOCALAPPDATA% on Windows, ~/Library/Caches on macOS, $XDG_CACHE_HOME or ~/.cache on Linux — under mcp-video-analyzer/ /; set MCP_CACHE_DIR to an absolute path to relocate it). Unlike the temp dir this used to live in, nothing reaps that location, so frames persist until you delete them — the directories are created 0700. Progress streams on stderr, so stdout can be piped straight into a JSON parser. Partial failures land in warnings with exit code 0; only hard failures exit 1.\n\n| Flag | Description |\n|------|-------------|\n| --detail | brief (metadata + transcript, no frames), standard (default), detailed |\n| --max-frames | Max key frames, 1–60 (default adapts to duration) |\n| --max-width | Width cap for emitted frames (default 800, or MCP_FRAME_MAX_WIDTH); 0 keeps the source resolution — see Frame size |\n| --fields | Output filter — comma-separated subset: metadata,transcript,frames,comments,chapters,ocrResults,timeline,aiSummary. Filters the emitted JSON only; use --detail brief to actually skip download/frame extraction |\n| --force-refresh | Bypass the cache and re-analyze |\n| --ocr-language | Tesseract languages (default eng+por) |\n| --model / --language | Whisper overrides for the transcription fallback |\n| --out | Where frame images are copied |\n\nRun with no arguments (npx mcp-video-analyzer@latest) to start the MCP stdio server — the CLI is purely additive.\n\nVerify it works\n\nOnce installed, ask your AI assistant:\n\n \n\n(also works with an Instagram/TikTok/Loom link, a direct .mp4 URL, or a local file path). If the server is connected, it will automatically call the analyze_video tool.\n\nTools\n\nEight tools — the AI picks the cheapest one for the job and calls it automatically. Click any tool to expand its parameters and examples.\n\n| Tool | What it does |\n|------|--------------|\n| **analyze_video** | Full analysis: transcript + key frames + OCR + timeline + metadata |\n| **analyze_videos** | Batch version, one structured result per source (resumable) |\n| **get_transcript** | Transcript only (native captions or Whisper fallback) |\n| **get_metadata** | Metadata + comments + chapters, no download |\n| **get_frames** | Key frames only (scene-change or dense 1 fps) |\n| **analyze_moment** | Deep-dive on a time range (burst frames + transcript + OCR) |\n| **get_frame_at** | Single frame at a timestamp |\n| **get_frame_burst** | N frames across a narrow window (motion/animation) |\n\n \n analyze_video — full video analysis \n\n \n\nExtracts everything from a video URL in one call:\n\n \n\nReturns:\n- **Transcript** with timestamps and speakers\n- **Key frames** extracted via scene-change detection (automatically deduplicated). For static clips with no scene cuts — e.g. talking-head Reels/Stories where only an on-screen text overlay changes — it automatically falls back to uniform temporal sampling so you still get frames (and OCR) instead of an empty result.\n- **OCR text** extracted from frames (code, error messages, UI text, prices/dates/CTAs visible on screen)\n- **Annotated timeline** merging transcript + frames + OCR into a unified "what happened when" view\n- **Metadata** (title, duration, platform)\n- **Comments** from viewers\n- **Chapters** and **AI summary** (when available)\n\nThe AI will **automatically** call this tool when it sees a video URL — no need to ask.\n\nOptions:\n- detail — analysis depth: "brief" (metadata + truncated transcript, no frames), "standard" (default), "detailed" (dense sampling, more frames)\n- fields — array of specific fields to return, e.g. ["metadata", "transcript"]. Available: metadata, transcript, frames, comments, chapters, ocrResults, timeline, aiSummary\n- maxFrames (1-60) — cap on extracted frames. Default scales with video duration at standard detail (~12 for ≤30s up to 60 for >10min); fixed 60 at detailed, 0 at brief. An explicit value always wins\n- threshold (0.0-1.0, default 0.1) — scene-change sensitivity\n- forceRefresh — bypass cache and re-analyze\n- skipFrames — skip frame extraction for transcript-only analysis\n- model / language / initialPrompt — per-call Whisper overrides for the transcription fallback (override WHISPER_MODEL / WHISPER_LANGUAGE / WHISPER_PROMPT for this call only — pick a heavier model or a domain glossary for one hard clip without restarting the server)\n\n \n\n \n analyze_videos — batch analysis \n\n \n\n \n\nRuns analyze_video over a list of sources with a concurrency limit (default 2), returning one **structured result per source** — counts + warnings on success, or a per-item error on failure (one bad file never aborts the batch). Frame images are not inlined and full transcript/OCR/timeline are returned only when fields is set; otherwise you get counts. Pair with MCP_WRITE_SIDECARS=1 (below) so each video\'s result persists to disk and a re-run r', 'readme_raw_len': 29014, 'one_line_zh': '把任意视频转成文稿、关键帧、文字识别结果与元数据，供 AI Agent 使用。', 'one_line_plain': None}, {'id': 4307, 'key': 'tan-yong-sheng-ai-vision-mcp', 'kind': 'mcp', 'name': 'tan-yong-sheng/ai-vision-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Image, video and object-detection analysis with Google Gemini and Vertex AI for UI/UX evaluation,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol (MCP) server that provides vision capabilities to analyze image and video', 'url': 'https://github.com/tan-yong-sheng/ai-vision-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/tan-yong-sheng/ai-vision-mcp', 'downloads': None, 'stars': 80, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/tan-yong-sheng.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Image, video and object-detection analysis with Google Gemini and Vertex AI for UI/UX evaluation, visual regression testing and interface understanding.', 'stars_own': 80, 'stars_creator': None, 'stars_own_repo': 'tan-yong-sheng/ai-vision-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "AI Vision MCP Server\n\nA powerful Model Context Protocol (MCP) server that provides AI-powered image and video analysis using Google Gemini and Vertex AI models.\n\nFeatures\n\n- **Dual Provider Support**: Choose between Google Gemini API and Vertex AI\n- **Multimodal Analysis**: Support for both image and video content analysis\n- **Flexible File Handling**: Upload via multiple methods (URLs, local files, base64)\n- **Storage Integration**: Built-in Google Cloud Storage support\n- **Comprehensive Validation**: Zod-based data validation throughout\n- **Error Handling**: Robust error handling with retry logic and circuit breakers\n- **TypeScript**: Full TypeScript support with strict type checking\n\nQuick Start\n\nPre-requisites\n\nYou could choose either to use google provider or vertex_ai provider. For simplicity, google provider is recommended.\n\nBelow are the environment variables you need to set based on your selected provider. (Note: It’s recommended to set the timeout configuration to more than 5 minutes for your MCP client).\n\n(i) **Using Google AI Studio Provider**\n\n \n\nGet your Google AI Studio's api key here\n\n(ii) **Using Vertex AI Provider**\n\n \n\nRefer to the guideline here on how to set this up.\n\nInstallation\n\nBelow are the installation guide for this MCP on different MCP clients, such as Claude Desktop, Claude Code, Cursor, Cline, etc.\n\n \n Claude Desktop \n\nAdd to your Claude Desktop configuration:\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n\n \n\n \n Claude Code \n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n\nNote: Increase the MCP startup timeout to 1 minutes and MCP tool execution timeout to about 5 minutes by updating ~\\.claude\\settings.json as follows:\n\n \n\n \n\n \n Cursor \n\nGo to: Settings -> Cursor Settings -> MCP -> Add new global MCP server\n\nPasting the following configuration into your Cursor ~/.cursor/mcp.json file is the recommended approach. You may also install in a specific project by creating .cursor/mcp.json in your project folder. See Cursor MCP docs for more info.\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n \n\n \n Cline \n\nCline uses a JSON configuration file to manage MCP servers. To integrate the provided MCP server configuration:\n\n1. Open Cline and click on the MCP Servers icon in the top navigation bar.\n2. Select the Installed tab, then click Advanced MCP Settings.\n3. In the cline_mcp_settings.json file, add the following configuration:\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n \n\n \n\n Other MCP clients \n\nThe server uses stdio transport and follows the standard MCP protocol. It can be integrated with any MCP-compatible client by running:\n\n \n \n\nMCP Tools\n\nThe server provides four main MCP tools:\n\n1) analyze_image\n\nAnalyzes an image using AI and returns a detailed description.\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the image\n- prompt (string): Question or instruction for the AI\n- mode (string, optional): Analysis mode - one of:\n - general (default) - General image analysis\n - palette - Extract design tokens (colors, spacing, typography)\n - hierarchy - Analyze visual hierarchy and eye flow\n - components - Catalog UI components and design system maturity\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Examples:**\n\n1. **General image analysis:**\n \n\n2. **Extract design tokens:**\n \n\n3. **Analyze visual hierarchy:**\n \n\n4. **Component inventory:**\n \n\n2) compare_images\n\nCompares multiple images using AI and returns a detailed comparison analysis.\n\n**Parameters:**\n- imageSources (array): Array of image sources (URLs, base64 data, or file paths) - minimum 2, maximum 4 images\n- prompt (string): Question or instruction for comparing the images\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Examples:**\n\n1. **Compare images from URLs:**\n \n\n2. **Compare mixed sources:**\n \n\n3) detect_objects_in_image\n\nDetects objects in an image using AI vision models and generates annotated images with bounding boxes. Returns detected objects with coordinates and either saves the annotated image to a file or temporary directory.\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the image\n- prompt (string): Custom detection prompt describing what to detect or recognize in the image\n- outputFilePath (string, optional): Explicit output path for the annotated image\n\n**Configuration:**\nThis function uses optimized default parameters for object detection and does not accept runtime options parameter. To customize the AI parameters (temperature, topP, topK, maxTokens), use environment variables:\n\n \n\n**File Handling Logic:**\n1. **Explicit outputFilePath provided** → Saves to the exact path specified\n2. **If not explicit outputFilePath** → Automatically saves to temporary directory\n\n**Response Types:**\n- Returns file object when explicit outputFilePath is provided\n- Returns tempFile object when explicit outputFilePath is not provided so the image file output is auto-saved to temporary folder\n- Always includes detections array with detected objects and coordinates\n- Includes summary with percentage-based coordinates for browser automation\n\n**Examples:**\n\n1. **Basic object detection:**\n \n\n2. **Save annotated image to specific path:**\n \n\n3. **Custom detection prompt:**\n \n\n4) audit_design\n\nAudits UI/UX design compliance with pixel-level analysis and AI critique.\n\nThis tool provides automated design compliance auditing using pure TypeScript/JavaScript pixel analysis combined with Gemini Vision API critique. It extracts dominant colors, detects visual complexity, validates WCAG contrast ratios, and generates actionable design recommendations.\n\n**Inspired by:** Automating UX/UI Design Analysis with Python, Machine Learning, and LLMs by Jade Graham\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the design image\n- prompt (string, optional): Custom audit context or focus areas\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Features:**\n- **Dominant Colors**: K-means clustering to extract 5 primary colors\n- **Edge Complexity**: Sobel operator for visual structure analysis\n- **WCAG Contrast**: W3C relative luminance formula validation (AA/AAA)\n- **Luminance Stats**: Mean brightness and standard deviation calculations\n- **Design Issues**: Automated detection of contrast, complexity, and brightness problems\n- **AI Critique**: Gemini-powered recommendations for design improvements\n\n**Examples:**\n\n1. **Basic design audit:**\n \n\n2. **Audit local design file:**\n \n\n5) analyze_video\n\nAnalyzes a video using AI and returns a detailed description.\n\n**Parameters:**\n- videoSource (string): YouTube URL, GCS URI, or local file path to the video\n- prompt (string): Question or instruction for the AI\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Supported video sources:**\n- YouTube URLs (e.g., https://www.youtube.com/watch?v=...)\n- Local file paths (e.g., C:\\Users\\username\\Downloads\\video.mp4)\n\n**Examples:**\n\n1. **Analyze video from YouTube URL:**\n \n\n2. **Analyze local video file:**\n \n\n**Note:** Only YouTube URLs are supported for public video URLs. Other public video URLs are not currently supported.\n\nEnvironment Configuration\n\nFor basic setup, you only need to configure the provider selection and required credentials:\n\nGoogle AI Studio Provider (Recommended)\n \n\nVertex AI Provider (Production)\n \n\n📖 **Detailed Configuration Guide**\n\nFor comprehensive environment variable documentation, including:\n- Complete configuration reference (60+ environment variables)\n- Function-specific optimization examples\n- Advanced configuration patterns\n- Troubleshooting guidance\n\n👉 **See Environment Variable Guide**\n\nConfiguration Priority Overview\n\nThe server uses a hierarchical configuration system where more specific settings override general ones:\n\n1. **LLM-assigned values** (ru", 'readme_raw_len': 10342, 'one_line_zh': '使用 Google Gemini 和 Vertex AI 进行图像、视频和物体检测分析，用于 UI/UX 评估', 'one_line_plain': 'Analyse images, video and objects with Google Gemini and Vertex AI for UI/UX evaluation'}, {'id': 371, 'key': 'raveenb-fal-mcp-server', 'kind': 'mcp', 'name': 'raveenb/fal-mcp-server', 'one_line': '🐍 ☁️ - Generate images, videos and music with Fal.ai models such as FLUX, Stable Diffusion and MusicGen', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/luminarylane/fal-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/raveenb/fal-mcp-server', 'downloads': None, 'stars': 61, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/raveenb.png?size=64', 'official_desc': 'Fal.ai MCP Server A Model Context Protocol (MCP) server that enables Claude Desktop (and other MCP clients) to generate images, videos, music, and audio using Fal.ai models.', 'stars_own': 61, 'stars_creator': None, 'stars_own_repo': 'luminarylane/fal-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': '🎨 Fal.ai MCP Server\n\n \n \n \n \n \n \n \n \n\nA Model Context Protocol (MCP) server that enables Claude Desktop (and other MCP clients) to generate images, videos, music, and audio using Fal.ai models.\n\n \n \n \n\n✨ Features\n\n🚀 Performance\n- **Native Async API** - Uses fal_client.run_async() for optimal performance\n- **Queue Support** - Long-running tasks (video/music) use queue API with progress updates\n- **Non-blocking** - All operations are truly asynchronous\n\n🌐 Transport Modes (New!)\n- **STDIO** - Traditional Model Context Protocol communication\n- **HTTP/SSE** - Web-based access via Server-Sent Events\n- **Dual Mode** - Run both transports simultaneously\n\n🎨 Media Generation (18 Tools)\n\n**Image Generation:**\n- 🖼️ **generate_image** - Create images from text prompts (Flux, SDXL, etc.)\n- 🎯 **generate_image_structured** - Fine-grained control over composition, lighting, subjects\n- 🔄 **generate_image_from_image** - Transform existing images with style transfer\n\n**Image Editing:**\n- ✂️ **remove_background** - Remove backgrounds from images (transparent PNG)\n- 🔍 **upscale_image** - Upscale images 2x or 4x while preserving quality\n- ✏️ **edit_image** - Edit images using natural language instructions\n- 🎭 **inpaint_image** - Edit specific regions using masks\n- 📐 **resize_image** - Smart resize for social media (Instagram, YouTube, TikTok, etc.)\n- 🏷️ **compose_images** - Overlay images (watermarks, logos) with precise positioning\n\n**Video Tools:**\n- 🎬 **generate_video** - Text-to-video and image-to-video generation\n- 📹 **generate_video_from_image** - Animate images into videos\n- 🔀 **generate_video_from_video** - Video restyling and motion transfer\n\n**Audio Tools:**\n- 🎵 **generate_music** - Create instrumental music or songs with vocals\n\n**Utility Tools:**\n- 🔍 **list_models** - Discover 600+ available models with smart filtering\n- 💡 **recommend_model** - AI-powered model recommendations for your task\n- 💰 **get_pricing** - Check costs before generating content\n- 📊 **get_usage** - View spending history and usage stats\n- ⬆️ **upload_file** - Upload local files for use with generation tools\n\n🔍 Dynamic Model Discovery (New!)\n- **600+ Models** - Access all models available on Fal.ai platform\n- **Auto-Discovery** - Models are fetched dynamically from the Fal.ai API\n- **Smart Caching** - TTL-based cache for optimal performance\n- **Flexible Input** - Use full model IDs or friendly aliases\n\n🚀 Quick Start\n\nPrerequisites\n\n- Python 3.10 or higher\n- Fal.ai API key (free tier available)\n- Claude Desktop (or any MCP-compatible client)\n\nInstallation\n\nOption 0: Claude Code Plugin (Simplest for Claude Code Users) 🔌\n\nIf you\'re using Claude Code, install directly via the plugin system:\n\n \n\nOr install directly without adding the marketplace:\n \n\n> **Note:** You\'ll need to set FAL_KEY in your environment before using the plugin.\n\nOption 1: uvx (Recommended - Zero Install) ⚡\n\nRun directly without installation using uv:\n\n \n\n**Claude Desktop Configuration for uvx:**\n \n\n> **Note:** Install uv first: curl -LsSf https://astral.sh/uv/install.sh | sh\n\nOption 2: Docker (Recommended for Production) 🐳\n\nOfficial Docker image available on GitHub Container Registry.\n\n**Step 1: Start the Docker container**\n\n \n\n**Step 2: Configure Claude Desktop to connect**\n\nAdd to your Claude Desktop config file:\n- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n- Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n\n \n\n> **Note:** This uses mcp-remote to connect to the HTTP/SSE endpoint. Alternatively, if you have curl available: "command": "curl", "args": ["-N", "http://localhost:8080/sse"]\n\n**Step 3: Restart Claude Desktop**\n\nThe fal-ai tools should now be available.\n\n**Docker Environment Variables:**\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| FAL_KEY | (required) | Your Fal.ai API key |\n| FAL_MCP_TRANSPORT | http | Transport mode: http, stdio, or dual |\n| FAL_MCP_HOST | 0.0.0.0 | Host to bind the server to |\n| FAL_MCP_PORT | 8080 | Port for the HTTP server |\n\n**Using Docker Compose:**\n\n \n\n**⚠️ File Upload with Docker:**\n\nThe upload_file tool requires volume mounts to access host files:\n\n \n\nThen use container paths like /downloads/image.png instead of host paths.\n\n| Feature | stdio (uvx) | Docker (HTTP/SSE) |\n|---------|:-----------:|:-----------------:|\n| upload_file | ✅ Full filesystem | ⚠️ Needs volume mounts |\n| Security | Runs as user | Sandboxed container |\n\nOption 3: Install from PyPI\n\n \n\nOr with uv:\n \n\nOption 4: Install from source\n\n \n\nConfiguration\n\n1. Get your Fal.ai API key from fal.ai\n\n2. Configure Claude Desktop by adding to:\n - macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n - Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n\nFor PyPI/pip Installation:\n \n\n> **Note:** For Docker configuration, see Option 2: Docker above.\n\nFor Source Installation:\n \n\n3. Restart Claude Desktop\n\n💬 Usage\n\nWith Claude Desktop\n\nOnce configured, ask Claude to:\n\n- "Generate an image of a sunset"\n- "Create a video from this image"\n- "Generate 30 seconds of ambient music"\n- "Convert this text to speech"\n- "Transcribe this audio file"\n\nDiscovering Available Models\n\nUse the list_models tool to discover available models:\n\n- "What image models are available?"\n- "List video generation models"\n- "Search for flux models"\n\nUsing Any Fal.ai Model\n\nYou can use any model from the Fal.ai platform:\n\n \n\nHTTP/SSE Transport (New!)\n\nRun the server with HTTP transport for web-based access:\n\n \n\nConnect from web clients via Server-Sent Events:\n- SSE endpoint: http://localhost:8080/sse (Docker) or http://localhost:8000/sse (pip)\n- Message endpoint: POST http://localhost:8080/messages/\n\nSee Docker Documentation and HTTP Transport Documentation for details.\n\n📦 Supported Models\n\nThis server supports **600+ models** from the Fal.ai platform through dynamic discovery. Use the list_models tool to explore available models, or use any model ID directly.\n\nPopular Aliases (Quick Reference)\n\nThese friendly aliases are always available for commonly used models:\n\n| Alias | Model ID | Type |\n|-------|----------|------|\n| flux_schnell | fal-ai/flux/schnell | Image |\n| flux_dev | fal-ai/flux/dev | Image |\n| flux_pro | fal-ai/flux-pro | Image |\n| sdxl | fal-ai/fast-sdxl | Image |\n| stable_diffusion | fal-ai/stable-diffusion-v3-medium | Image |\n| svd | fal-ai/stable-video-diffusion | Video |\n| animatediff | fal-ai/fast-animatediff | Video |\n| kling | fal-ai/kling-video | Video |\n| musicgen | fal-ai/musicgen-medium | Audio |\n| musicgen_large | fal-ai/musicgen-large | Audio |\n| bark | fal-ai/bark | Audio |\n| whisper | fal-ai/whisper | Audio |\n\nUsing Full Model IDs\n\nYou can also use any model directly by its full ID:\n\n \n\nUse list_models with category filters to discover more:\n- list_models(category="image") - All image generation models\n- list_models(category="video") - All video generation models\n- list_models(category="audio") - All audio models\n- list_models(search="flux") - Search for specific models\n\n📚 Documentation\n\n| Guide | Description |\n|-------|-------------|\n| Installation Guide | Detailed setup instructions for all platforms |\n| API Reference | Complete tool documentation with parameters |\n| Examples | Usage examples for image, video, and audio generation |\n| Docker Guide | Container deployment and configuration |\n| HTTP Transport | Web-based SSE transport setup |\n| Local Testing | Running CI locally with act |\n\n📖 **Full documentation site**: raveenb.github.io/fal-mcp-server\n\n🔌 Claude Code Plugin Marketplace\n\nThis project is part of the **Luminary Lane Tools** marketplace for Claude Code plugins.\n\n**Add the marketplace:**\n \n\n**Available plugins:**\n| Plugin | Description |\n|--------|-------------|\n| fal-ai | Generate images, videos, and music using 600+ Fal.ai models |\n\n*More plugins coming soon!*\n\n🔧 Troubleshooting\n\nCommon Errors\n\nFAL_KEY not set\n \n**Solution:** Set your Fal.ai API key:\n \n\nModel not found\n \n**Solution:** Use list_models to discover available models, or check the ', 'readme_raw_len': 9308, 'one_line_zh': '使用 FLUX、Stable Diffusion、MusicGen 等 Fal.ai 模型生成图像、视频和音乐', 'one_line_plain': 'Fal.ai models such as FLUX, Stable Diffusion and MusicGen let users generate images, videos and music'}, {'id': 3795, 'key': 'anwerj-youtube-uploader-mcp', 'kind': 'mcp', 'name': 'anwerj/youtube-uploader-mcp', 'one_line': '🏎️ ☁️ - Upload videos to YouTube directly from an AI assistant', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Upload, Update or Schedule videos on Youtube with the help of AI', 'url': 'https://github.com/anwerj/youtube-uploader-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/anwerj/youtube-uploader-mcp', 'downloads': None, 'stars': 59, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/anwerj.png?size=64', 'official_desc': '🏎️ ☁️ - Upload videos to YouTube directly from an AI assistant.', 'stars_own': 59, 'stars_creator': None, 'stars_own_repo': 'anwerj/youtube-uploader-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'YouTube Uploader MCP\n \n \n\nAI‑powered YouTube uploader—no CLI, no YouTube Studio, and no secrets ever shared with LLMs or third‑party apps and all free of cost! It includes OAuth2 authentication, token management, and video upload functionality.\n\nFeatures\n\n* **Direct Uploads**: Upload videos to YouTube from Claude, Cursor, VS Code, or any other MCP client.\n* **AI-Assisted Metadata**: Automatically generate titles, descriptions, and tags via your MCP client.\n* **OAuth2 Authentication**: Secure local login, multi-channel support, and auto-refreshing.\n* **Metadata & Settings**: Category tags, optional language settings, and explicit metadata control.\n* **Privacy & Scheduling**: Support for public, private, or unlisted statuses, and scheduled publish times.\n* **Post-Upload Configs (update_video)**: Add videos to playlists, upload custom thumbnails ( \n\n \n\nManual Installation\nPlease check Single Command Installation, proceed if you prefer manual installation.\n\nVisit the Releases page and download the appropriate binary for your operating system:\n\n- youtube-uploader-mcp-linux-amd64\n- youtube-uploader-mcp-darwin-arm64\n- youtube-uploader-mcp-windows-amd64.exe\n- etc.\n\n> You can use the latest versioned tag, e.g., v1.0.0.\n\n---\n\n2. Make it Executable (Linux/macOS)\n\n \n\n3. Configure MCP (e.g., in Claude Desktop or Cursor)\n \n4. Set Up Google OAuth 2.0\nTo upload to YouTube, you must configure OAuth and get a client_secret.json file from the Google Developer Console.\n\n➡️ Follow the guide in youtube_oauth2_setup.md for a step-by-step walkthrough.\n\n> [!WARNING]\n> **Important: Sensitive OAuth Scope & Re-Authentication**:\n> Subtitle/caption upload relies on the https://www.googleapis.com/auth/youtube.force-ssl scope, which is required by the YouTube Data API to perform caption write operations. \n> * **Existing Users**: If you authenticated with an older version of this MCP, your saved token will lack this scope. Any calls attempting to upload subtitles will fail with a 403 Forbidden API error. \n> * **How to Update**: You must delete the channel cache file (usually .youtube_uploader_channels_cache in your working directory) and rerun the authenticate tool to grant the new permissions.\n> * **Google Console Warning**: Adding this sensitive scope will trigger a warning screen in Google Cloud Console. Since this is for your personal use, you can safely proceed past the "unverified app" warnings.\n\nUsage & Tool Orchestration\n\nThe MCP server registers the following tools:\n1. authenticate: Generates the OAuth2 URL for authentication.\n2. accesstoken: Exchanges the code for user credentials and channel info.\n3. channels: Retrieves authenticated channels.\n4. refreshtoken: Force-refreshes tokens.\n5. upload_video: Uploads the video file and configures main details (title, description, tags, category, optional language, status, kids\' flags, scheduled publish).\n6. update_video: Decoupled tool that manages post-upload configurations: adds the video to a playlist, uploads a custom thumbnail (must be <2MB), attaches subtitle/caption tracks, schedules an existing private video to publish at an RFC3339 time, and updates the made-for-kids audience setting.\n7. list_videos: Lists videos uploaded to a channel (public, unlisted, and private), with optional title/description search, privacy filter, and date ordering.\n\nContributing\n\nSee CONTRIBUTING.md to contribute.', 'readme_raw_len': 3379, 'one_line_zh': '🏎️ ☁️ - 通过 AI 助手直接上传视频到 YouTube', 'one_line_plain': 'YouTube lets you upload videos directly from an AI assistant'}, {'id': 5248, 'key': 'utensils-mold', 'kind': 'repo', 'name': 'utensils/mold', 'one_line': 'CLI-native local AI image and video generation for people, scripts, and agents — CUDA on Linux, Metal on macOS, plus desktop, web, iPhone, REST/SSE, and MCP.', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'CLI-native local AI image and video generation for people, scripts, and agents — CUDA on Linux, Metal on macOS, plus desktop, web, iPhone, REST/SSE, and MCP.', 'url': 'https://github.com/utensils/mold', 'source': 'github', 'source_url': 'https://github.com/utensils/mold', 'downloads': None, 'stars': 54, 'installs': None, 'mentions': 1, 'score': 103.1, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-10', 'times_seen': 2, 'icon': 'https://github.com/utensils.png?size=64', 'official_desc': 'CLI-native local AI image and video generation for people, scripts, and agents — CUDA on Linux, Metal on macOS, plus desktop, web, iPhone, REST/SSE, and MCP.', 'stars_own': 54, 'stars_creator': None, 'stars_own_repo': 'utensils/mold', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "mold\n\n \n \n \n \n \n \n \n\nLocal AI image and video generation on your own GPU. Mold supports NVIDIA CUDA\nand Apple Silicon Metal, with a CLI, native desktop app, web studio, mobile\ncompanions, Discord bot, and REST/SSE API built on the same engine.\n\n**Documentation** ·\n**Models** ·\n**Desktop guide** ·\n**API**\n\n \n\nThe generation composer offers **Edit prompt** for spacious long-text editing,\nrecent-prompt search and Clear with Undo. Native iOS and macOS Library Settings\nalso let you hide date separators for a continuous grid.\n\nInstall\n\nStable release:\n\n \n\nNightly CLI from the latest published main build:\n\n \n\nAutomatic cloud provisioning uses a verified container digest for the client’s exact\nsource revision. Nightly/source builds wait for their matching container readiness\nmanifest; an unpublished image fails before creating a paid host.\n\nThe installer selects a compatible build and verifies its checksum. Linux x86_64\nclients without a visible NVIDIA GPU receive a GPU-free CLI. To use only remote\nGPU hosts, including from a machine with NVIDIA hardware:\n\n \n\nThe CPU archive needs no NVIDIA driver or CUDA libraries. Local GPU generation\nrequires a CUDA build (or Metal on macOS). See the\ninstallation guide for Nix,\nArch, Windows, Android, and source builds.\nGH200, GB200, and GB300 require future linux/arm64 artifacts and are unsupported.\n\nMold no longer publishes new versions to crates.io. Existing registry versions\nare historical; use GitHub releases, Nix, Docker, AUR, or a source build\nfor current versions.\n\nQuick start\n\n \n\nFor API-only servers, set web_ui_enabled = false in config.toml (or\nMOLD_WEB_UI_ENABLED=false) and restart. The interface defaults on; disabling\nit leaves every API route available. NixOS: services.mold.webUi.enable = false;.\n\nModels download automatically on first use. Generated media is saved locally\nwith prompt, model, seed, and generation metadata.\nLoRA reuse tracks adapter file revisions as well as strengths; replacing an\nadapter invalidates cached weights. FLUX and SD3 retain at most 256 MiB of\nderived LoRA deltas per engine and release that cache when unloaded.\nWeb, desktop and mobile reject empty video/audio attachments and files above\n64 MiB before encoding, with inline errors that preserve the prior attachment.\nMobile also enforces a 45 MiB combined media budget.\nFramewise video upscale also needs the host codec bridge: Nix packages and\nCUDA containers include it, while raw binary installs must provide ffmpeg\nand ffprobe on PATH before the server advertises that feature.\n\nWhat it supports\n\n- **Models:** FLUX.1, Flux.2, Stable Diffusion, Z-Image, Qwen-Image,\n Qwen Image 2.1, Wuerstchen, LTX Video, Wan, MiniMax H3, and Hunyuan3D. See the\n model catalog for variants and hardware\n requirements.\n Wan's 1.3B BF16 and 5B Q8/FP16 paths are performance-qualified on Apple\n Metal as well as CUDA; fp8-scaled Wan checkpoints remain CUDA-only.\n MiniMax H3's compact runtime is supported on SM89 CUDA and Apple Metal;\n Metal admission checks each request against live unified-memory headroom.\n FL2VA accepts text-only (no frame), first, last, or both boundary frames,\n with roughly 1,000 prompt tokens with paired frames and charging their\n additional memory. Distilled FL2VA tiers include Turbo 4/8-step, DMAD\n 4-step, PDMD 2/4-step, and FastVideo's FastH3 V2 8-step (CUDA-qualified).\n Queued FL2VA jobs preserve first/last endpoint roles through durable storage\n and replay, including all supported Turbo tiers.\n Forced-local H3 execution accepts one FL2VA request; batches, sequences, and\n Ref2VA reference uploads require the server route.\n Remote Ref2VA automatically selects authenticated uploads or bounded inline\n references on hosts with authentication disabled; no client\n API key is needed for the latter. Server license acceptance remains server-owned.\n Qwen Image 2.1 (non-commercial Qwen Research License) edits from up to ten\n ordered references, renders transparent PNG/WebP cut-outs, runs native 2K\n presets, and ships BF16, INT8, FP8, GGUF and 6-step turbo tiers; its CUDA\n fast path is 2.5x faster at 1024² and 6x at 2K than v0.32.\n- **Images:** text-to-image, image editing, inpainting, ControlNet, LoRA,\n identity photos, reference-image prompting, transparent backgrounds, prompt\n expansion, and upscaling, as PNG, JPEG or still WebP.\n- **Video and audio:** text/image-to-video, sequences, clip continuation,\n lip dub, text-to-audio, and MP4 output with generated audio.\n- **3D:** single-image and named multiview-to-mesh with Hunyuan3D 2.0 and 2.1,\n automatic background removal, optional highlight and lighting removal, and\n Hunyuan3D Paint PBR materials in CUDA builds. Shape transformers can be\n locally derived as qualified FP8/GGUF tiers with mold quantize, which\n registers the derived tier on the host that made it. Results are published to the Library as\n binary glTF with a rendered poster tile, exportable as OBJ, an OBJ+PBR ZIP, STL, or PLY, or\n shared as a turntable GIF, APNG, or WebP. Durable text-to-3D, supplied-mesh texturing, and mesh-rebuild workflows remain\n available through the API/CLI. Apps generate 3-D objects directly from supplied\n images in the main generation screen.\n Painted prints expose their base-color, metallic-roughness, and normal maps as\n independent, digest-checked Library downloads on web, desktop, and mobile.\n- **Multiple machines:** connect local, LAN, Tailscale, and RunPod hosts, then\n route work and browse one combined Library.\n- **Organization:** title, favorite, tag, collect, restore, and manage prints\n across the desktop and web apps.\n\nCurated models use the same short manifest title throughout the apps and\nmold list, such as **FLUX.1 Dev Q4**. The runnable ID (flux-dev:q4)\nremains available for commands, saved settings, and API requests. Model files\nkeep their original names; third-party models keep their provider titles.\n\nModel weights keep their own licenses. See each model page for terms and\ncurrent platform support.\n\nMold Studio\n\nUse **View** (native apps) or **View original** (web, desktop and mobile) beside an attached input to inspect it without replacing or removing it. References, source/target pictures, boundary frames, masks, control and identity pictures, and attached video/audio have separate viewing actions. Image previews fit the whole picture. Use **Actual size** on desktop and web, or pinch to zoom in the native iOS app, for close inspection. **Done** or Escape returns to the attachment or picker. Picker previews do not select or attach the picture. Queue viewing actions show the full submitted media, which may already have been fitted to the generation canvas; older machines without the original-input endpoint report it unavailable.\n\nEvery download, with requirements, is on the\nDownload page.\n\n| Platform | Download |\n| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| macOS | Mold Studio for Mac — built natively, Apple Silicon, macOS 26+ |\n| macOS | Mold Desktop for Mac — same app as Windows and Linux, macOS 12+ |\n| Windows | Mold Desktop for Windows — self-signed, see below |\n| Linux | Mold Desktop for Linux — GPU-free, for remote GPU machines |\n| iPhone and iPad | Mold Studio Companion (iOS 26) and Mold for iPhone (iOS 17) — TestFlight |\n| Android | Mold for Android — signed APK, Android 7.0+ |\n\n**Two desktop apps on the Mac.** Mold Studio is built natively for macOS: signed,\nnotarized, self-updating through Sparkle, with mold's own Metal engine built\nin, and it talks to any mold serve on your network too. Generate, a merged\nmulti-machine Library, Queue, Models and Machines, all in one native window.\nLibrary’s native machine and media filter menus keep their current selections visible.\nBoth native apps keep neutral queue placeholders for jobs without source images;\nMac restores fully local source attachments without stale archive warnings.\nQueue jobs open ", 'readme_raw_len': 32967, 'one_line_zh': '面向人、脚本与 Agent 的 CLI 原生本地 AI 图像与视频生成，Linux 用 CUDA、macOS 用 Metal，并提供桌面与 MCP', 'one_line_plain': 'Generate images and videos locally using CUDA on Linux, Metal on macOS, and MCP for desktop, web, and iPhone'}, {'id': 1590, 'key': 'pzalutski-pixel-javalens-mcp', 'kind': 'mcp', 'name': 'pzalutski-pixel/javalens-mcp', 'one_line': '☕ 🏠 - Semantic Java analysis via Eclipse JDT: compiler-accurate navigation, refactoring, find references,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server providing semantic Java code analysis for AI agents. Built on Eclipse JDT with tools for navigation, refactoring, search, and metrics.', 'url': 'https://github.com/pzalutski-pixel/javalens-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/pzalutski-pixel/javalens-mcp', 'downloads': None, 'stars': 44, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/pzalutski-pixel.png?size=64', 'official_desc': '☕ 🏠 - Semantic Java analysis via Eclipse JDT: compiler-accurate navigation, refactoring, find references, call hierarchy and code metrics.', 'stars_own': 44, 'stars_creator': None, 'stars_own_repo': 'pzalutski-pixel/javalens-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'JavaLens: AI-First Code Analysis for Java\n\n \n \n \n\nAn MCP server providing 75 semantic analysis tools for Java, built directly on Eclipse JDT for compiler-accurate code understanding.\n\nBuilt for AI Agents\n\nJavaLens exists because **AI systems need compiler-accurate insights that reading source files cannot provide**. When an AI uses grep or Read to find usages of a method, it cannot distinguish:\n\n- A method call from a method with the same name in an unrelated class\n- A field read from a field write\n- An interface implementation from an unrelated class\n- A cast to a type from other references to that type\n\nThis leads to incorrect refactorings, missed usages, and incomplete understanding of code behavior.\n\nCompiler-Accurate Analysis\n\nJavaLens provides **compiler-accurate code analysis** through Eclipse JDT—the same engine that powers Eclipse IDE. Unlike text search, JDT understands:\n\n- Type resolution across inheritance hierarchies\n- Method overloading and overriding\n- Generic type arguments\n- Import resolution and classpath dependencies\n- Java source from version 1.1 through **Java 25** (markdown Javadoc, module imports, compact source files, flexible constructor bodies)\n- **Lombok**-generated members — a bundled agent makes @Data accessors and the like resolve, so code using them is not flagged as undefined\n\n**Example:** Finding all places where UserService.save() is called:\n\n| Approach | Result |\n|----------|--------|\n| grep "save(" | Returns 47 matches including orderService.save(), saveButton, comments |\n| find_references | Returns exactly 12 calls to UserService.save() |\n\nAI Training Bias Warning\n\n> ⚠️ **Important for AI developers and users**\n\nAI models may exhibit **trained bias toward native tools** (Grep, Read, LSP) over MCP server tools, even when semantic analysis provides better results. This happens because:\n\n1. Training data contains extensive grep/text-search patterns\n2. Native tools are "always available" in the model\'s experience\n3. The model may not recognize when semantic analysis is superior\n\n**To get the best results:**\n\nAdd guidance to your project instructions or system prompt (e.g., CLAUDE.md for Claude Code):\n\n \n\nWhat is JavaLens?\n\nJavaLens is an MCP server that gives AI assistants deep understanding of Java codebases. It provides semantic analysis, navigation, refactoring, and code intelligence tools that go beyond simple text search.\n\nWhy Not LSP?\n\nLanguage Server Protocol was designed for IDE autocomplete and basic navigation—not for AI agent workflows that require deep semantic analysis.\n\n| Capability | Native LSP | JavaLens |\n|------------|------------|----------|\n| Find all @Annotation usages | ❌ | ✅ |\n| Find all new Type() instantiations | ❌ | ✅ |\n| Find all casts to a type | ❌ | ✅ |\n| Distinguish field reads from writes | ❌ | ✅ |\n| Detect circular package dependencies | ❌ | ✅ |\n| Calculate cyclomatic complexity | ❌ | ✅ |\n| Find unused private methods | ❌ | ✅ |\n| Detect possible null pointer bugs | ❌ | ✅ |\n| Project-wide dead-code reachability from entry points | ❌ | ✅ |\n| Find the tests that exercise a symbol, transitively | ❌ | ✅ |\n\nJavaLens wraps **Eclipse JDT Core** directly via OSGi, providing:\n\n- **Fine-grained reference types**: Find specifically casts, annotations, throws clauses, catch blocks, instanceof checks, method references, type arguments\n- **Read vs write access distinction**: Track where fields are mutated vs just read\n- **Indexed search**: JDT pre-builds an index at load time, so symbol/reference queries do not walk source files\n- **Full AST access**: Direct manipulation for complex refactorings\n\nInstallation\n\nPrerequisites\n\n- **Java 21** or later (must be on PATH or set JAVA_HOME) — required for both install paths.\n- **Node.js 18+** — required *only* if you use the npm/npx install path below. Skip if you use the direct-download path.\n\nJavaLens is an analytical server, not a compiler. It uses Eclipse JDT 2025-12 to parse and understand Java source code from **version 1.1 through 25**. Java 21 is required only as the server runtime.\n\nInstall from GitHub Releases (recommended — Java only)\n\nThis is the simplest path if you already have Java 21 and don\'t have Node.js. Download from Releases:\n\n| Platform | File |\n|----------|------|\n| All platforms | javalens.zip or javalens.tar.gz |\n\nExtract to a location of your choice (e.g., /opt/javalens or C:\\javalens). Then point your MCP client at the bundled jar — see Configure MCP Client below.\n\nInstall via npm (requires Node.js 18+)\n\nIf you already have Node.js, npx will download and cache the JavaLens distribution (~23 MB) on first run:\n\n \n\nConfigure MCP Client\n\nAdd to your MCP configuration (e.g., .mcp.json for Claude Code):\n\n \n\nThe -data argument specifies where JavaLens stores its workspace metadata. See How Workspaces Work below.\n\nAuto-Load a Project\n\nSet JAVA_PROJECT_PATH to auto-load a project when the server starts:\n\n \n\n> **Note:** Project loading happens asynchronously in the background. The MCP server responds immediately while the project loads. Use health_check to monitor loading status—it will show "project.status": "loading" until complete, then "loaded" when ready.\n\nHow Workspaces Work\n\nUnlike in-memory code models, Eclipse JDT requires a **workspace directory** to store:\n\n- Search indexes for fast symbol lookup\n- Compilation state and caches\n- Project metadata\n\nWorkspaces Are Outside Your Source\n\nJavaLens creates its workspace **outside your source project** to keep your codebase clean:\n\n \n1. load_project(projectPath="/path/to/java/project")\n2. search_symbols(query="*Service", kind="Class")\n3. find_references(filePath="...", line=10, column=15)\n4. analyze_type(typeName="com.example.UserService")\n \n1. Use JavaLens tools to analyze\n2. Write changes to files\n3. Use JavaLens tools to verify — answers already reflect the changes\n bash\ngit clone https://github.com/pzalutski-pixel/javalens-mcp.git\ncd javalens-mcp\n./mvnw clean verify\n bash\nFull suite, gentle (missing tools skip)\n./mvnw verify\n\nFull suite, strict (missing tools fail; what CI does)\nJAVALENS_TESTS_REQUIRE_TOOLS=true ./mvnw verify\n mermaid\nflowchart TD\n Client[" MCP Client "]\n MCP[" org.javalens.mcp McpProtocolHandler → ToolRegistry → 75 Tools"]\n Core[" org.javalens.core JdtServiceImpl → WorkspaceManager, SearchService"]\n JDT[" Eclipse JDT Core (via OSGi / Equinox) IWorkspace, IJavaProject, SearchEngine, ASTParser"]\n\n Client -->|"JSON-RPC over stdio"| MCP\n MCP --> Core\n Core --> JDT\n`\n\nLicense\n\nMIT License - see LICENSE for details.', 'readme_raw_len': 6513, 'one_line_zh': '通过 Eclipse JDT 进行 Java 语义分析：编译器级精确导航、重构、查找引用等', 'one_line_plain': 'Navigate and refactor Java code with compiler-accurate accuracy via Eclipse JDT'}, {'id': 7542, 'key': 'houtini-ai-gemini-mcp', 'kind': 'repo', 'name': 'houtini-ai/gemini-mcp', 'one_line': "Google Gemini MCP - Use Google's visual, video and chat capabilities in your AI Assistant", 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '["开源项目"]', 'desc_raw': "Google Gemini MCP - Use Google's visual, video and chat capabilities in your AI Assistant. Uses MCP Apps to preview created images, landing pages, SVGs and video", 'url': 'https://github.com/houtini-ai/gemini-mcp', 'source': 'github', 'source_url': 'https://github.com/houtini-ai/gemini-mcp', 'downloads': None, 'stars': 34, 'installs': None, 'mentions': 1, 'score': 103.07, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-14', 'last_seen': '2026-10-19', 'times_seen': 6, 'icon': 'https://github.com/houtini-ai.png?size=64', 'official_desc': "Google Gemini MCP - Use Google's visual, video and chat capabilities in your AI Assistant. Uses MCP Apps to preview created images, landing pages, SVGs and video", 'stars_own': 34, 'stars_creator': None, 'stars_own_repo': 'houtini-ai/gemini-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'claude mcp add -e GEMINI_API_KEY=your-api-key-here -s user gemini -- npx -y @houtini/gemini-mcp', 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Gemini MCP - Google Gemini image generation, video and search grounding inside Claude\n\n \n \n \n \n\nI\'ve had this Gemini MCP server running in my Claude Desktop setup for the best part of a year now. It\'s one of the few I leave switched on permanently. Not because Gemini replaces Claude (it doesn\'t), but because grounded search, image generation, SVG diagrams and video are things Gemini happens to do well, and having them as tools inside Claude beats flipping between browser tabs.\n\nFourteen tools, built around the models people actually come looking for: **Nano Banana Pro** (gemini-3-pro-image) and **Nano Banana 2** for images, **Veo 3.1** for video with synchronised audio, **Gemini 4 Argon** for chat and **Gemini 3.1 Pro** for deep research, both with Google Search grounding. Everything previews inline in Claude Desktop through MCP Apps rather than landing as a file path you have to go and open.\n\nOne npx command. That\'s it.\n\nNew here? The full user guide walks through every tool, the free-tier gotcha, and what to do when something breaks.\n\n \n \n \n \n \n\n---\n\n> **Quick Navigation**\n>\n> What it makes | Get started | What it does | Image output | Configuration | Tools | Models | Requirements\n\n---\n\nWhat it makes\n\nEverything below came out of the tools in this repo, unretouched, on the afternoon I wrote this. Prompts are in the captions so you can judge for yourself.\n\n**Image generation with search grounding.** One generate_image call with use_search=true. Gemini looked up the actual Met Office forecast for the week, then drew it. The dates and temperatures are real (well, as real as a forecast gets).\n\n \n\n*Prompt: "A polished editorial infographic poster: London weather, this week. Use real forecast data from search for the next 5 days..." - gemini-3-pro-image, 16:9, 2K.*\n\n**Text that\'s actually spelled correctly.** This is the thing Nano Banana Pro does that the previous generation of image models couldn\'t. Every label here is straight out of the model.\n\n \n\n*Prompt: "A clean, magazine-quality infographic titled How an MCP tool call works, with four numbered steps..." - gemini-3-pro-image.*\n\n**Generate, then edit.** Left is generate_image. Right is edit_image on that file with one sentence of instructions: change the track to Monza in daylight, swap the rim lighting for window light, make the pedals red. Same cockpit, same camera angle.\n\n| generate_image | edit_image |\n|:---:|:---:|\n| | |\n\n**Image to video with Veo 3.1.** The night-time cockpit above, passed to generate_video as firstFrameImage. Eight seconds at 1080p with generated audio (engine note, tyre hiss) - the GIF below is silent and squashed for GitHub, the real file is a proper MP4.\n\n \n\n**SVG that you can actually use.** Not a picture of a diagram - real vector markup you can drop into a page, edit by hand or commit to a repo. Both of these are the raw .svg files the tool wrote to disk.\n\n| generate_svg style=technical | generate_svg style=data-viz |\n|:---:|:---:|\n| | |\n\n**A landing page from a paragraph.** generate_landing_page with a brief, a company name and a brand colour. Self-contained HTML, inline CSS, the little chart in the hero is animated SVG. Screenshot of the file opened in Chrome, nothing else touched.\n\n \n\n**And the fast one.** Nano Banana 2 (gemini-3.1-flash-image) for when you want volume rather than 4K. This took about ten seconds.\n\n \n\n---\n\nGet started in two minutes\n\n**Step 1: Get a Gemini API key**\n\nGo to Google AI Studio and create one.\n\nA word on the free tier, because the defaults lean on a paid model. Google\'s pricing page gives Gemini 3 Flash Preview a free tier, but the flagship models are paid-only - and gemini-4-argon is the default for gemini_chat and generate_landing_page, with gemini-3.1-pro-preview the default for gemini_deep_research (synthesis) and analyze_image. On a free key those calls will fail unless you point them at a free model: set GEMINI_DEFAULT_MODEL=gemini-3-flash-preview (covers chat and landing pages), GEMINI_DEEP_RESEARCH_MODEL and GEMINI_IMAGE_ANALYSIS_MODEL likewise, or pass model per call. generate_svg already defaults to gemini-3-flash-preview. Check the pricing page for the other models before relying on them - Google changes these tiers.\n\n**Step 2: Add to your Claude Desktop config**\n\nConfig file locations:\n- Windows: C:\\Users\\{username}\\AppData\\Roaming\\Claude\\claude_desktop_config.json\n- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n\n \n\n**Step 3: Restart Claude Desktop**\n\nThat\'s it. Tools show up automatically. npx pulls the package on first run, so there\'s no separate install.\n\nLocal build instead\n\nFor development, or if you\'d rather not rely on npx:\n\n \n\nThen point your config at the local build:\n\n \n\nClaude Code (CLI)\n\nClaude Code doesn\'t read claude_desktop_config.json. Use claude mcp add instead:\n\n \n\nWith an output directory for images and video:\n\n \n\nCheck with claude mcp get gemini - you want to see Status: Connected.\n\n---\n\nWhat it does\n\nChat with Google Search grounding\n\n \n\nGrounding is on by default. Gemini searches Google before it answers, so you get this month\'s information rather than a training-cutoff guess, and the sources come back as markdown links. For questions where you want pure reasoning ("explain this code", that sort of thing) set grounding: false.\n\nRuns on gemini-4-argon (Gemini 4 Argon) unless you say otherwise. Pass model: "gemini-3.8-flash" if you\'d rather have speed than depth, or model: "gemini-3.1-pro-preview" for the previous flagship. thinking_level works on Gemini 3.x and 4 models: high for the hard stuff, low to keep it snappy.\n\nDeep research\n\n \n\nRuns grounded search passes and then writes them up as one report. The passes run on gemini-3.8-flash with low thinking (they\'re gathering facts, not reasoning about them), and the synthesis at the end runs on gemini-3.1-pro-preview with high thinking. Two passes plus a synthesis is the default and lands in two to three minutes.\n\nThat split matters more than it sounds. Earlier versions ran everything on Pro with full thinking, and one pass on a broad question could run past Claude Desktop\'s four-minute timeout on its own. Keep max_iterations at 2 or 3 in Claude Desktop; in an IDE or an agent framework, 5 to 7 produces noticeably better synthesis. focus_areas takes an array if you want to steer each pass.\n\nImage generation with search grounding\n\n \n\nDefault model is gemini-3-pro-image, which is Nano Banana Pro now that it\'s out of preview. It renders legible text, does 4K, and it\'s the only one that supports conversational editing. gemini-3.1-flash-image (Nano Banana 2) is the fast option - not far off in quality, a fraction of the time and the cost. There\'s a Lite variant too if you\'re doing hundreds.\n\nWith use_search=true, Gemini looks things up before it draws. Weather, prices, sports results, that kind of data-driven image works reliably. The full-resolution file always goes to disk; the inline preview is resized to fit the MCP transport cap but the original is untouched.\n\nVideo generation with Veo 3.1\n\n \n\nGoogle\'s Veo 3.1. Four to eight second clips at up to 4K with native, synchronised audio. It\'s asynchronous on Google\'s side and takes two to five minutes; the tool polls until it\'s ready so you don\'t have to.\n\nOptions worth knowing about:\n- aspectRatio - 16:9 landscape or 9:16 for vertical\n- generateAudio - on by default; dialogue and effects that match the prompt\n- firstFrameImage - animate from a still (that\'s how the cockpit clip above was made)\n- referenceImages - up to three, for character or style consistency\n- sampleCount - up to four variations in one call\n- seed - deterministic output across runs\n- generateThumbnail - pulls a frame out with ffmpeg, if you\'ve got it in PATH\n- model - veo-3.1-lite-generate-preview if you want cheaper and faster\n\nSVG generation\n\nThis is the one people underestimate. The output isn\'t a picture of a diagram, it\'s the SVG markup itself - drop it into a codebase, a slide, a web page, and it scales without a', 'readme_raw_len': 17845, 'one_line_zh': 'Google Gemini MCP：在你的 AI 助手中使用 Google 的视觉、视频与聊天能力', 'one_line_plain': "Use Google Gemini's visual, video and chat capabilities in your AI Assistant"}, {'id': 3382, 'key': 'format37-youtube-mcp', 'kind': 'mcp', 'name': 'format37/youtube_mcp', 'one_line': '🐍 ☁️ - Transcribe YouTube videos to text using yt-dlp for audio and OpenAI Whisper for transcription, with…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'youtube transcriber mcp server', 'url': 'https://github.com/format37/youtube_mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/format37/youtube_mcp', 'downloads': None, 'stars': 32, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/format37.png?size=64', 'official_desc': '🐍 ☁️ - Transcribe YouTube videos to text using yt-dlp for audio and OpenAI Whisper for transcription, with chunked output for long videos.', 'stars_own': 32, 'stars_creator': None, 'stars_own_repo': 'format37/youtube_mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'youtube_mcp\nYoutube transcribation MCP server\n\nDemo Video\n\n \n\n*Click the image above to watch the demo video*\n\nRequirements:\n* OpenAI API key\n* Cookies\n\nServer installation\n* Clone the repo:\n \n* Extract your cookies. See cookies.md \nPlace cookies.txt in the ./mcp/ folder.\n* Generate MCP_KEY:\n \n* Define .env:\n \n* Provide run access\n \n* Run\n \n* Check that port is opened for incoming connections.\n\nClient configuration\n3. Add Bybit server to the Claude desktop config:\nExample:\n \nClient side\n`\n4. Restart Claude desktop\n5. Check that tws tools are listed in the tools list. Ask Claude to check ibkr account', 'readme_raw_len': 606, 'one_line_zh': '借助 yt-dlp 抓取音频、OpenAI Whisper 转写文字，将 YouTube 视频转为文本', 'one_line_plain': 'Transcribe YouTube videos to text with yt-dlp and OpenAI Whisper'}, {'id': 3166, 'key': 'aditya2755-video-edit-mcp', 'kind': 'mcp', 'name': 'video-edit-mcp', 'one_line': '🐍 🏠 🍎 🪟 - Video and audio editing: trim, merge, effects, overlays, format conversion, audio processing and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/Aditya2755/video-edit-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/Aditya2755/video-edit-mcp', 'downloads': None, 'stars': 28, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/Aditya2755.png?size=64', 'official_desc': '🐍 🏠 🍎 🪟 - Video and audio editing: trim, merge, effects, overlays, format conversion, audio processing and YouTube downloads, chaining steps without intermediate files.', 'stars_own': 28, 'stars_creator': None, 'stars_own_repo': 'Aditya2755/video-edit-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "Video Edit MCP Server 🎬\n\nA powerful **Model Context Protocol (MCP)** server designed for advanced video and audio editing operations. This server enables MCP clients—such as Claude Desktop, Cursor, and others—to perform comprehensive multimedia editing tasks through a standardized and unified interface.\n\n \n \n \n\nhttps://github.com/user-attachments/assets/134b8b82-80b1-4678-8930-ab53121b121f\n\n✨ Key Features\n\n🎥 Video Operations\n- **Basic Editing**: Trim, merge, resize, crop, rotate videos\n- **Effects**: Speed control, fade in/out, grayscale, mirror\n- **Overlays**: Add text, images, or video overlays with transparency\n- **Format Conversion**: Convert between formats with codec control\n- **Frame Operations**: Extract frames, create videos from images\n\n🎵 Audio Operations \n- **Audio Processing**: Extract, trim, loop, concatenate audio\n- **Volume Control**: Adjust levels, fade in/out effects\n- **Audio Mixing**: Mix multiple tracks together\n- **Integration**: Add audio to videos, replace soundtracks\n\n📥 Download & Utilities\n- **Video Download**: Download from YouTube and other platforms\n- **File Management**: Directory operations, file listing\n- **Path Suggestions**: Get recommended download locations\n\n🧹 Memory & Cleanup\n- **Smart Memory**: Chain operations without saving intermediate files\n- **Resource Management**: Clear memory, check stored objects\n- **Efficient Processing**: Keep objects in memory for complex workflows\n\n🔗 Operation Chaining\nSeamlessly chain multiple operations together without creating intermediate files. Process your video through multiple steps (trim → add audio → apply effects → add text) while keeping everything in memory for optimal performance.\n\n📋 Requirements\n\n- **Python 3.10 or higher**\n- **moviepy==1.0.3**\n- **yt-dlp>=2023.1.6**\n- **mcp>=1.12.2**\n- **typing-extensions>=4.0.0**\n\n⚙️ Installation & Setup\n\nFor Claude Desktop / Cursor MCP Integration\n\n**Ensure that uv is installed.** \nIf not, install it using the following PowerShell command:\n\n \n\nAdd this configuration to your MCP configuration file:\n\n \n\n**Configuration file locations:**\n- **Claude Desktop (Windows)**: %APPDATA%/Claude/claude_desktop_config.json\n- **Claude Desktop (macOS)**: ~/Library/Application Support/Claude/claude_desktop_config.json\n- **Cursor**: .cursor/mcp.json in your project root\n\nManual Installation\n\n \n\n🏗️ Project Structure\n\n \n\n🎯 Example Usage\n\n \n\n🚀 Future Enhancements & Contributions\n\nWe welcome contributions in these exciting areas:\n\n🤖 AI-Powered Features\n- **Speech-to-Text (STT)**: Automatic subtitle generation and transcription\n- **Text-to-Speech (TTS)**: AI voice synthesis for narration\n- **Audio Enhancement**: AI-based noise reduction and audio quality improvement\n- **Smart Timestamps**: Automatic scene detection and chapter generation\n- **Face Tracking**: Advanced face detection and tracking for automatic editing\n- **Object Recognition**: Track and edit based on detected objects\n- **Content Analysis**: AI-powered content categorization and tagging\n\n🤝 Contributing\n\n1. Fork the repository\n2. Create a feature branch: git checkout -b feature/amazing-feature\n3. Commit your changes: git commit -m 'Add amazing feature'\n4. Push to the branch: git push origin feature/amazing-feature\n5. Open a Pull Request\n\n📄 License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n---\n\n \n\n**Made with ❤️ for the AI and multimedia editing community**\n\n⭐ Star this project | 🤝 Contribute | 📖 Documentation", 'readme_raw_len': 3472, 'one_line_zh': '音视频编辑：裁剪、合并、特效、叠加、格式转换与音频处理等。', 'one_line_plain': 'Video and audio editing lets you trim, merge, add effects and overlays, convert formats, and process audio'}]/2026-10-20 https://anclum.com/zh/[{'id': 8056, 'key': 'skillsmp-com-creators-aldegad-sprite-gen-skill', 'kind': 'skill', 'name': 'sprite-gen', 'one_line': 'Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults,…', 'one_line_ok': 1, 'why_bad': 'ok', 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/aldegad/sprite-gen/skill', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/aldegad/sprite-gen/skill', 'downloads': None, 'stars': 2836, 'installs': None, 'mentions': 3, 'score': 303.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-17', 'last_seen': '2026-10-19', 'times_seen': 3, 'icon': 'https://github.com/aldegad.png?size=64', 'official_desc': 'Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults, automatic cleanup and optional curation. Handles sprite requests, ordinary image generation/editing, typography logos (로고, 타이포그래피, 타이틀 글자, 타이틀 로고, wordmark, logo, title lettering: a title scr', 'stars_own': None, 'stars_creator': 2918, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'aldegad/sprite-gen', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': None, 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '通过 GPT 或 Grok 生成图像与游戏精灵，带引导式供应商选择与分开保存的默认值…', 'one_line_plain': 'Generate images and game sprites using GPT or Grok, with guided provider choices and saved defaults'}, {'id': 5749, 'key': 'skillsmp-com-creators-daymade-claude-code-skills-frontend-visual-qa', 'kind': 'skill', 'name': 'frontend-visual-qa', 'one_line': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'downloads': None, 'stars': 1452, 'installs': None, 'mentions': 5, 'score': 503.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/daymade.png?size=64', 'official_desc': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard, design-system, or Electron/native app — via real-browser/native-app journeys and a Playwright sweep. Use after UI implementation to find typography, overflow, responsive, routing, data-viz, browser-output', 'stars_own': None, 'stars_creator': 1477, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'daymade/claude-code-skills', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '审计已渲染的界面，以及 Markdown 与 Obsidian 中的图片排版。', 'one_line_plain': 'The tool audits already-rendered UI and Markdown/Obsidian image layout for web apps, decks, dashboards, and more'}, {'id': 5746, 'key': 'skillsmp-com-creators-youmind-openlab-ai-image-prompts-skill-skill', 'kind': 'skill', 'name': 'ai-image-prompts-skill', 'one_line': 'Recommend curated prompts from a 10,000+ real-world image generation prompt library. Works with ANY AI image…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/youmind-openlab/ai-image-prompts-skill/skill', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/youmind-openlab/ai-image-prompts-skill/skill', 'downloads': None, 'stars': 1248, 'installs': None, 'mentions': 8, 'score': 803.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/YouMind-OpenLab.png?size=64', 'official_desc': 'Recommend curated prompts from a 10,000+ real-world image generation prompt library.\nWorks with ANY AI image model — Nano Banana Pro, Nano Banana 2, Seedream 5.0, GPT Image 1.5,\nMidjourney, DALL-E 3, Flux, Stable Diffusion, and more.\n\nUse this skill when users want to:\n- Find proven image generation', 'stars_own': None, 'stars_creator': 1309, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'youmind-openlab/ai-image-prompts-skill', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '从一万多条真实图像生成 Prompt 中推荐精选提示词，兼容任意 AI 绘图工具。', 'one_line_plain': 'Get curated prompts from a 10,000+ real-world image generation prompt library that works with any AI image tool'}, {'id': 3109, 'key': 'aidc-ai-pixelle-mcp', 'kind': 'mcp', 'name': 'AIDC-AI/Pixelle-MCP', 'one_line': '🐍 📇 🏠 - Omnimodal AIGC framework that turns ComfyUI workflows into MCP tools without code, for text, image,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/ATH-MaaS/Pixelle-MCP', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/AIDC-AI/Pixelle-MCP', 'downloads': None, 'stars': 1131, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/AIDC-AI.png?size=64', 'official_desc': 'or quick experience and testing** Temporary Run **View uvx CLI Reference →** Persistent Installation **View pip CLI Reference →** After startup, it will automatically enter the **configuration wizard** to guide you through execution engine selection (ComfyUI/RunningHub) and LLM configuration.', 'stars_own': 1131, 'stars_creator': None, 'stars_own_repo': 'ATH-MaaS/Pixelle-MCP', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "🎨 Pixelle MCP - Omnimodal Agent Framework \n\n English | 中文 \n\n ✨ An AIGC solution based on the MCP protocol, supporting both local ComfyUI and cloud ComfyUI (RunningHub) modes, seamlessly converting workflows into MCP tools with zero code. \n\n \n\nhttps://github.com/user-attachments/assets/65422cef-96f9-44fe-a82b-6a124674c417\n\n📋 Recent Updates\n\n- ✅ **2025-09-29**: Added RunningHub cloud ComfyUI support, enabling workflow execution without local GPU and ComfyUI environment\n- ✅ **2025-09-03**: Architecture refactoring from three services to unified application; added CLI tool support; published to PyPI\n- ✅ **2025-08-12**: Integrated the LiteLLM framework, adding multi-model support for Gemini, DeepSeek, Claude, Qwen, and more\n\n🚀 Features\n\n- ✅ 🔄 **Full-modal Support**: Supports TISV (Text, Image, Sound/Speech, Video) full-modal conversion and generation\n- ✅ 🚀 **Dual Execution Modes**: Local ComfyUI self-hosted environment + RunningHub cloud ComfyUI service, users can flexibly choose based on their needs\n- ✅ 🧩 **ComfyUI Ecosystem**: Built on ComfyUI, inheriting all capabilities from the open ComfyUI ecosystem\n- ✅ 🔧 **Zero-code Development**: Defines and implements the Workflow-as-MCP Tool solution, enabling zero-code development and dynamic addition of new MCP Tools\n- ✅ 🗄️ **MCP Server**: Based on the MCP protocol, supporting integration with any MCP client (including but not limited to Cursor, Claude Desktop, etc.)\n- ✅ 🌐 **Web Interface**: Developed based on the Chainlit framework, inheriting Chainlit's UI controls and supporting integration with more MCP Servers\n- ✅ 📦 **One-click Deployment**: Supports PyPI installation, CLI commands, Docker and other deployment methods, ready to use out of the box\n- ✅ ⚙️ **Simplified Configuration**: Uses environment variable configuration scheme, simple and intuitive configuration\n- ✅ 🤖 **Multi-LLM Support**: Supports multiple mainstream LLMs, including OpenAI, Ollama, Gemini, DeepSeek, Claude, Qwen, and more\n\n📁 Project Architecture\n\nPixelle MCP adopts a **unified architecture design**, integrating MCP server, web interface, and file services into one application, providing:\n\n- 🌐 **Web Interface**: Chainlit-based chat interface supporting multimodal interaction\n- 🔌 **MCP Endpoint**: For external MCP clients (such as Cursor, Claude Desktop) to connect\n- 📁 **File Service**: Handles file upload, download, and storage\n- 🛠️ **Workflow Engine**: Supports both local ComfyUI and cloud ComfyUI (RunningHub) workflows, automatically converts workflows into MCP tools\n\n \n\n \n\n🏃\u200d♂️ Quick Start\n\nChoose the deployment method that best suits your needs, from simple to complex:\n\n🎯 Method 1: One-click Experience\n\n> 💡 **Zero configuration startup, perfect for quick experience and testing**\n\n🚀 Temporary Run\n\n \n\n📚 **View uvx CLI Reference →**\n\n📦 Persistent Installation\n\n \n\n📚 **View pip CLI Reference →**\n\nAfter startup, it will automatically enter the **configuration wizard** to guide you through execution engine selection (ComfyUI/RunningHub) and LLM configuration.\n\n🛠️ Method 2: Local Development Deployment\n\n> 💡 **Supports custom workflows and secondary development**\n\n📥 1. Get Source Code\n\n \n\n🚀 2. Start Service\n\n \n\n📚 **View Complete CLI Reference →**\n\n🔧 3. Add Custom Workflows (Optional)\n\n \n\n**⚠️ Important**: Make sure to test workflows in ComfyUI first to ensure they run properly, otherwise execution will fail.\n\n🐳 Method 3: Docker Deployment\n\n> 💡 **Suitable for production environments and containerized deployment**\n\n📋 1. Prepare Configuration\n\n \n\n🚀 2. Start Container\n\n \n\n🌐 Access Services\n\nRegardless of which method you use, after startup you can access via:\n\n- **🌐 Web Interface**: http://localhost:9004 \n *Default username and password are both dev, can be modified after startup*\n- **🔌 MCP Endpoint**: http://localhost:9004/pixelle/mcp \n *For MCP clients like Cursor, Claude Desktop to connect*\n\n**💡 Port Configuration**: Default port is 9004, can be customized via environment variable PORT=your_port.\n\n⚙️ Initial Configuration\n\nOn first startup, the system will automatically detect configuration status:\n\n1. **🚀 Execution Engine Selection**: Choose between local ComfyUI or RunningHub cloud service\n2. **🤖 LLM Configuration**: Configure at least one LLM provider (OpenAI, Ollama, etc.)\n3. **📁 Workflow Directory**: System will automatically create necessary directory structure\n\n🌐 RunningHub Cloud Mode Advantages\n- ✅ **Zero Hardware Requirements**: No need for local GPU or high-performance hardware\n- ✅ **No Environment Setup**: No need to install and configure ComfyUI locally\n- ✅ **Ready to Use**: Register and get API key to start immediately\n- ✅ **Stable Performance**: Professional cloud infrastructure ensures stable execution\n- ✅ **Auto Scaling**: Automatically handles concurrent requests and resource allocation\n\n🏠 Local ComfyUI Mode Advantages\n- ✅ **Full Control**: Complete control over execution environment and model versions\n- ✅ **Privacy Protection**: All data processing happens locally, ensuring data privacy\n- ✅ **Custom Models**: Support for custom models and nodes not available in cloud\n- ✅ **No Network Dependency**: Can work offline without internet connection\n- ✅ **Cost Control**: No cloud service fees for high-frequency usage\n\n**🆘 Need Help?** Join community groups for support (see Community section below)\n\n🛠️ Add Your Own MCP Tool\n\n⚡ One workflow = One MCP Tool, supports two addition methods:\n\n📋 **Method 1: Local ComfyUI Workflow** - Export API format workflow files\n📋 **Method 2: RunningHub Workflow ID** - Use cloud workflow IDs directly\n\n \n\n🎯 1. Add the Simplest MCP Tool\n\n* 📝 Build a workflow in ComfyUI for image Gaussian blur (Get it here), then set the LoadImage node's title to $image.image! as shown below:\n \n\n* 📤 Export it as an API format file and rename it to i_blur.json. You can export it yourself or use our pre-exported version (Get it here)\n\n* 📋 Copy the exported API workflow file (must be API format), input it on the web page, and let the LLM add this Tool\n\n \n\n* ✨ After sending, the LLM will automatically convert this workflow into an MCP Tool\n\n \n\n* 🎨 Now, refresh the page and send any image to perform Gaussian blur processing via LLM\n\n \n\n🔌 2. Add a Complex MCP Tool\n\nThe steps are the same as above, only the workflow part differs (Download workflow: UI format and API format)\n\n> **Note:** When using RunningHub, you only need to input the corresponding workflow ID, no need to download and upload workflow files.\n\n \n\n🔧 ComfyUI Workflow Custom Specification\n\n🎨 Workflow Format\nThe system supports ComfyUI workflows. Just design your workflow in the canvas and export it as API format. Use special syntax in node titles to define parameters and outputs.\n\n📝 Parameter Definition Specification\n\nIn the ComfyUI canvas, double-click the node title to edit, and use the following DSL syntax to define parameters:\n\n \n\n🔍 Syntax Explanation:\n- param_name: The parameter name for the generated MCP tool function\n- ~: Optional, indicates URL parameter upload processing, returns relative path\n- field_name: The corresponding input field in the node\n- !: Indicates this parameter is required\n- description: Description of the parameter\n\n💡 Example:\n\n**Required parameter example:**\n\n- Set LoadImage node title to: $image.image!:Input image URL\n- Meaning: Creates a required parameter named image, mapped to the node's image field\n\n**URL upload processing example:**\n\n- Set any node title to: $image.~image!:Input image URL\n- Meaning: Creates a required parameter named image, system will automatically download URL and upload to ComfyUI, returns relative path\n\n> 📝 Note: LoadImage, VHS_LoadAudioUpload, VHS_LoadVideo and other nodes have built-in functionality, no need to add ~ marker\n\n🎯 Type Inference Rules\n\nThe system automatically infers parameter types based on the current value of the node field:\n- 🔢 int: Integer values (e.g. 512, 1024)\n- 📊 float: Floating-point values (e.g. 1.5, 3.14)\n- ✅ bool: Boolean values (e.g. true, false)\n- 📝 str: String values (default typ", 'readme_raw_len': 11185, 'one_line_zh': '全模态 AIGC 框架，无需写代码即可把 ComfyUI 工作流转成 MCP 工具，覆盖文本与图像等。', 'one_line_plain': 'Omnimodal AIGC framework converts ComfyUI workflows into MCP tools for text, image, and other media'}, {'id': 1387, 'key': 'ckreiling-mcp-server-docker', 'kind': 'mcp', 'name': 'ckreiling/mcp-server-docker', 'one_line': '🐍 🏠 - Manage Docker containers, images, volumes and networks', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for Docker', 'url': 'https://github.com/ckreiling/mcp-server-docker', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ckreiling/mcp-server-docker', 'downloads': None, 'stars': 748, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ckreiling.png?size=64', 'official_desc': '🐍 🏠 - Manage Docker containers, images, volumes and networks.', 'stars_own': 748, 'stars_creator': None, 'stars_own_repo': 'ckreiling/mcp-server-docker', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '🐋 Docker MCP server\n\nAn MCP server for managing Docker with natural language!\n\n🪩 What can it do?\n\n- 🚀 Compose containers with natural language\n- 🔍 Introspect & debug running containers\n- 📀 Manage persistent data with Docker volumes\n\n❓ Who is this for?\n\n- Server administrators: connect to remote Docker engines for e.g. managing a\n public-facing website.\n- Tinkerers: run containers locally and experiment with open-source apps\n supporting Docker.\n- AI enthusiasts: push the limits of that an LLM is capable of!\n\nDemo\n\nA quick demo showing a WordPress deployment using natural language:\n\nhttps://github.com/user-attachments/assets/65e35e67-bce0-4449-af7e-9f4dd773b4b3\n\n🏎️ Quickstart\n\nInstall\n\nClaude Desktop\n\nOn MacOS: ~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n Install from PyPi with uv \n\nIf you don\'t have uv installed, follow the installation instructions for your\nsystem:\nlink\n\nThen add the following to your MCP servers file:\n\n \n\n \n\n \n Install with Docker \n\nPurely for convenience, the server can run in a Docker container.\n\nAfter cloning this repository, build the Docker image:\n\n \n\nAnd then add the following to your MCP servers file:\n\n \n\nNote that we mount the Docker socket as a volume; this ensures the MCP server\ncan connect to and control the local Docker daemon.\n\n \n\n📝 Prompts\n\n🎻 docker_compose\n\nUse natural language to compose containers. See above for a demo.\n\nProvide a Project Name, and a description of desired containers, and let the LLM\ndo the rest.\n\nThis prompt instructs the LLM to enter a plan+apply loop. Your interaction\nwith the LLM will involve the following steps:\n\n1. You give the LLM instructions for which containers to bring up\n2. The LLM calculates a concise natural language plan and presents it to you\n3. You either:\n - Apply the plan\n - Provide the LLM feedback, and the LLM recalculates the plan\n\nExamples\n\n- name: nginx, containers: "deploy an nginx container exposing it on port\n 9000"\n- name: wordpress, containers: "deploy a WordPress container and a supporting\n MySQL container, exposing Wordpress on port 9000"\n\nResuming a Project\n\nWhen starting a new chat with this prompt, the LLM will receive the status of\nany containers, volumes, and networks created with the given project name.\n\nThis is mainly useful for cleaning up, in-case you lose a chat that was\nresponsible for many containers.\n\n📔 Resources\n\nThe server exposes resource templates rather than enumerating currently-running containers:\n\n- docker://containers/{container_id}/logs (text/plain)\n- docker://containers/{container_id}/stats (application/json)\n\nRead either URI with a Docker container ID or name.\n\n🔨 Tools\n\nContainers\n\n- list_containers\n- create_container\n- run_container\n- recreate_container\n- start_container\n- fetch_container_logs\n- stop_container\n- remove_container\n\nImages\n\n- list_images\n- pull_image\n- push_image\n- build_image\n- remove_image\n\nNetworks\n\n- list_networks\n- create_network\n- remove_network\n\nVolumes\n\n- list_volumes\n- create_volume\n- remove_volume\n\n🚧 Disclaimers\n\nSensitive Data\n\n**DO NOT CONFIGURE CONTAINERS WITH SENSITIVE DATA.** This includes API keys,\ndatabase passwords, etc.\n\nAny sensitive data exchanged with the LLM is inherently compromised, unless the\nLLM is running on your local machine.\n\nIf you are interested in securely passing secrets to containers, file an issue\non this repository with your use-case.\n\nReviewing Created Containers\n\nBe careful to review the containers that the LLM creates. Docker is not a secure\nsandbox, and therefore the MCP server can potentially impact the host machine\nthrough Docker.\n\nFor safety reasons, this MCP server doesn\'t support sensitive Docker options\nlike --privileged or --cap-add/--cap-drop. If these features are of interest\nto you, file an issue on this repository with your use-case.\n\n🛠️ Configuration\n\nThis server uses the Python Docker SDK\'s from_env method. For configuration\ndetails, see\nthe documentation.\n\nConnect to Docker over SSH\n\nThis MCP server can connect to a remote Docker daemon over SSH.\n\nSimply set a ssh:// host URL in the MCP server definition:\n\n \n\n💻 Development\n\nPrefer using Devbox to configure your development environment. The server uses\nMCP Python SDK v2\'s high-level MCPServer API and can be inspected directly:\n\n \n\nRun the hermetic test and lint suite without a Docker daemon:\n\n \n\nSee the devbox.json for helpful development commands.\n\nAfter setting up devbox you can configure your Claude MCP config to use it:', 'readme_raw_len': 4511, 'one_line_zh': '管理 Docker 容器、镜像、数据卷和网络', 'one_line_plain': 'Manage Docker containers, images, volumes and networks'}, {'id': 4206, 'key': 'evalstate-mcp-hfspace', 'kind': 'mcp', 'name': 'evalstate/mcp-hfspace', 'one_line': '📇 ☁️ - Use Hugging Face Spaces for open-source image generation, chat, vision and more, with image, audio and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP Server to Use HuggingFace spaces, easy configuration and Claude Desktop mode.', 'url': 'https://github.com/evalstate/mcp-hfspace', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/evalstate/mcp-hfspace', 'downloads': None, 'stars': 389, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/evalstate.png?size=64', 'official_desc': '📇 ☁️ - Use Hugging Face Spaces for open-source image generation, chat, vision and more, with image, audio and text uploads and downloads.', 'stars_own': 389, 'stars_creator': None, 'stars_own_repo': 'evalstate/mcp-hfspace', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'mcp-hfspace MCP Server 🤗\n\n> [!TIP]\n>\n> You can access and configure Hugging Face MCP services directly at https://hf.co/mcp, including Gradio spaces.\n>\n> This project has been superceded by the official Hugging Face MCP Server and Gradio MCP Endpoints.\n> \n> Alternatively you can run hf-mcp-server locally as a STDIO Server, or with robust support for SSE, Streaming HTTP and Streaming HTTP JSON Mode. This also runs a local UI for selecting tools and endpoints and supports ToolListChangedNotifications too.\n\nhf.co/mcp\n\n \n\nmcp-hfspace\n\nRead the introduction here llmindset.co.uk/resources/mcp-hfspace/\n\nConnect to Hugging Face Spaces with minimal setup needed - simply add your spaces and go!\n\nBy default, it connects to black-forest-labs/FLUX.1-schnell providing Image Generation capabilities to Claude Desktop.\n\n \n\nGradio MCP Support\n\n> [!TIP]\n> Gradio 5.28 now has integrated MCP Support via SSE: https://huggingface.co/blog/gradio-mcp. Check out whether your target Space is MCP Enabled!\n\nInstallation\n\nNPM Package is @llmindset/mcp-hfspace.\n\nInstall a recent version of NodeJS for your platform, then add the following to the mcpServers section of your claude_desktop_config.json file:\n\n \n\nPlease make sure you are using Claude Desktop 0.78 or greater.\n\nThis will get you started with an Image Generator.\n\nBasic setup\n\nSupply a list of HuggingFace spaces in the arguments. mcp-hfspace will find the most appropriate endpoint and automatically configure it for usage. An example claude_desktop_config.json is supplied below.\n\nBy default the current working directory is used for file upload/download. On Windows this is a read/write folder at \\users\\ \\AppData\\Roaming\\Claude\\ Text Output: david bowie\n \n\n_If you are uploading something to Claude\'s context use the Paperclip Attachment button, otherwise specify the filename for the Server to send directly._\n\nWe can also supply a URL. For example : use paligemma to detect humans in https://e3.365dm.com/24/12/1600x900/skynews-taylor-swift-eras-tour_6771083.jpg?20241209000914 -> One person is detected in the image - Taylor Swift on stage.\n\nExample 3 - Text-to-Speech (Download Audio)\n\nIn _Claude Desktop Mode_, the audio file is saved in the WORK_DIR, and Claude is notified of the creation. If not in desktop mode, the file is returned as a base64 encoded resource to the Client (useful if it supports embedded Audio attachments).\n\n \n\nExample 4 - Speech-to-Text (Upload Audio)\n\nHere, we use hf-audio/whisper-large-v3-turbo to transcribe some audio, and make it available to Claude.\n\n \n\nExample 5 - Image-to-Image\n\nIn this example, we specify the filename for microsoft/OmniParser to use, and get returned an annotated Image and 2 separate pieces of text: descriptions and coordinates. The prompt used was use omniparser to analyse ./screenshot.png and use the analysis to produce an artifact that reproduces that screen. DawnC/Pawmatch is also good at this.\n\n \n\nExample 6 - Chat\n\nIn this example, Claude sets a number of reasoning puzzles for Qwen, and asks follow-up questions for clarification.\n\n \n\nSpecifying API Endpoint\n\nIf you need, you can specify a specific API Endpoint by adding it to the spacename. So rather than passing in Qwen/Qwen2.5-72B-Instruct you would use Qwen/Qwen2.5-72B-Instruct/model_chat.\n\nClaude Desktop Mode\n\nThis can be disabled with the option --desktop-mode=false or the environment variable CLAUDE_DESKTOP_MODE=false. In this case, content as returned as an embedded Base64 encoded Resource.\n\nRecommended Spaces\n\nSome recommended spaces to try:\n\nImage Generation\n\n- shuttleai/shuttle-3.1-aesthetic\n- black-forest-labs/FLUX.1-schnell\n- yanze/PuLID-FLUX\n- gokaygokay/Inspyrenet-Rembg (Background Removal)\n- diyism/Datou1111-shou_xin - Beautiful Pencil Drawings\n\nChat\n\n- Qwen/Qwen2.5-72B-Instruct\n- prithivMLmods/Mistral-7B-Instruct-v0.3\n\nText-to-speech / Audio Generation\n\n- fantaxy/Sound-AI-SFX\n- parler-tts/parler_tts\n\nSpeech-to-text\n\n- hf-audio/whisper-large-v3-turbo\n- (the openai models use unnamed parameters so will not work)\n\nText-to-music\n\n- haoheliu/audioldm2-text2audio-text2music\n\nVision Tasks\n\n- microsoft/OmniParser\n- merve/paligemma2-vqav2\n- merve/paligemma-doc\n- DawnC/PawMatchAI\n- DawnC/PawMatchAI/on_find_match_click - for interactive dog recommendations\n\nOther Features\n\nPrompts\n\nPrompts for each Space are generated, and provide an opportunity to input. Bear in mind that often Spaces aren\'t configured with particularly helpful labels etc. Claude is actually very good at figuring this out, and the Tool description is quite rich (but not visible in Claude Desktop).\n\nResources\n\nA list of files in the WORK_DIR is returned, and as a convenience returns the name as "Use the file..." text. If you want to add something to Claude\'s context, use the paperclip - otherwise specify the filename for the MCP Server. Claude does not support transmitting resources from within Context.\n\nPrivate Spaces\n\nPrivate Spaces are supported with a HuggingFace token. The Token is used to download and save generated content.\n\nUsing Claude Desktop\n\nTo use with Claude Desktop, add the server config:\n\nOn MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n\nKnown Issues and Limitations\n\nmcp-hfspace\n\n- Endpoints with unnamed parameters are unsupported for the moment.\n- Full translation from some complex Python types to suitable MCP formats.\n\nClaude Desktop\n\n- Claude Desktop 0.75 doesn\'t seem to respond to errors from the MCP Server, timing out instead. For persistent issues, use the MCP Inspector to get a better look at diagnosing what\'s going wrong. If something suddenly stops working, it\'s probably due to exhausting your HuggingFace ZeroGPU quota - try again after a short period, or set up your own Space for hosting.\n- Claude Desktop seems to use a hard timeout value of 60s, and doesn\'t appear to use Progress Notifications to manage UX or keep-alive. If you are using ZeroGPU spaces, large/heavy jobs may timeout. Check the WORK_DIR for results though; the MCP Server will still capture and save the result if it was produced.\n- Claude Desktops reporting of Server Status, logging etc. isn\'t great - use @modelcontextprotocol/inspector to help diagnose issues.\n\nHuggingFace Spaces\n\n- If ZeroGPU quotas or queues are too long, try duplicating the space. If your job takes less than sixty seconds, you can usually change the function decorator @spaces.GPU(duration=20) in app.py` to request less quota when running the job.\n- Passing HF_TOKEN will make ZeroGPU quotas apply to your (Pro) HF account\n- If you have a private space, and dedicated hardware your HF_TOKEN will give you direct access to that - no quota\'s apply. I recommend this if you are using for any kind of Production task.\n\nThird Party MCP Services', 'readme_raw_len': 6795, 'one_line_zh': '借助 Hugging Face Spaces 实现开源图像生成、对话、视觉等多项能力', 'one_line_plain': 'Create and share AI applications using Hugging Face Spaces for image, text, and audio tasks'}, {'id': 3157, 'key': 'sunriseapps-imagesorcery-mcp', 'kind': 'mcp', 'name': 'sunriseapps/imagesorcery-mcp', 'one_line': '🐍 🏠 🐧 🍎 🪟 - Computer vision-based image recognition and editing tools', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'An MCP server providing tools for image processing operations', 'url': 'https://github.com/sunriseapps/imagesorcery-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/sunriseapps/imagesorcery-mcp', 'downloads': None, 'stars': 333, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/sunriseapps.png?size=64', 'official_desc': '🐍 🏠 🐧 🍎 🪟 - Computer vision-based image recognition and editing tools.', 'stars_own': 333, 'stars_creator': None, 'stars_own_repo': 'sunriseapps/imagesorcery-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '🪄 ImageSorcery MCP\n**ComputerVision-based 🪄 sorcery of local image recognition and editing tools for AI assistants**\n\nOfficial website: imagesorcery.net\n\n \n \n \n\n \n \n \n\n✅ With ImageSorcery MCP\n\n🪄 ImageSorcery empowers AI assistants with powerful image processing capabilities:\n\n- ✅ Crop, resize, and rotate images with precision\n- ✅ Remove background\n- ✅ Draw text and shapes on images\n- ✅ Add logos and watermarks\n- ✅ Detect objects using state-of-the-art models\n- ✅ Extract text from images with OCR\n- ✅ Use a wide range of pre-trained models for object detection, OCR, and more\n- ✅ Do all of this **locally**, without sending your images to any servers\n\nJust ask your AI to help with image tasks:\n\n> "copy photos with pets from folder photos to folder pets"\n \n\n> "Find a cat at the photo.jpg and crop the image in a half in height and width to make the cat be centered"\n \n😉 _**Hint:** Use full path to your files"._\n\n> "Enumerate form fields on this form.jpg with foduucom/web-form-ui-field-detection model and fill the form.md with a list of described fields"\n \n😉 _**Hint:** Specify the model and the confidence"._\n\n😉 _**Hint:** Add "use imagesorcery" to make sure it will use the proper tool"._\n\nYour tool will combine multiple tools listed below to achieve your goal.\n\n🛠️ Available Tools\n\n| Tool | Description | Example Prompt |\n|------|-------------|----------------|\n| blur | Blurs specified rectangular or polygonal areas of an image using OpenCV. Can also invert the provided areas e.g. to blur background. | "Blur the area from (150, 100) to (250, 200) with a blur strength of 21 in my image \'test_image.png\' and save it as \'output.png\'" |\n| change_color | Changes the color palette of an image | "Convert my image \'test_image.png\' to sepia and save it as \'output.png\'" |\n| config | View and update ImageSorcery MCP configuration settings | "Show me the current configuration" or "Set the default detection confidence to 0.8" |\n| crop | Crops an image using OpenCV\'s NumPy slicing approach | "Crop my image \'input.png\' from coordinates (10,10) to (200,200) and save it as \'cropped.png\'" |\n| detect | Detects objects in an image using models from Ultralytics. Can return segmentation masks (as PNG files) or polygons. | "Detect objects in my image \'photo.jpg\' with a confidence threshold of 0.4" |\n| draw_arrows | Draws arrows on an image using OpenCV | "Draw a red arrow from (50,50) to (150,100) on my image \'photo.jpg\'" |\n| draw_circles | Draws circles on an image using OpenCV | "Draw a red circle with center (100,100) and radius 50 on my image \'photo.jpg\'" |\n| draw_lines | Draws lines on an image using OpenCV | "Draw a red line from (50,50) to (150,100) on my image \'photo.jpg\'" |\n| draw_rectangles | Draws rectangles on an image using OpenCV | "Draw a red rectangle from (50,50) to (150,100) and a filled blue rectangle from (200,150) to (300,250) on my image \'photo.jpg\'" |\n| draw_texts | Draws text on an image using OpenCV | "Add text \'Hello World\' at position (50,50) and \'Copyright 2023\' at the bottom right corner of my image \'photo.jpg\'" |\n| fill | Fills specified rectangular, polygonal, or mask-based areas of an image with a color and opacity, or makes them transparent. Can also invert the provided areas e.g. to remove background. | "Fill the area from (150, 100) to (250, 200) with semi-transparent red in my image \'test_image.png\'" |\n| find | Finds objects in an image based on a text description. Can return segmentation masks (as PNG files) or polygons. | "Find all dogs in my image \'photo.jpg\' with a confidence threshold of 0.4" |\n| get_metainfo | Gets metadata information about an image file | "Get metadata information about my image \'photo.jpg\'" |\n| ocr | Performs Optical Character Recognition (OCR) on an image using EasyOCR | "Extract text from my image \'document.jpg\' using OCR with English language" |\n| overlay | Overlays one image on top of another, handling transparency | "Overlay \'logo.png\' on top of \'background.jpg\' at position (10, 10)" |\n| resize | Resizes an image using OpenCV | "Resize my image \'photo.jpg\' to 800x600 pixels and save it as \'resized_photo.jpg\'" |\n| rotate | Rotates an image using imutils.rotate_bound function | "Rotate my image \'photo.jpg\' by 45 degrees and save it as \'rotated_photo.jpg\'" |\n\n😉 _**Hint:** detailed information and usage instructions for each tool can be found in the tool\'s /src/imagesorcery_mcp/tools/README.md._\n\n📚 Available Resources\n\n| Resource URI | Description | Example Prompt |\n|--------------|-------------|----------------|\n| models://list | Lists all available models in the models directory | "Which models are available in ImageSorcery?" |\n\n😉 _**Hint:** detailed information and usage instructions for each resource can be found in the resource\'s /src/imagesorcery_mcp/resources/README.md._\n\n💬 Available Prompts\n\n| Prompt Name | Description | Example Usage |\n|-------------|-------------|---------------|\n| remove-background | Guides the AI through a comprehensive background removal workflow using object detection and masking tools | "Use the remove-background prompt to remove the background from my photo \'portrait.jpg\', keeping only the person" |\n\n😉 _**Hint:** detailed information and usage instructions for each prompt can be found in the prompt\'s /src/imagesorcery_mcp/prompts/README.md._\n\n🚀 Getting Started\n\nRequirements\n\n- Python 3.10 or higher\n- pipx (recommended) - for easy installation and virtual environment management\n- ffmpeg, libsm6, libxext6, libgl1-mesa-glx - system libraries required by OpenCV\n- Claude.app, Cline, or another MCP client\n\n \n Virtual environments (e.g. Docker) \n\nThese dependencies are typically included with OpenCV installation and don\'t require separate installation. But they might be missing in some virtual environments like Docker.\n\n**For Ubuntu/Debian systems:**\n \n\n**For Docker containers:**\nAdd this line to your Dockerfile:\n \n \n\nInstallation\n\n \n \n For Cline just copy-paste LLM-INSTALL.md \n\n \n \n\nMethod 1: Using pipx (Recommended)\n\npipx is the recommended way to install ImageSorcery MCP as it automatically handles virtual environment creation and management, making the installation process much simpler.\n\n \n 0. Install pipx (if not already installed): \n\n0. **Install pipx (if not already installed):**\n \n \n\n1. **Install ImageSorcery MCP with pipx:**\n \n\n2. **Run the post-installation script:**\n This step is crucial. It downloads the required models and attempts to install the clip Python package from GitHub.\n \n\nMethod 2: Manual Virtual Environment (Plan B)\n\n \n If pipx doesn\'t work for your system, you can manually create a virtual environment \n\nFor reliable installation of all components, especially the clip package (installed via the post-install script), it is **strongly recommended to use Python\'s built-in venv module instead of uv venv**.\n\n1. **Create and activate a virtual environment:**\n \n\n2. **Install the package into the activated virtual environment:**\n You can use pip or uv pip.\n \n\n3. **Run the post-installation script:**\n This step is crucial. It downloads the required models and attempts to install the clip Python package from GitHub into the active virtual environment.\n \n\n**Note:** When using this method, you\'ll need to provide the full path to the executable in your MCP client configuration (e.g., /full/path/to/venv/bin/imagesorcery-mcp).\n \n\nAdditional Notes\n \n What does the post-installation script do? \nThe imagesorcery-mcp --post-install script performs the following actions:\n\n- **Creates a config.toml configuration file** in the current directory, allowing users to customize default tool parameters.\n- Creates a models directory (usually within the site-packages directory of your virtual environment, or a user-specific location if installed globally) to store pre-trained models.\n- Generates an initial models/model_descriptions.json file there.\n- Downloads default YOLO models (yoloe-11l-seg-pf.pt, yoloe-11s-seg-pf.pt, yoloe-11l-seg.pt, yoloe-11s-seg.pt) required by the detect tool into this model', 'readme_raw_len': 16425, 'one_line_zh': '基于计算机视觉的图像识别与编辑工具', 'one_line_plain': 'Use computer vision tools for image recognition and editing'}, {'id': 5499, 'key': 'openimagedebugger-openimagedebugger', 'kind': 'repo', 'name': 'OpenImageDebugger/OpenImageDebugger', 'one_line': 'An advanced in-memory image visualization plugin for GDB and LLDB on Linux, with experimental support for MacOS and Windows. Previously known as gdb-imagewatch. Also available as an extension for VSCode and forks', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'An advanced in-memory image visualization plugin for GDB and LLDB on Linux, with experimental support for MacOS and Windows. Previously known as gdb-imagewatch. Also available as an extension for VSCode and forks', 'url': 'https://github.com/OpenImageDebugger/OpenImageDebugger', 'source': 'github', 'source_url': 'https://github.com/OpenImageDebugger/OpenImageDebugger', 'downloads': None, 'stars': 250, 'installs': None, 'mentions': 1, 'score': 103.5, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-12', 'times_seen': 1, 'icon': 'https://github.com/OpenImageDebugger.png?size=64', 'official_desc': 'An advanced in-memory image visualization plugin for GDB and LLDB on Linux, with experimental support for MacOS and Windows. Previously known as gdb-imagewatch. Also available as an extension for VSCode and forks', 'stars_own': 250, 'stars_creator': None, 'stars_own_repo': 'OpenImageDebugger/OpenImageDebugger', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Open Image Debugger: Enabling visualization of in-memory buffers on GDB/LLDB\n\n \n \n \n\nOpen Image Debugger is a tool for visualizing in-memory buffers during debug\nsessions, compatible with both GDB and LLDB. It works out of the box with\ninstances of the OpenCV Mat class and Eigen matrices, but can also be\ncustomized to work with any arbitrary data structure.\n\n \n\n> **Prefer VS Code or a fork (Cursor, VSCodium, Windsurf, …)?** Skip the manual\n> build — install the extension from the\n> VS Code Marketplace\n> or Open VSX.\n> See Installation below.\n\n> **New — using CLion or Android Studio?** The Open Image Debugger plugin is\n> now on the\n> JetBrains Marketplace:\n> plot buffers straight from the native C/C++ debugger, inside the IDE. See\n> Installation below.\n\n> **New — declarative custom types.** You can now describe your own buffer types\n> in a .oid/types.json file instead of writing Python; the same file works in\n> gdb, lldb, and the VS Code and JetBrains extensions. See\n> doc/declarative-types.md.\n\nDownload (experimental) \nA bit experimental, better to compile manually\n\nFeatures\n\n* GUI interactivity:\n * Scroll to zoom, left click+drag to move the buffer around;\n * Rotate buffers 90° clockwise or counterclockwise;\n * Go-to widget that quickly takes you to any arbitrary pixel location;\n* Buffer values: Zoom in close enough to inspect the numerical contents of any pixel.\n* Auto update: Whenever a breakpoint is hit, the buffer view is automatically\n updated.\n* Auto contrast: The entire range of values present in the buffer can be\n automatically mapped to the visualization range [0, 1], where 0\n represents black and 1 represents white.\n* The contrast range can be manually adjusted, which is useful for inspecting\n buffers with extreme values (e.g. infinity, nan and other outliers).\n* Link views together, moving all watched buffers simultaneously when any\n single buffer is moved on the screen\n* Supported buffer types: uint8_t, int16_t, uint16_t, int32_t, uint32_t,\n float and double\n* Supported buffer channels: Up to four channels (Grayscale, two-channels, RGB\n and RGBA)\n* GPU accelerated\n* Supports large buffers whose dimensions exceed GL_MAX_TEXTURE_SIZE.\n* Supports data structures that map to a ROI of a larger buffer.\n* Exports buffers as png images (with auto contrast) or octave/matlab matrix\n files (unprocessed).\n* Auto-load buffers being visualized in the previous debug session\n* Designed to scale well for HighDPI displays\n* Works on Linux, macOS X and Windows (experimental)\n\nSupported OSes\n\n* OID is developed with Ubuntu as the main target. The goal is to support the two latest LTS versions at a given time.\n * Ubuntu is also used as a basis for the minimum versions of the dependencies: we try to support the default versions of the packages you get via apt install\n* There are currently no plans to support other Linux distros. OID may or may not compile on your favorite distro, your mileage may vary.\n* Support for MacOS and Windows are somewhat experimental now - the code should be able to compile (see ), but the binaries are not actively tested - in fact we currently have no automated tests at all for any OS - help is more than welcome in this regard. Also, we haven\'t come up with a simple installation/usage guides for these OSes yet.\n\nRequirements\n\n* A C++20 compliant compiler\n* GDB **15.0.50+** or LLDB **18.1.3+**\n* CMake **3.28.3+**\n* Python **3.12.3+** development packages\n* OpenGL **2.1+** support\n* Linux only: Wayland and X11 development packages (needed to build the bundled GLFW) and GTK 3 development packages (needed by the native file-open dialog); see the apt install command below. Alternatively, configure with -DNFD_PORTAL=ON to use the xdg-desktop-portal (D-Bus) dialog backend instead of GTK.\n\nAll other third-party libraries are bundled as git submodules and built from source, so they don\'t need to be installed:\n\n* Dear ImGui — viewer UI\n* GLFW — window and OpenGL context management\n* Eigen — linear algebra for the visualization layer\n* Asio (standalone) — IPC between the debugger bridge and the viewer\n* nlohmann/json — settings persistence\n* stb — image decoding for opening files, plus PNG export and text rendering (stb_image, stb_image_write, stb_truetype)\n* nanosvg — toolbar icon rasterization\n* nativefiledialog-extended — native OS dialog for File → Open (native builds only)\n* GoogleTest — unit tests\n\nNote: this list might get out-of-date by accident. For a more accurate list of requirements, please check what is used in and in the CI container images defined in .\n\nInstallation\n\nVS Code and forks\n\nThe quickest way to get started is the Open Image Debugger extension, available for\nVS Code and compatible forks (Cursor, VSCodium, Windsurf, and others):\n\n* VS Code Marketplace\n* Open VSX\n\nCLion and Android Studio\n\nThe Open Image Debugger plugin brings the same viewer to JetBrains IDEs with a\nnative C/C++ debugger — CLion, and Android Studio with the NDK:\n\n* JetBrains Marketplace\n\nIf you\'d rather build and integrate the desktop version manually, follow the steps below.\n\nUbuntu Linux dependencies\n\nOn Ubuntu, you can install most of the dependencies with the following command:\n\n \n\nBuilding the Open Image Debugger\n\nClone the source code to any folder you prefer and initialize the\nsubmodules:\n\n \n\nNow run the following commands to build it:\n\n \n\n**GDB integration:** Edit the file ~/.gdbinit (create it if it doesn\'t exist)\nand append the following line:\n\n \n\n**LLDB integration:** Edit the file ~/.lldbinit (create it if it doesn\'t\nexist) and append the following line:\n\n \n\nMacOS Installation\n\nAt the moment, the MacOS build is only known to work with python3 and lldb\ninstalled from Homebrew (the system-provided LLDB from the\nXcode Command Line Tools is not supported). Install them with:\n\n \n\nMake sure python3 resolves to the Homebrew one — run which python3 and\nconfirm it points under the Homebrew prefix (/opt/homebrew on Apple Silicon,\n/usr/local on Intel; brew --prefix prints it), rather than a pyenv, conda or\nsystem Python. The standard Homebrew install puts that prefix\'s bin on your\nPATH.\n\nThen debug your program using the Homebrew LLDB, for example:\n\n \n\nTesting your installation\n\nAfter compiling the plugin, you can test it by running the following command\n(use the same Python 3 interpreter CMake found when building):\n\n \n\nOn MacOS, invoke the test with the full path to the Homebrew python3, for\nexample:\n\n \n\nIf the installation was succesful, you should see the Open Image Debugger window\nwith the buffers sample_buffer_1 and sample_buffer_2.\n\nUsing plugin\n\nWhen the debugger hits a breakpoint, the Open Image Debugger window will be\nopened. You only need to type the name of the buffer to be watched in the\n"add symbols" input, and press .\n\nOpening image files directly\n\nYou can also open an image or NumPy array in the viewer without a debugger\nsession at all, either from the **File → Open** menu (shortcut Ctrl+O) or\nfrom the command line.\n\nFrom the command line, pass one or more files with the repeatable -o /\n--open flag:\n\n \n\nSupported formats:\n\n| Category | Extensions |\n| --- | --- |\n| Images (via stb_image) | png, jpg/jpeg, bmp, tga, gif, psd, hdr, ppm/pgm/pnm |\n| NumPy arrays | npy (little-endian uint8/uint16/int16/int32/float32/float64; 2-D grayscale or 3-D with 1–4 channels) |\n\nFiles opened this way are shown alongside any debugger buffers, but they are\nnever reported back to a debugger and are not saved as session state.\n\n> **Linux build note:** the native file dialog requires GTK 3\n> (libgtk-3-dev) at build time, or configure with -DNFD_PORTAL=ON to use\n> the xdg-desktop-portal (D-Bus) backend instead. MacOS and Windows need no\n> extra packages.\n\nOpen Image Debugger does not register itself as a system handler for these\nfile types; use the **File → Open** menu or the --open flag to load them.\n\nAuto-contrast and manual contrast\n\nThe (min) and (max) fields on top of the buffer view can be changed to control\nautocontrast settings. By default, O', 'readme_raw_len': 13409, 'one_line_zh': '面向 Linux 上 GDB 与 LLDB 的高级内存图像可视化插件，实验性支持 macOS 与 Windows', 'one_line_plain': None}, {'id': 3399, 'key': 'hellokaton-unsplash-mcp-server', 'kind': 'mcp', 'name': 'hellokaton/unsplash-mcp-server', 'one_line': '🐍 ☁️ - Unsplash image search', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A MCP server for Unsplash image search.', 'url': 'https://github.com/hellokaton/unsplash-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/hellokaton/unsplash-mcp-server', 'downloads': None, 'stars': 237, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/hellokaton.png?size=64', 'official_desc': '— A MCP server for Unsplash image search. ☆`237`', 'stars_own': 237, 'stars_creator': None, 'stars_own_repo': 'hellokaton/unsplash-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Unsplash MCP Server\n\nEnglish | 简体中文\n\n> A simple MCP server for seamless Unsplash image integration and search capabilities.\n\n \n \n\n \n \n\n📋 Overview\n\nUnsplash MCP Server is used for searching rich, high-quality images. It's ideal for developers who want to integrate Unsplash functionality into their own applications.\n\n✨ Features\n\n- **Advanced Image Search**: Search Unsplash's extensive photo library with filters for:\n - Keyword relevance\n - Color schemes\n - Orientation options\n - Custom sorting and pagination\n\n🔑 Obtaining Unsplash Access Key\n\nBefore installing this server, you'll need to obtain an Unsplash API Access Key:\n\n1. Create a developer account at Unsplash\n2. Register a new application\n3. Get your Access Key from the application details page\n4. Use this key in the configuration steps below\n\nFor more details, refer to the official Unsplash API documentation.\n\n🚀 Installation\n\nTo install Unsplash Image Integration Server for Claude Desktop automatically via Smithery:\n\nIDE Setup\n\n**Cursor IDE**\n\n \n\n**Windsurf**\n\n \n\n**Cline**\n\n \n\nManual Installation\n\n \n\n**Cursor Editor Integration**\n\nAdd the following configuration to your Cursor editor's settings.json:\n\n⚠️ **Note:** Please adjust the following configuration according to your actual installation:\n\n- If uv is not in your system PATH, use an absolute path (e.g., /path/to/uv)\n- ./server.py should be modified to the actual location of your server script (can use absolute path or path relative to workspace)\n\n \n\n \n\nUsing in Cursor\n\n \n\n🛠️ Available Tools\n\nSearch Photos\n\n \n\n🔄 Other Implementations\n\n- Golang: unsplash-mcp-server\n- Java: unsplash-mcp-server\n\n📄 License\n\nMIT License\n\n📬 Contact\n\n- Twitter/X\n- GitHub Issues", 'readme_raw_len': 1688, 'one_line_zh': '基于 Unsplash 的图片搜索服务', 'one_line_plain': 'Search and download Unsplash images'}, {'id': 329, 'key': 'constantineb6-comfy-pilot', 'kind': 'mcp', 'name': 'ConstantineB6/comfy-pilot', 'one_line': '🐍 🏠 - View, edit and run ComfyUI node-based image generation workflows, with an embedded terminal', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server + embedded terminal that lets Claude Code see and edit your ComfyUI workflows', 'url': 'https://github.com/ConstantineB6/comfy-pilot', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ConstantineB6/comfy-pilot', 'downloads': None, 'stars': 232, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ConstantineB6.png?size=64', 'official_desc': '🐍 🏠 - View, edit and run ComfyUI node-based image generation workflows, with an embedded terminal.', 'stars_own': 232, 'stars_creator': None, 'stars_own_repo': 'ConstantineB6/comfy-pilot', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Comfy Pilot\n\n \n \n \n\nTalk to your ComfyUI workflows. Comfy Pilot gives Claude Code direct access to see, edit, and run your workflows — with an embedded terminal right inside ComfyUI.\n\n \n\nWhy?\n\nBuilding ComfyUI workflows means manually searching for nodes, dragging connections, and tweaking values one at a time. With Comfy Pilot, you just describe what you want:\n\n- *"Build me an SDXL workflow with ControlNet"* — Claude creates all the nodes, connects them, and sets the parameters\n- *"Look at the output and increase the detail"* — Claude sees your generated image and adjusts the workflow\n- *"Download the FLUX schnell model and set up a workflow for it"* — Claude downloads the model and builds a workflow from scratch\n\nNo copy-pasting node names. No hunting through menus. Just say what you want.\n\nInstallation\n\n**CLI (Recommended):**\n \n\n**ComfyUI Manager:**\n1. Open ComfyUI\n2. Click **Manager** → **Install Custom Nodes**\n3. Search for "Comfy Pilot"\n4. Click **Install**\n5. Restart ComfyUI\n\n**Git Clone:**\n \n\nClaude Code CLI will be installed automatically if not found.\n\nRequirements\n\n- ComfyUI\n- Python 3.8+\n\nFeatures\n\n- **MCP Server** - Gives Claude Code direct access to view, edit, and run your ComfyUI workflows\n- **Embedded Terminal** - Full xterm.js terminal running Claude Code right inside ComfyUI\n- **Image Viewing** - Claude can see outputs from Preview Image and Save Image nodes\n- **Graph Editing** - Create, delete, move, and connect nodes programmatically\n\nDemo\n\nhttps://github.com/user-attachments/assets/325b1194-2334-48a1-94c3-86effd1fef02\n\nUsage\n\n1. Restart ComfyUI after installation\n2. The floating Claude Code terminal appears in the top-right corner\n3. The MCP server is automatically configured for Claude Code\n4. Ask Claude to help with your workflow:\n - "What nodes are in my current workflow?"\n - "Add a KSampler node connected to my checkpoint loader"\n - "Look at the preview image and tell me what you see"\n - "Run the workflow up to node 5"\n\nMCP Tools\n\nThe MCP server provides these tools to Claude Code:\n\n| Tool | Description |\n|------|-------------|\n| get_workflow | Get the current workflow from the browser |\n| summarize_workflow | Human-readable workflow summary |\n| get_node_types | Search available node types with filtering |\n| get_node_info | Get detailed info about a specific node type |\n| get_status | Queue status, system stats, and execution history |\n| run | Run workflow (optionally up to a specific node) or interrupt |\n| edit_graph | Batch create, delete, move, connect, and configure nodes |\n| view_image | View images from Preview Image / Save Image nodes |\n| search_custom_nodes | Search ComfyUI Manager registry for custom nodes |\n| install_custom_node | Install a custom node from the registry |\n| uninstall_custom_node | Uninstall a custom node |\n| update_custom_node | Update a custom node to latest version |\n| download_model | Download models from Hugging Face, CivitAI, or direct URLs |\n\nExample: Creating Nodes\n\n \n\nClaude will use edit_graph to:\n1. Create the KSampler node\n2. Connect the MODEL output from CheckpointLoader to KSampler\'s model input\n3. Position it appropriately in the graph\n\nExample: Viewing Images\n\n \n\nClaude will use view_image to fetch and analyze the image output.\n\nExample: Downloading Models\n\n \n\nClaude will use download_model to download from Hugging Face to your ComfyUI models folder. Supports:\n- Hugging Face (including gated models with token auth)\n- CivitAI\n- Direct download URLs\n\nTerminal Controls\n\n- **Drag** title bar to move\n- **Drag** bottom-right corner to resize\n- **−** Minimize\n- **×** Close\n- **↻** Reconnect session\n\nArchitecture\n\n \n\nFiles\n\n- __init__.py - Plugin backend: WebSocket terminal, REST endpoints\n- js/claude-code.js - Frontend: xterm.js terminal, workflow sync\n- mcp_server.py - MCP server for Claude Code integration\n- CLAUDE.md - Instructions for Claude when working with ComfyUI\n\nTroubleshooting\n\n"Command \'claude\' not found"\n\nInstall Claude Code CLI:\n\n**macOS / Linux / WSL:**\n \n\n**Windows (PowerShell):**\n \n\n**Windows (CMD):**\n \n\nMCP server not connecting\n\nThe plugin auto-configures MCP on startup. Check ComfyUI console for errors, or manually add to ~/.claude.json:\n\n \n\nTerminal disconnected\n\nClick the ↻ button to reconnect, or check ComfyUI console for errors.\n\nLicense\n\nMIT', 'readme_raw_len': 4305, 'one_line_zh': '🐍 🏠 - 查看、编辑并运行 ComfyUI 基于节点的图像生成工作流，内置终端', 'one_line_plain': 'Lets you view, edit and run ComfyUI node-based image generation workflows, with an embedded terminal'}, {'id': 4238, 'key': 'johannesbrandenburger-typst-mcp', 'kind': 'mcp', 'name': 'johannesbrandenburger/typst-mcp', 'one_line': '🐍 🏠 - Typst typesetting tools: convert between LaTeX and Typst, validate Typst syntax and generate images…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Typst MCP Server is an MCP (Model Context Protocol) implementation that helps AI models interact with Typst, a markup-based typesetting system. The server provides tools for converting between LaTeX and Typst, validating Typst syntax, and generating images from Typst code.', 'url': 'https://github.com/johannesbrandenburger/typst-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/johannesbrandenburger/typst-mcp', 'downloads': None, 'stars': 184, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/johannesbrandenburger.png?size=64', 'official_desc': '🐍 🏠 - Typst typesetting tools: convert between LaTeX and Typst, validate Typst syntax and generate images from Typst code.', 'stars_own': 184, 'stars_creator': None, 'stars_own_repo': 'johannesbrandenburger/typst-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Typst MCP Server\n\nTypst MCP Server is an MCP (Model Context Protocol) implementation that helps AI models interact with Typst, a markup-based typesetting system. The server provides tools for converting between LaTeX and Typst, validating Typst syntax, and generating images from Typst code.\n\nAvailable Tools\n\n>⚠️ Currently all the functionality is implemented as tools, because Cursor and VS Code are not able to handle the other primitives yet.\n\nThe server provides the following tools:\n\n1. **list_docs_chapters()**: Lists all chapters in the Typst documentation.\n - Lets the LLM get an overview of the documentation and select a chapter to read.\n - The LLM should select the relevant chapter to read based on the task at hand.\n\n2. **get_docs_chapter(route)**: Retrieves a specific chapter from the Typst documentation.\n - Based on the chapter selected by the LLM, this tool retrieves the content of the chapter.\n - Also available as get_docs_chapters(routes: list) for retrieving multiple chapters at once.\n\n3. **latex_snippet_to_typst(latex_snippet)**: Converts LaTeX code to Typst using Pandoc.\n - LLMs are better at writing LaTeX than Typst, so this tool helps convert LaTeX code to Typst.\n - Also available as latex_snippets_to_typst(latex_snippets: list) for converting multiple LaTeX snippets at once.\n\n4. **check_if_snippet_is_valid_typst_syntax(typst_snippet)**: Validates Typst code.\n - Before sending Typst code to the user, the LLM should check if the code is valid.\n - Also available as check_if_snippets_are_valid_typst_syntax(typst_snippets: list) for validating multiple Typst snippets at once.\n\n5. **typst_to_image(typst_snippet)**: Renders Typst code to a PNG image.\n - Before sending complex Typst illustrations to the user, the LLM should render the code to an image and check if it looks correct.\n - Only relevant for multi modal models.\n\nGetting Started\n\n- Clone this repository\n - git clone https://github.com/johannesbrandenburger/typst-mcp.git\n- Clone the typst repository\n - git clone https://github.com/typst/typst.git\n- Run the docs generation in the typst repository\n - cargo run --package typst-docs -- --assets-dir ../typst-mcp/typst-docs --out-file ../typst-mcp/typst-docs/main.json\n - Make sure to adjust the path to your local clone of the typst-mcp repository\n - This will generate the main.json and the assets in the typst-docs folder\n- Install required dependencies: uv sync (install uv if not already installed)\n \n- Install Typst\n\nRunning the Server\n\nLocal Installation\n\nExecute the server script:\n\n \n\nOr install it in Claude Desktop with MCP:\n\n \n\nOr use the new agent mode in VS Code:\n\nAgent mode: available to all users and supports MCP\n\nDocker\n\nFor Docker installation and usage, see DOCKER.md for detailed build information and instructions.\n\nPlatform Configuration\n\nThis MCP server can be integrated with various AI coding platforms. Below are configuration instructions for popular platforms:\n\nClaude Desktop\n\nAdd the following to your Claude Desktop configuration file (claude_desktop_config.json):\n\n**macOS**: ~/Library/Application Support/Claude/claude_desktop_config.json \n**Windows**: %APPDATA%\\Claude\\claude_desktop_config.json\n\n \n\nOr for local installation:\n\n \n\nCursor\n\nCreate .cursor/mcp.json in your project root for project-specific configuration:\n\n \n\nOr create ~/.cursor/mcp.json for global configuration. For local installation:\n\n \n\nRoo Code\n\nCreate .roo/mcp.json in your project root for project-specific configuration:\n\n \n\nOr edit global settings via Roo Code MCP settings view. For local installation:\n\n \n\nOpenCode\n\nCreate or edit the opencode.json file in your project root:\n\n \n\nFor local installation:\n\n \n\nClaude Code\n\nConfigure MCP servers in your Claude Code settings file (~/.claude/settings.json):\n\n \n\nOr for local installation:\n\n \n\nVS Code with Agent Mode\n\nVS Code's Agent Mode has native MCP support (no extension required):\n\n1. Enable Agent Mode in VS Code settings (chat.agent.enabled)\n2. Create .vscode/mcp.json in your workspace:\n\n \n\nFor global configuration, add to your user profile via **MCP: Open User Configuration** command.\n\nJSON Schema of the Typst Documentation\n\n>⚠️ The schema of the typst documentation is not stable and may change at any time. The schema is generated from the typst source code and is not guaranteed to be complete or correct. If the schema changes, this repository will need to be updated accordingly, so that the docs functionality works again.", 'readme_raw_len': 4448, 'one_line_zh': 'Typst 排版工具：在 LaTeX 与 Typst 之间转换、校验 Typst 语法并生成图片', 'one_line_plain': 'Typst tools to convert, validate syntax and generate images from LaTeX'}, {'id': 1411, 'key': 'docker-hub-mcp', 'kind': 'mcp', 'name': 'docker/hub-mcp', 'one_line': '🎖️ 📇 ☁️ 🏠 - Interact with Docker Hub: repositories, search and Docker Hardened Images', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Docker Hub MCP Server', 'url': 'https://github.com/docker/hub-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/docker/hub-mcp', 'downloads': None, 'stars': 169, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/docker.png?size=64', 'official_desc': '🎖️ 📇 ☁️ 🏠 - Interact with Docker Hub: repositories, search and Docker Hardened Images.', 'stars_own': 169, 'stars_creator': None, 'stars_own_repo': 'docker/hub-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Docker Hub MCP Server\n\n \n\nThe Docker Hub MCP Server is a Model Context Protocol (MCP) server that interfaces with Docker Hub APIs to make them accessible to LLMs, enabling intelligent content discovery and repository management.\n\nDevelopers building with containers, especially in AI and LLM-powered workflows, often face inadequate context across the vast landscape of Docker Hub images. As a result, LLMs struggle to recommend the right images, and developers lose time manually searching instead of building.\n\n \n \n \n\nUse Cases\n\n- AI-powered image recommendations - LLMs access real-time Docker Hub data for accurate container image suggestions.\n- Enhanced content discovery - AI tools help developers find the right images faster.\n- Simplified Hub workflows - Manage Docker repositories and images using natural language.\n\nPrerequisites\n\n- Docker installed\n- Node.js (version 22+)\n- [Optional] A Docker Personal Access Token (PAT) with appropriate permissions\n\nSetup\n\n1. **Build**\n\n \n\n2. **Run**\n\n \n\n- Default args:\n - transport: Choose between http or stdio (default: stdio)\n - port=3000\n - host=127.0.0.1 (HTTP transport only)\n This starts the server with default settings and can only access public Docker Hub content.\n\n> [!IMPORTANT]\n> The http transport binds to 127.0.0.1 (loopback) by default and **requires\n> authentication**. See Securing the HTTP transport\n> before exposing it to a network.\n\nRun in inspector [Optional]\n\nThe MCP Inspector provides a web interface to test your server:\n\n \n\nSecuring the HTTP transport\n\nThe stdio transport is only reachable by the local process that spawns it. The\nhttp transport, however, dispatches every tool call using the server operator's\nDocker Hub Personal Access Token (HUB_PAT_TOKEN). Anyone who can reach the HTTP\nendpoint can therefore act as that Docker Hub identity — including creating and\nmodifying repositories. To prevent this, the HTTP transport is locked down by\ndefault:\n\n- **Loopback binding.** The listener binds to 127.0.0.1 unless you pass\n --host= (for example --host=0.0.0.0 to expose it from a container).\n- **Authentication required (fail-closed).** In http mode the server refuses to\n start unless you either provide a bearer token or explicitly opt out. Set the\n token via the MCP_AUTH_TOKEN environment variable; clients must then send it as\n Authorization: Bearer on every request.\n- **DNS-rebinding / CSRF protection.** Requests are rejected when the Host header\n is not in the allow-list (loopback plus --host, extendable with\n --allowed-hosts), or when they carry a browser Origin header that is not\n listed in --allowed-origins. Non-browser MCP clients are unaffected.\n\nRun the HTTP transport with authentication:\n\n \n\nExpose it beyond loopback (e.g. inside a container), still authenticated:\n\n \n\n| Flag / env var | Purpose |\n| ------------------------- | --------------------------------------------------------------- |\n| MCP_AUTH_TOKEN | Bearer token required on every /mcp request. |\n| --host= | Address to bind (default 127.0.0.1). |\n| --allowed-hosts=a,b | Extra Host header values to accept (comma-separated). |\n| --allowed-origins=a,b | Browser Origin values to accept (comma-separated). |\n| --allow-unauthenticated | Serve /mcp with **no** authentication. Insecure; opt-in only. |\n\n> [!WARNING]\n> --allow-unauthenticated disables authentication entirely and exposes your\n> Docker Hub PAT to any client that can reach the port. Only use it on a trusted,\n> isolated network.\n\nAuthenticate with docker\n\nBy default this MCP server can only query public content on Docker Hub. In order to manage your repositories you need to provide authentication.\n\nRun with authentication\n\n \n\nRun in inspector [Optional]\n\n \n\nUsage in Docker Ask Gordon\n\nYou can configure Gordon to be a host that can interact with the Docker Hub MCP server.\n\nGordon Setup\n\nAsk Gordon is your personal AI assistant embedded in Docker Desktop and the Docker CLI. It's designed to streamline your workflow and help you make the most of the Docker ecosystem.\n\nYou can configure Gordon to be a client that can interact with the Docker Hub MCP server.\n\n1. Create the gordon-mcp.yml file file in your working directory.\n2. Replace environment variables in the gordon-mcp.yml with your Docker Hub username and a PAT token.\n\n \n\n2. Run docker build -t hub .\n3. Run docker ai\n\nUsage in other MCP Clients\n\nUsage with Claude Desktop\n\n> NOTE: Make sure you have already built the application as mentioned in Step 1.\n\n1. Add the Docker Hub MCP Server configuration to your claude_desktop_config.json:\n\n> NOTE: if you are using nvm to manage node versions, you should put the node binary path in the command property. This ensure MCP server runs with the right node version. You can find your binary path by running which node in your shell\n\nFor public repositories only:\n\n- /FULL/PATH/TO/YOUR/docker-hub-mcp-server - The complete path to where you cloned this repository\n\n \n\nFor authenticated access (recommended):\n\nReplace the following values:\n\n- YOUR_DOCKER_HUB_USERNAME - Your Docker Hub username\n- YOUR_DOCKER_HUB_PERSONAL_ACCESS_TOKEN - Your Docker Hub Personal Access Token\n- /FULL/PATH/TO/YOUR/docker-hub-mcp-server - The complete path to where you cloned this\n\n \n\n2. Save the configuration file and completely restart Claude Desktop for the changes to take effect.\n\nUsage with VS Code\n\n1. Add the Docker Hub MCP Server configuration to your User Settings (JSON) file in VS Code. You can do this by opening the Command Palette and typing Preferences: Open User Settings (JSON).\n\nFor public repositories only:\n\n- /FULL/PATH/TO/YOUR/docker-hub-mcp-server - The complete path to where you cloned this repository\n\n \n\nFor authenticated access (recommended):\n\nReplace the following values:\n\n- YOUR_DOCKER_HUB_USERNAME - Your Docker Hub username\n- YOUR_DOCKER_HUB_PERSONAL_ACCESS_TOKEN - Your Docker Hub Personal Access Token\n- /FULL/PATH/TO/YOUR/docker-hub-mcp-server - The complete path to where you cloned this\n\n \n\n2. Open the Command Palette and type MCP: List Servers.\n3. Select docker-hub and select Start Server.\n\nTask Examples\n\nFinding images\n\n \n\nRepository Management\n\n \n\nPull/Push Images\n\n \n\nTag Management\n\n \n\nDocker Hardened Images\n\n \n\nTools\n\nSearch\n\n- **search** - Search repositories and content using Search V4 API\n - query: Search query parameter (string, required)\n - architectures: Filter on architectures (string, optional)\n - badges: Filter by image content type badges (string, optional)\n - categories: Filter on categories (string, optional)\n - extension_reviewed: Filter on reviewed extensions (boolean, optional)\n - from: Number of documents to skip for pagination (number, optional)\n - images: Filter on image names (string, optional)\n - operating_systems: Filter on operating systems (string, optional)\n - order: Change the ordering of results (string, optional)\n - size: Maximum number of results to return (number, optional)\n - sort: Sort results by search field (string, optional)\n - type: Filter on repository content type (string, optional)\n\nNamespace Management\n\n- **get_namespaces** - Get list of namespaces the user is a member of\n - page: Page number for pagination (string, optional)\n - page_size: Number of items per page (string, optional)\n\nRepository Management\n\n- **list_repositories_by_namespace** - List all repositories under the provided namespace\n - namespace: Repository namespace (string, required)\n - content_types: Comma-delimited list of content types (string, optional)\n - media_types: Comma-delimited list of media types (string, optional)\n - name: Search by repository name (string, optional)\n - ordering: Sort order (string, optional)\n - page: Page number (number, optional)\n - page_size: Number of items per page (number, optional)\n\n- **get_repository_info** - Get information about a repository\n - namespace: Repository namespace (string, required)\n - repository: Repository name (string, required)\n\n- **check_repository** - Check if a repository exists\n - namespace: Repository namespace (string, requi", 'readme_raw_len': 9582, 'one_line_zh': '与 Docker Hub 交互：仓库、搜索及 Docker Hardened Images', 'one_line_plain': 'The tool lets users interact with Docker Hub: searching repositories and accessing Docker Hardened Images'}, {'id': 3448, 'key': 'mikechao-brave-search-mcp', 'kind': 'mcp', 'name': 'mikechao/brave-search-mcp', 'one_line': "📇 ☁️ - Web, image, news, video and local point-of-interest search using Brave's Search API", 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'An MCP Server implementation that integrates the Brave Search API, providing, Web Search, Local Points of Interest Search, Image Search, Video Search, News Search and LLM Context Search capabilities', 'url': 'https://github.com/mikechao/brave-search-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/mikechao/brave-search-mcp', 'downloads': None, 'stars': 130, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/mikechao.png?size=64', 'official_desc': "📇 ☁️ - Web, image, news, video and local point-of-interest search using Brave's Search API.", 'stars_own': 130, 'stars_creator': None, 'stars_own_repo': 'mikechao/brave-search-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Brave Search MCP Server\n\nAn MCP Server implementation that integrates the Brave Search API, providing, Web Search, Local Points of Interest Search, Video Search, Image Search, News Search and LLM Context Search capabilities\n\n \n \n \n\nFeatures\n\n- **Web Search**: Perform a regular search on the web\n- **Image Search**: Search the web for images.\n- **News Search**: Search the web for news\n- **Video Search**: Search the web for videos\n- **Local Points of Interest Search**: Search for local physical locations, businesses, restaurants, services, etc\n- **LLM Context Search**: Fetch and extract full web page content optimized for reading and synthesizing sources\n\nTools\n\n- **brave_web_search**\n - Execute web searches using Brave's API\n - Inputs:\n - query (string): The term to search the internet for\n - count (number, optional): The number of results to return (max 20, default 10)\n - offset (number, optional, default 0): The offset for pagination\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_image_search**\n - Get images from the web relevant to the query\n - Inputs:\n - query (string): The term to search the internet for images of\n - count (number, optional): The number of images to return (max 50, default 10)\n\n- **brave_news_search**\n - Searches the web for news\n - Inputs:\n - query (string): The term to search the internet for news articles, trending topics, or recent events\n - count (number, optional): The number of results to return (max 20, default 10)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_local_search**\n - Search for local businesses, services and points of interest\n - Falls back to brave_web_search on the initial page if no location results are found\n - Inputs:\n - query (string): Local search term\n - count (number, optional): The number of results to return (max 20, default 5)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n\n- **brave_video_search**\n - Search the web for videos\n - Inputs:\n - query: (string): The term to search for videos\n - count: (number, optional): The number of videos to return (max 20, default 10)\n - offset (number, optional, default 0): The zero-based offset for pagination (max 9)\n - freshness (enum, optional): Filters search results by when they were discovered\n - The following values are supported\n - pd: Discovered within the last 24 hours.\n - pw: Discovered within the last 7 Days.\n - pm: Discovered within the last 31 Days.\n - py: Discovered within the last 365 Days\n - YYYY-MM-DDtoYYYY-MM-DD: Custom date range (e.g., 2022-04-01to2022-07-30)\n\n- **brave_llm_context_search**\n - Pre-extracted web content optimized for AI agents, LLM grounding, and RAG pipelines.\n - Uses Brave's balanced context threshold mode in compact mode and disables Brave relevance filtering in full mode.\n - Inputs:\n - query (string): The search query. Maximum 400 characters and 50 words.\n - url (string, optional): Optional URL to target. When provided, query and URL are combined for retrieval and only snippets from this exact URL are returned.\n - count (number, optional, default 8): The maximum number of search results considered. Minimum 1, maximum 50.\n - maximumNumberOfUrls (number, optional, default 8): The maximum number of URLs to include in the response. Minimum 1, maximum 50.\n - maximumNumberOfTokens (number, optional, default 2048): The approximate maximum number of tokens in the returned context. Minimum 1024, maximum 32768.\n - maximumNumberOfSnippets (number, optional, default 16): The maximum number of snippets across all URLs. Minimum 1, maximum 100.\n - maximumNumberOfTokensPerUrl (number, optional, default 512): The maximum number of tokens per URL. Minimum 512, maximum 8192.\n - maximumNumberOfSnippetsPerUrl (number, optional, default 2): The maximum number of snippets per URL. Minimum 1, maximum 100.\n - responseMode (enum, optional, default compact): compact applies Brave's balanced relevance filtering plus local snippet filtering/truncation. full disables Brave's relevance filtering and returns raw snippets without local filtering or truncation.\n - maxSnippetChars (number, optional, default 400): Maximum characters per snippet in compact mode. Minimum 80, maximum 4000.\n - maxOutputChars (number, optional, default 8000): Approximate maximum serialized response size in compact mode. Minimum 1000, maximum 100000.\n\nOpenAI Apps & MCP Apps Support\n\n \n \n \n \n \n ▶️ Click to watch the demo video \n \n\nThere is now support for OpenAI Apps and MCP Apps in this MCP Server. When UI mode is enabled for each tool there is a corresponding UI widget that let's you control what gets added to the model's context. See the directions in usage with ChatGPT section.\n\nConfiguration\n\nGetting an API Key\n\n1. Sign up for a Brave Search API account\n2. Choose a plan\n3. Generate your API key from the developer dashboard\n\nRuntime modes\n\nBy default the MCP server runs in stdio mode.\n\n \n\nTo enable Streamable HTTP mode:\n\n \n\nBy default the server listens on port 3001.\nThe URL is:\n\n \n\nThere are two configuration modes:\n\n- Env mode: BRAVE_MCP_CONFIG is unset. Feature toggles come from environment variables exactly as in previous releases.\n- File mode: BRAVE_MCP_CONFIG=/path/to/config.toml is set. The TOML file becomes the single source of truth for feature settings, and overlapping feature env vars are ignored with warnings.\n\nEnvironment-only settings\n\nThese settings are always read from the process environment, regardless of mode:\n\n- BRAVE_API_KEY (required): Brave Search API key.\n- PORT (optional): HTTP port (default: 3001).\n- HOST (optional): Interface to bind to (default: 0.0.0.0).\n- BRAVE_MCP_CONFIG (optional): Absolute or relative path to a TOML config file for feature settings.\n\nEnv mode feature settings\n\nWhen BRAVE_MCP_CONFIG is not set, these feature env vars are supported:\n\n- ALLOWED_HOSTS (HTTP mode only): Comma-separated list of allowed hostnames for Host header validation.\n - Example: ALLOWED_HOSTS=localhost,127.0.0.1,my-app.ngrok-free.app\n - Use hostnames only (no scheme/path), e.g. my-app.ngrok-free.app not https://my-app.ngrok-free.app/mcp\n- BRAVE_MCP_POLICY_FILE: JSON policy file path.\n- BRAVE_MCP_POLICY_REDACT: true to redact matched text instead of blocking it.\n- BRAVE_MCP_REQUEST_LIMIT: Positive integer request cap.\n- BRAVE_MCP_WINDOW_SECONDS: Non-negative integer rolling window size.\n- BRAVE_MCP_COOLDOWN_SECONDS: Non-negative integer cooldown after the limit is exceeded.\n- BRAVE_MCP_AUDIT_LOG: true to emit audit logs.\n- BRAVE_MCP_AUDIT_LOG_RAW: true to include raw query text in audit logs.\n- BRAVE_MCP_REQUIRE_JUSTIFICATION: true to reject tool calls without a justification string.\n\nExamples:\n\n \n\n \n\nFile mode (BRAVE_MCP_CONFIG)\n\nWhen BRAVE_MCP_CONFIG is set, the file controls feature configuration, including the HTTP host allowlist.\n\n \n\nNotes:\n\n- BRAVE_API_KEY, PORT, and HOST remain environment-only even in file mode.\n- If you set overlapping feature env vars such as BRAVE_MCP_REQUEST_LIMIT or ALLOWED_HOSTS alongside BRAVE_MCP_CONFIG, startup warns that they are being ignored.\n- Unknown TOML keys also emit warnings so typos like [guardrails] are visible before you debug runtime behavior.\n\nTo validate a config file without starting the server, use the packaged entrypoint after building t", 'readme_raw_len': 10161, 'one_line_zh': '通过 Brave 搜索 API 进行网页、图片、新闻、视频及本地兴趣点搜索', 'one_line_plain': 'Brave Search API lets users search the web, images, news, videos, and local points of interest'}, {'id': 2589, 'key': 'deusxmachina-dev-memorylane', 'kind': 'mcp', 'name': 'deusXmachina-dev/memorylane', 'one_line': '📇 ☁️ 🏠 🍎 - Desktop app that captures screen activity, stores AI summaries and OCR text locally, and exposes…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/deusXmachina-dev/memorylane', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/deusXmachina-dev/memorylane', 'downloads': None, 'stars': 120, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/deusXmachina-dev.png?size=64', 'official_desc': '📇 ☁️ 🏠 🍎 - Desktop app that captures screen activity, stores AI summaries and OCR text locally, and exposes the history via semantic search and a timeline.', 'stars_own': 120, 'stars_creator': None, 'stars_own_repo': 'deusXmachina-dev/memorylane', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'MemoryLane\n\n \n\nApp Download\n\n \n\n \n\nWhat is MemoryLane?\n\nMemoryLane is a tray app for macOS and Windows that captures your screen as you work, summarizes each capture into text through your model endpoint, then deletes the screenshot. Only the summaries, OCR text, and embeddings are kept — in a local SQLite database with full-text and vector search. From there it detects recurring workflows, surfaces automation opportunities, and makes everything queryable from any AI chat over MCP.\n\n**Capture → summarize → store locally → detect patterns → query from any AI chat via MCP**\n\n \n \n \n\n🎬 Demo\n\nUse your own models\n\nBy default, captures are summarized through a managed OpenRouter endpoint. You can instead point MemoryLane at your own LLM — a local model via Ollama, vLLM, or LM Studio, or your own OpenRouter account — all from Settings, no rebuild. With a local endpoint, captures never leave your machine. See Using Your Own Models.\n\nPrivacy & Permissions\n\nSee Privacy & Permissions.\n\nCLI\n\nInstall the CLI from npm: @deusxmachina-dev/memorylane-cli\n\nCommunity\n\nQuestions, feedback, and feature ideas are welcome in our Discord server.\n\nJoin the MemoryLane Discord', 'readme_raw_len': 1167, 'one_line_zh': '记录屏幕活动的桌面应用，把 AI 摘要与 OCR 文本存在本地，并通过 MCP 暴露出来。', 'one_line_plain': 'Desktop app that captures screen activity, stores AI summaries and OCR text locally, and exposes them for use'}, {'id': 257, 'key': 'surescaleai-openai-gpt-image-mcp', 'kind': 'mcp', 'name': 'SureScaleAI/openai-gpt-image-mcp', 'one_line': "📇 ☁️ - Image generation and editing with OpenAI's GPT image models", 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/SureScaleAI/openai-gpt-image-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/SureScaleAI/openai-gpt-image-mcp', 'downloads': None, 'stars': 115, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/SureScaleAI.png?size=64', 'official_desc': "A Model Context Protocol (MCP) tool server for OpenAI's GPT-4o/gpt-image-1 image generation and editing APIs.", 'stars_own': 115, 'stars_creator': None, 'stars_own_repo': 'SureScaleAI/openai-gpt-image-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "openai-gpt-image-mcp\n\n \n \n \n \n \n \n \n\n---\n\nA Model Context Protocol (MCP) tool server for OpenAI's GPT-4o/gpt-image-1 image generation and editing APIs.\n\n- **Generate images** from text prompts using OpenAI's latest models.\n- **Edit images** (inpainting, outpainting, compositing) with advanced prompt control.\n- **Supports**: Claude Desktop, Cursor, VSCode, Windsurf, and any MCP-compatible client.\n\n---\n\n✨ Features\n\n- **create-image**: Generate images from a prompt, with advanced options (size, quality, background, etc).\n- **edit-image**: Edit or extend images using a prompt and optional mask, supporting both file paths and base64 input.\n- **File output**: Save generated images directly to disk, or receive as base64.\n\n---\n\n🚀 Installation\n\n \n\n---\n\n🔑 Configuration\n\nAdd to Claude Desktop or VSCode (including Cursor/Windsurf) config:\n\n \n\nAlso supports Azure deployments:\n\n \n\nAlso supports supplying an environment files:\n\n \n\n---\n\n⚡ Advanced\n\n- For create-image, set n to generate up to 10 images at once.\n- For edit-image, provide a mask image (file path or base64) to control where edits are applied.\n- Provide an environment file with --env-file path/to/file/.env\n- See src/index.ts for all options.\n\n---\n\n🧑\u200d💻 Development\n\n- TypeScript source: src/index.ts\n- Build: yarn build\n- Run: node dist/index.js\n\n---\n\n📝 License\n\nMIT\n\n---\n\n🩺 Troubleshooting\n\n- Make sure your OPENAI_API_KEY is valid and has image API access.\n- You must have a verified OpenAI organization. After verifying, it can take 15–20 minutes for image API access to activate.\n- File paths must be absolute.\n - **Unix/macOS/Linux**: Starting with / (e.g., /path/to/image.png)\n - **Windows**: Drive letter followed by : (e.g., C:/path/to/image.png or C:\\path\\to\\image.png)\n- For file output, ensure the directory is writable.\n- If you see errors about file types, check your image file extensions and formats.\n\n---\n\n⚠️ Limitations & Large File Handling\n\n- **1MB Payload Limit:** MCP clients (including Claude Desktop) have a hard 1MB limit for tool responses. Large images (especially high-res or multiple images) can easily exceed this limit if returned as base64.\n- **Auto-Switch to File Output:** If the total image size exceeds 1MB, the tool will automatically save images to disk and return the file path(s) instead of base64. This ensures compatibility and prevents errors like result exceeds maximum length of 1048576.\n- **Default File Location:** If you do not specify a file_output path, images will be saved to /tmp (or the directory set by the MCP_HF_WORK_DIR environment variable) with a unique filename.\n- **Environment Variable:**\n - MCP_HF_WORK_DIR: Set this to control where large images and file outputs are saved. Example: export MCP_HF_WORK_DIR=/your/desired/dir\n- **Best Practice:** For large or production images, always use file output and ensure your client is configured to handle file paths.\n\n---\n\n📚 References\n\n- OpenAI Images API Documentation\n\n---\n\n🙏 Credits\n\n- Built with @modelcontextprotocol/sdk\n- Uses openai Node.js SDK \n- Built by SureScale.ai\n- Contributions from Axle Research and Technology", 'readme_raw_len': 3097, 'one_line_zh': '📇 ☁️ - 使用 OpenAI 的 GPT 图像模型进行图像生成与编辑', 'one_line_plain': "Image generation and editing with OpenAI's GPT image models"}, {'id': 3416, 'key': 'just-every-mcp-screenshot-website-fast', 'kind': 'mcp', 'name': 'just-every/mcp-screenshot-website-fast', 'one_line': '📇 🏠 - Capture website screenshots, tiling full pages into 1072x1072 chunks for Claude Vision, with…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Quickly screenshots webpages and converts to an LLM friendly size', 'url': 'https://github.com/just-every/mcp-screenshot-website-fast', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/just-every/mcp-screenshot-website-fast', 'downloads': None, 'stars': 110, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/just-every.png?size=64', 'official_desc': '📇 🏠 - Capture website screenshots, tiling full pages into 1072x1072 chunks for Claude Vision, with configurable viewports and wait strategies for dynamic content.', 'stars_own': 110, 'stars_creator': None, 'stars_own_repo': 'just-every/mcp-screenshot-website-fast', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '@just-every/mcp-screenshot-website-fast\n\nFast, efficient screenshot capture of web pages - optimized for CLI coding tools. Automatically tiles full pages into 1072x1072 chunks for optimal processing.\n\n \n \n \n\n \n \n\nOverview\n\nBuilt specifically for AI vision workflows, this tool captures high-quality screenshots with automatic resolution limiting and tiling for optimal processing by Claude Vision API and other AI models. It ensures screenshots are perfectly sized at 1072x1072 pixels (1.15 megapixels) for maximum compatibility.\n\nFeatures\n\n- 📸 **Fast screenshot capture** using Puppeteer headless browser\n- 🎯 **Claude Vision optimized** with automatic resolution limiting (1072x1072 for optimal 1.15 megapixels)\n- 🔲 **Automatic tiling** - Full pages are automatically split into 1072x1072 tiles\n- 🎬 **Screencast capture** - Record series of screenshots over time with configurable intervals\n- 🔄 **Always fresh content** - No caching ensures up-to-date screenshots\n- 📱 **Configurable viewports** for responsive testing\n- ⏱️ **Wait strategies** for dynamic content (networkidle, custom delays)\n- 📄 **Full page capture** by default for complete page screenshots\n- 🎥 **Animated WebP export** - Save screencasts as high-quality animated WebP files\n- 💉 **JavaScript injection** - Execute custom JS before screencast capture\n- 📦 **Minimal dependencies** for fast npm installs\n- 🔌 **MCP integration** for seamless AI workflows\n- 🪟 **Windows-compatible launcher** for npm-installed MCP usage\n- 🔋 **Resource efficient** - Automatic browser cleanup after 60 seconds of inactivity\n- 🧹 **Memory management** - Pages are closed after each screenshot to prevent leaks\n\nInstallation\n\nClaude Code\n\n \n\nVS Code\n\n \n\nCursor\n\n \n\nJetBrains IDEs\n\nSettings → Tools → AI Assistant → Model Context Protocol (MCP) → Add\n\nChoose "As JSON" and paste:\n\n \n\nRaw JSON (works in any MCP client)\n\n \n\nDrop this into your client\'s mcp.json (e.g. .vscode/mcp.json, ~/.cursor/mcp.json, or .mcp.json for Claude).\n\nPrerequisites\n\n- Node.js 20.x or higher\n- npm or npx\n- Chrome/Chromium (automatically downloaded by Puppeteer)\n\nQuick Start\n\nMCP Server Usage\n\nOnce installed in your IDE, the following tools are available:\n\nAvailable Tools\n\n- take_screenshot - Captures a high-quality screenshot of a webpage\n - Parameters:\n - url (required): The HTTP/HTTPS URL to capture\n - width (optional): Viewport width in pixels (max 1072, default: 1072)\n - height (optional): Viewport height in pixels (max 1072, default: 1072)\n - fullPage (optional): Capture full page screenshot with tiling (default: true)\n - waitUntil (optional): Wait until event: load, domcontentloaded, networkidle0, networkidle2 (default: domcontentloaded)\n - waitFor (optional): Additional wait time in milliseconds\n - directory (optional): Directory to save screenshots - returns file paths instead of base64 images\n\n- capture_selector - Captures a screenshot of a specific DOM element matched by a CSS selector\n - Parameters:\n - url (required): The HTTP/HTTPS URL to capture\n - selector (required): CSS selector for the element to capture\n - width (optional): Viewport width in pixels (max 1072, default: 1072)\n - height (optional): Viewport height in pixels (max 1072, default: 1072)\n - waitUntil (optional): Wait until event: load, domcontentloaded, networkidle0, networkidle2 (default: domcontentloaded)\n - waitForMS (optional): Additional wait time in milliseconds\n - selectorTimeoutMS (optional): How long to wait for the selector to appear before failing (default: 5000)\n\nUsage Examples\n\n**Default usage (returns base64 images):**\n \n\n**Save to directory (returns file paths):**\n \n\n**Capture a specific element:**\n \n\nWhen using the directory parameter:\n- Screenshots are saved as PNG files with timestamps\n- File paths are returned instead of base64 data\n- For tiled screenshots, each tile is saved as a separate file\n- Directory is created automatically if it doesn\'t exist\n\ntake_screencast\n\nCaptures a series of screenshots over time to create a screencast. Only captures the top tile (1072x1072) of the viewport.\n\nParameters\n- url (required): The URL to capture\n- duration (optional): Total duration in seconds (default: 10)\n- interval (optional): Interval between screenshots in seconds (default: 2)\n- jsEvaluate (optional): JavaScript code to execute at the start\n- waitUntil (optional): Wait strategy: \'load\', \'domcontentloaded\', \'networkidle0\', \'networkidle2\'\n- waitForMS (optional): Additional wait time before starting\n- directory (optional): Save as animated WebP to directory (captures every 1 second)\n\nUsage Examples\n\n**Basic screencast (5 frames over 10 seconds):**\n \n\n**Custom timing:**\n \n\n**With JavaScript execution:**\n \n\n**Save as animated WebP:**\n \n\nWhen using the directory parameter:\n- An animated WebP is created with 1-second intervals\n- Individual frames are also saved as PNG files\n- The animation loops forever by default\n- WebP provides excellent quality:\n - Full color support (no 256 color limitation)\n - Efficient compression for web animations\n - Perfect for gradient backgrounds and smooth animations\n - Smaller file sizes compared to GIF with better quality\n\nDevelopment Usage\n\nInstall\n\n \n\nCapture screenshot\n \n\nCLI Options\n\n- -w, --width - Viewport width (max 1072, default: 1072)\n- -h, --height - Viewport height (max 1072, default: 1072)\n- --no-full-page - Disable full page capture and tiling\n- --wait-until - Wait until event: load, domcontentloaded, networkidle0, networkidle2\n- --wait-for - Additional wait time in milliseconds\n- -o, --output - Output file path (required for tiled output)\n\nAuto-Restart Feature\n\nThe MCP server includes automatic restart capability by default for improved reliability:\n\n- Automatically restarts the server if it crashes\n- Handles unhandled exceptions and promise rejections\n- Implements exponential backoff (max 10 attempts in 1 minute)\n- Logs all restart attempts for monitoring\n- Gracefully handles shutdown signals (SIGINT, SIGTERM)\n\nFor development/debugging without auto-restart:\n \n\nArchitecture\n\n \n\nDevelopment\n\n \n\nWhy This Tool?\n\nBuilt specifically for AI vision workflows:\n\n1. **Optimized for Claude Vision API** - Automatic resolution limiting to 1072x1072 pixels (1.15 megapixels)\n2. **Automatic tiling** - Full pages split into perfect chunks for AI processing\n3. **Always fresh** - No caching ensures you get the latest content\n4. **MCP native** - First-class integration with AI development tools\n5. **Simple API** - Clean, straightforward interface for capturing screenshots\n\nContributing\n\nContributions are welcome! Please:\n\n1. Fork the repository\n2. Create a feature branch\n3. Add tests for new functionality\n4. Submit a pull request\n\nTroubleshooting\n\nPuppeteer Issues\n- Ensure Chrome/Chromium can be downloaded\n- Check firewall settings\n- Try setting PUPPETEER_SKIP_CHROMIUM_DOWNLOAD=true and provide custom executable\n\nScreenshot Quality\n- Adjust viewport dimensions\n- Use appropriate wait strategies\n- Check if site requires authentication\n\nTimeout Errors\n- Increase wait time with --wait-for flag\n- Use different --wait-until strategies\n- Check if site is accessible\n\nLicense\n\nMIT', 'readme_raw_len': 7103, 'one_line_zh': '截取网站截图，将整页切成 1072x1072 分块供 Claude Vision 使用', 'one_line_plain': 'Website screenshot tool lets you capture full pages tiled into 1072x1072 chunks for Claude Vision'}, {'id': 4182, 'key': 'awkoy-replicate-flux-mcp', 'kind': 'mcp', 'name': 'awkoy/replicate-flux-mcp', 'one_line': '📇 ☁️ - Generate images via the Replicate API', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP for Replicate Flux Model - A powerful tool for generating customized images and SVG assets that match specific coding vibes and aesthetic styles. Streamline your visual asset creation process with AI-powered design generation tailored for developers.', 'url': 'https://github.com/awkoy/replicate-flux-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/awkoy/replicate-flux-mcp', 'downloads': None, 'stars': 105, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/awkoy.png?size=64', 'official_desc': '— MCP server for Replicate Flux image and SVG generation ☆`105`', 'stars_own': 105, 'stars_creator': None, 'stars_own_repo': 'awkoy/replicate-flux-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Replicate Flux MCP\n\nEnglish | 中文\n\n \n \n \n \n\n \n \n \n \n\n \n \n \n\n**Replicate Flux MCP** is an advanced Model Context Protocol (MCP) server that empowers AI assistants to generate high-quality images and vector graphics. By default it uses black-forest-labs/flux-schnell for raster images and recraft-ai/recraft-v3-svg for SVG output. You can override the curated image/SVG models through environment variables, and image tools also accept a per-call model_id override from the built-in allowlist.\n\n📑 Table of Contents\n\n- Getting Started & Integration\n - Setup Process\n - Cursor Integration\n - Claude Desktop Integration\n - Smithery Integration\n- Glama.ai Integration\n- Codex Integration\n- Features\n- Documentation\n - Available Tools\n - Available Resources\n - Available Prompts\n - Structured Output\n - Environment Variables\n- Development\n - Testing\n- Technical Details\n- Troubleshooting\n- Contributing\n- License\n- Resources\n- Examples\n\n🚀 Getting Started & Integration\n\nSetup Process\n\n1. **Obtain a Replicate API Token**\n - Sign up at Replicate\n - Create an API token in your account settings\n\n2. **Choose Your Integration Method**\n - Follow one of the integration options below based on your preferred MCP client\n\n3. **Ask Your AI Assistant to Generate an Image**\n - Simply ask naturally: "Can you generate an image of a serene mountain landscape at sunset?"\n - Or be more specific: "Please create an image showing a peaceful mountain scene with a lake reflecting the sunset colors in the foreground"\n\n4. **Explore Advanced Features**\n - Try different parameter settings for customized results\n - Experiment with SVG generation using generate_svg\n - Use batch image generation or variant generation features\n\nCursor Integration\n\nMethod 1: Using mcp.json\n\n1. Create or edit the .cursor/mcp.json file in your project directory:\n\n \n\n2. Replace YOUR_TOKEN with your actual Replicate API token\n3. Restart Cursor to apply the changes\n\nMethod 2: Manual Mode\n\n1. Open Cursor and go to Settings\n2. Navigate to the "MCP" or "Model Context Protocol" section\n3. Click "Add Server" or equivalent\n4. Enter the following command in the appropriate field:\n\n \n\n5. Replace YOUR_TOKEN with your actual Replicate API token\n6. Save the settings and restart Cursor if necessary\n\nClaude Desktop Integration\n\n1. Create or edit the mcp.json file in your configuration directory:\n\n \n\n2. Replace YOUR_TOKEN with your actual Replicate API token\n3. Restart Claude Desktop to apply the changes\n\nSmithery Integration\n\nThis MCP server is available as a hosted service on Smithery, allowing you to use it without setting up your own server.\n\n1. Visit Smithery and create an account if you don\'t have one\n2. Navigate to the Replicate Flux MCP server page\n3. Click "Add to Workspace" to add the server to your Smithery workspace\n4. Configure your MCP client (Cursor, Claude Desktop, etc.) to use your Smithery workspace URL\n\nFor more information on using Smithery with your MCP clients, visit the Smithery documentation.\n\nGlama.ai Integration\n\nThis MCP server is also available as a hosted service on Glama.ai, providing another option to use it without local setup.\n\n1. Visit Glama.ai and create an account if you don\'t have one\n2. Go to the Replicate Flux MCP server page\n3. Click "Install Server" to add the server to your workspace\n4. Configure your MCP client to use your Glama.ai workspace\n\nFor more information, visit the Glama.ai MCP servers documentation.\n\nCodex Integration\n\nAdd the server to ~/.codex/config.toml:\n\n \n\nReplace the env values as needed. If you omit REPLICATE_IMAGE_MODEL_ID / REPLICATE_SVG_MODEL_ID, the server uses black-forest-labs/flux-schnell for images and recraft-ai/recraft-v3-svg for SVGs.\n\nCurated tools validate model overrides against built-in allowlists:\n\n- **Image generation**: black-forest-labs/flux-schnell, google/imagen-4, black-forest-labs/flux-kontext-pro, ideogram-ai/ideogram-v3-turbo, black-forest-labs/flux-1.1-pro, black-forest-labs/flux-dev\n- **SVG generation**: recraft-ai/recraft-v3-svg\n- **Legacy run_model extras**: minimax/video-01, luma/reframe-video, topazlabs/video-upscale, topazlabs/image-upscale, szcho/codeformer, tencentarc/gfpgan\n\n🌟 Features\n\n- **🖼️ High-Quality Image Generation** — Flux Schnell raster images by default, with environment-variable and per-tool image model overrides.\n- **🎨 Vector Graphics** — Recraft V3 SVG for logos, icons, and diagrams.\n- **📊 Batch + Variants** — Generate N images from N prompts or N variants of one prompt (seed-based or prompt-modifier-based).\n- **🧩 Arbitrary Replicate Models** — run_replicate_model escape hatch accepts any owner/name[:version] reference, with get_model_schema introspection for the OpenAPI input schema. Optional allowlist via REPLICATE_MODEL_ALLOWLIST.\n- **📦 Structured Output** — Every generate_* tool returns machine-readable structuredContent alongside human-readable content, matching a per-tool outputSchema (URL, prompt, format, aspect ratio, per-variant seed, etc).\n- **⏳ Progress Notifications** — Batch and variant generation emit notifications/progress for clients that opt in via progressToken, so long runs aren\'t black-boxed.\n- **💬 Curated Prompts** — 5 ready-made prompt templates (logo, portrait, svg-icon, product-shot, isometric-diagram) surfaced in Claude Desktop\'s slash palette and Cursor\'s @-menu.\n- **🏷️ Proper Tool Annotations** — readOnlyHint / destructiveHint / openWorldHint / idempotentHint set correctly so clients can reason about safety and cost.\n- **🪵 Structured Logging** — Server-side errors travel over notifications/message instead of stderr.\n- **🔌 Universal MCP Compatibility** — MCP protocol 2025-11-25; works with Claude Desktop, Cursor, Cline, Zed, and any spec-compliant client.\n- **🔍 Generation History** — Browse past runs through imagelist, svglist, and predictionlist resources.\n\n📚 Documentation\n\nAvailable Tools\n\ngenerate_image\n\nGenerates an image based on a text prompt using the configured image model (allowlist only).\n\n \n\ngenerate_multiple_images\n\nGenerates multiple images based on an array of prompts using the configured image model (allowlist only).\n\n \n\ngenerate_image_variants\n\nGenerates multiple variants of the same image from a single prompt using the configured image model (allowlist only).\n\n \n\ngenerate_svg\n\nGenerates SVG/vector output based on a text prompt using the configured SVG model (allowlist only).\n\n \n\nprediction_list\n\nRetrieves a list of your recent predictions from Replicate.\n\n \n\nget_prediction\n\nGets detailed information about a specific prediction.\n\n \n\nrun_model\n\nRuns a whitelisted Replicate model with a raw input payload (useful for video or restoration models).\n\n \n\nrun_replicate_model\n\nRuns any model hosted on Replicate by its owner/name[:version] reference. Use this as an escape hatch when none of the curated tools fit. Call get_model_schema first if you don\'t know the input shape.\n\n \n\nSet the REPLICATE_MODEL_ALLOWLIST env var (comma-separated owner/name entries) to restrict which models can be invoked. Unset = any model allowed. Set-but-empty = deny all (the server fails closed rather than silently allowing everything).\n\nget_model_schema\n\nFetches the OpenAPI input schema and description for a Replicate model so you can pass the right parameters to run_replicate_model.\n\n \n\nAvailable Resources\n\nimagelist\n\nBrowse your history of generated images created with the configured image model.\n\nsvglist\n\nBrowse your history of generated SVG outputs created with the configured SVG model.\n\npredictionlist\n\nBrowse all your Replicate predictions history.\n\nAvailable Prompts\n\nCurated templates surfaced in Claude Desktop\'s slash menu and Cursor\'s @-palette. Each one fills in sensible defaults then delegates to the relevant generation tool.\n\n| Prompt | Description | Arguments |\n| --- | --- | --- |\n| logo | Brand/product logo | brand, style?, palette? |\n| portrait | Photoreal portrait | subject, mood?, lens? |\n| svg-icon | Single-concept vector icon | concept, style? |\n| product-shot | Studio product photography | product, surf', 'readme_raw_len': 12942, 'one_line_zh': '📇 ☁️ - 通过 Replicate API 生成图像', 'one_line_plain': 'Generate images via the Replicate API'}, {'id': 1061, 'key': 'christianhinge-dicom-mcp', 'kind': 'mcp', 'name': 'ChristianHinge/dicom-mcp', 'one_line': '🐍 ☁️ 🏠 - DICOM integration to query, read and move medical images and reports from PACS and other…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Model Context Protocol (MCP) for interacting with dicom servers (PACS etc.)', 'url': 'https://github.com/ChristianHinge/dicom-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ChristianHinge/dicom-mcp', 'downloads': None, 'stars': 103, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ChristianHinge.png?size=64', 'official_desc': '🐍 ☁️ 🏠 - DICOM integration to query, read and move medical images and reports from PACS and other DICOM-compliant systems.', 'stars_own': 103, 'stars_creator': None, 'stars_own_repo': 'ChristianHinge/dicom-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'DICOM MCP Server for Medical Imaging Systems 🏥\n\n \n \n \n\nThe dicom-mcp server enables AI assistants to query, read, and move data on DICOM servers (PACS, VNA, etc.). \n\n \n\n🤝 **Contribute** •\n📝 **Report Bug** •\n📝 **Blog Post 1** \n\n \n\n \n\n✨ Core Capabilities\n\ndicom-mcp provides tools to:\n\n* **🔍 Query Metadata**: Search for patients, studies, series, and instances using various criteria.\n* **📄 Read DICOM Reports (PDF)**: Retrieve DICOM instances containing encapsulated PDFs (e.g., clinical reports) and extract the text content.\n* **➡️ Send DICOM Images**: Send series or studies to other DICOM destinations, e.g. AI endpoints for image segmentation, classification, etc.\n* **⚙️ Utilities**: Manage connections and understand query options.\n\n🚀 Quick Start\n📥 Installation\nInstall using uv or pip:\n\n \nOr by cloning the repository:\n\n \n\n⚙️ Configuration\n\ndicom-mcp requires a YAML configuration file (config.yaml or similar) defining DICOM nodes and calling AE titles. Adapt the configuration or keep as is for compatibility with the sample ORTHANC Server.\n\n \n> [!WARNING]\nDICOM-MCP is not meant for clinical use, and should not be connected with live hospital databases or databases with patient-sensitive data. Doing so could lead to both loss of patient data, and leakage of patient data onto the internet. DICOM-MCP can be used with locally hosted open-weight LLMs for complete data privacy. \n\n(Optional) Sample ORTHANC server\nIf you don\'t have a DICOM server available, you can run a local ORTHANC server using Docker:\n\nClone the repository and install test dependencies pip install -e ".[dev]\n\n \nUI at http://localhost:8042\n\n🔌 MCP Integration\n\nAdd to your client configuration (e.g. claude_desktop_config.json):\n\n \n\nFor development:\n\n \n\n🛠️ Tools Overview\n\ndicom-mcp provides four categories of tools for interaction with DICOM servers and DICOM data. \n\n🔍 Query Metadata\n\n* **query_patients**: Search for patients based on criteria like name, ID, or birth date.\n* **query_studies**: Find studies using patient ID, date, modality, description, accession number, or Study UID.\n* **query_series**: Locate series within a specific study using modality, series number/description, or Series UID.\n* **query_instances**: Find individual instances (images/objects) within a series using instance number or SOP Instance UID\n📄 Read DICOM Reports (PDF)\n\n* **extract_pdf_text_from_dicom**: Retrieve a specific DICOM instance containing an encapsulated PDF and extract its text content.\n\n➡️ Send DICOM Images\n\n* **move_series**: Send a specific DICOM series to another configured DICOM node using C-MOVE.\n* **move_study**: Send an entire DICOM study to another configured DICOM node using C-MOVE.\n\n⚙️ Utilities\n\n* **list_dicom_nodes**: Show the currently active DICOM node and list all configured nodes.\n* **switch_dicom_node**: Change the active DICOM node for subsequent operations.\n* **verify_connection**: Test the DICOM network connection to the currently active node using C-ECHO.\n* **get_attribute_presets**: List the available levels of detail (minimal, standard, extended) for metadata query results. \n\nExample interaction\nThe tools can be chained together to answer complex questions:\n\n \n \n \n\n📈 Contributing\nRunning Tests\n\nTests require a running Orthanc DICOM server. You can use Docker:\n\n \n\nRun tests using pytest:\n\n \n\nStop the Orthanc container:\n\n \n\nDebugging\n\nUse the MCP Inspector for debugging the server communication:\n\n \n\n🙏 Acknowledgments\n\n* Built using pynetdicom\n* Uses PyPDF2 for PDF text extraction', 'readme_raw_len': 3507, 'one_line_zh': '🐍 ☁️ 🏠 - DICOM 集成：查询、读取和移动来自 PACS 等系统的医学影像与报告…', 'one_line_plain': 'DICOM integration lets you query, read, and move medical images and reports from PACS and other systems'}, {'id': 1671, 'key': 'wise-vision-mcp-server-ros-2', 'kind': 'mcp', 'name': 'WiseVision/mcp_server_ros_2', 'one_line': '🐍 - ROS 2 integration for AI-driven robotics applications and services', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/wise-vision/ros2_mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/wise-vision/mcp_server_ros_2', 'downloads': None, 'stars': 93, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/wise-vision.png?size=64', 'official_desc': '🐍 - ROS 2 integration for AI-driven robotics applications and services.', 'stars_own': 93, 'stars_creator': None, 'stars_own_repo': 'wise-vision/ros2_mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'ROS2 MCP\n\n \n \n \n \n \n \n \n \n \n\n \n\n \n\n**ROS2 MCP** is an open-source (MPL-2.0) **Model Context Protocol** server that lets AI agents (Claude, Cursor, Codex and any other MCP client) list, subscribe to, publish on and call ROS 2 topics, services and actions on a live robot. It talks to ROS 2 directly through rclpy: no rosbridge, no changes to your robots. Transports: **stdio** (default), **Streamable HTTP** and SSE (deprecated by the MCP spec).\n\nEvery tool is free and open source. Commercial support is available if you want help putting it on a real machine.\n\n⚡ Quickstart (30 seconds, Docker)\n\nYou need Docker and an MCP client. ROS 2 itself does not have to be installed: the image contains it.\n\n \n\nAny other MCP client: run docker run -i --rm -e ROS2_MCP_READONLY=1 wisevision/ros2_mcp:jazzy as its stdio command. Then ask: *"List the ROS 2 topics"* and *"Read three messages from /chatter"*.\n\nNo robot at hand? Start a demo publisher first:\n\n \n\nPrefer uv? On a machine with ROS 2 installed and sourced (the server needs rclpy; use the Python that matches your distro, e.g. 3.12 on Jazzy):\n\n \n\nRemove -e ROS2_MCP_READONLY=1 (or the --read-only flag) only when you want the agent to be able to move things; see Security. Client-specific setup (VS Code Copilot, Claude Desktop, Warp, Codex CLI, custom messages): installation guide. Docs: wisevision.tech/docs.\n\n> mcp/ros2 in Docker\'s official MCP catalog (13,750 pulls at the time of writing) is built by Docker and lags our releases: it was last updated on 2026-02-04 and predates read-only mode. wisevision/ros2_mcp is built and pushed by this repository\'s CI and is the one the commands above use.\n\n🧰 Tools\n\n24 tools. ⚠ marks tools that can change robot state; read-only mode removes them. The complete, generated reference with input schemas is docs/generated/tools.md (a CI check keeps it in sync with the code).\n\n| Area | Tools |\n|---|---|\n| **Discover** | ros2_topic_list, ros2_service_list, ros2_list_actions, ros2_interface_list, ros2_get_message_fields (field names and types of a message type) |\n| **Topics** | ros2_topic_subscribe, ros2_subscribe_multiple_topics (images come back as PNG), ⚠ ros2_topic_publish, ⚠ ros2_publish_multiple_topics (set frequency and duration) |\n| **Services** | ⚠ ros2_service_call |\n| **Actions** | ⚠ ros2_send_action_goal (optionally waits for the result), ros2_action_request_result, ros2_action_subscribe_feedback, ros2_action_subscribe_status, ⚠ ros2_cancel_action_goal |\n| **Perception** | ros2_get_map_as_image (nav_msgs/OccupancyGrid to PNG), ros2_get_pointcloud_as_bev (sensor_msgs/PointCloud2 to bird\'s-eye-view PNG) |\n| **History** | ros2_get_messages_stored_in_influx_data_base (past messages from the WiseVision Data Black Box, an InfluxDB-based alternative to Rosbag2) |\n| **Viewer app** | ros2_viewer_app, ros2_viewer_config, ros2_stream_start, ros2_stream_next, ros2_stream_next_image, ros2_stream_stop (interactive image / point cloud / plot viewer for MCP clients that support Apps; the stream tools also serve clients that cannot render it) |\n\nAlso included: four built-in prompts, automatic QoS selection for topics, nested message fields, and custom message types (source your package before starting; see the installation guide).\n\n🧭 Supported ROS 2 distributions\n\n| Distro | Status |\n|---|---|\n| **Jazzy Jalisco** (Ubuntu 24.04, Python 3.12) | Supported. Tests run in CI on ros:jazzy-ros-base; Docker image wisevision/ros2_mcp:jazzy. |\n| **Humble Hawksbill** (Ubuntu 22.04, Python 3.10) | Supported. Tests run in CI on ros:humble-ros-base; Docker image wisevision/ros2_mcp:humble. |\n| Iron, Kilted, Rolling | Not tested and not shipped. It may work from source; we make no promise. Open an issue if you need one. |\n\nROS 1 is not supported (the server uses rclpy). Python 3.10 or newer.\n\n🆚 How it compares\n\nOther open-source ROS MCP servers exist. They make different trade-offs; pick by what you need. Facts below were read from each project\'s README, pyproject.toml and GitHub page on 2026-10-14.\n\n| | **ROS2 MCP** (this repo) | robotmcp/ros-mcp-server | LCAS/ros2_mcp |\n|---|---|---|---|\n| How it reaches ROS | rclpy, directly on the ROS 2 graph (runs where ROS 2 is sourced, or in the Docker image) | rosbridge WebSocket; the MCP server can run on a different machine from the robot | ROS 2 package built in your colcon workspace |\n| ROS versions | ROS 2 Humble, Jazzy | ROS 1 and ROS 2 | ROS 2 Humble, Iron, Jazzy (per its README) |\n| Install | Docker image wisevision/ros2_mcp, or uvx/uv from git | uvx ros-mcp (on PyPI) | git clone, rosdep, colcon build; Docker Compose with an SSE endpoint |\n| Transports | stdio, Streamable HTTP, SSE | stdio, http, streamable-http (--transport) | SSE endpoint documented |\n| Read-only mode | Yes: mutating tools are not registered, and a test enforces the classification | No such switch found in its code; tools carry the MCP readOnlyHint annotation | Its README lists observation features only |\n| Publish / services / actions | Yes, including goal, result, feedback, status and cancel | Yes (topics, services and actions tools) | Not listed in its README |\n| Nodes and parameters | **No** dedicated tools yet | Yes (node and parameter tools) | Node listing |\n| Images and maps | Image topics as PNG, occupancy-grid map, point-cloud bird\'s-eye view | Image tool | Image retrieval for VLM analysis |\n| License | MPL-2.0 | Apache-2.0 | Apache-2.0 |\n| GitHub stars | 90 | 1,493 | 9 |\n\nWhen to pick another one: you need **ROS 1**, node or parameter tools, or you want the MCP server **off the robot** with only rosbridge exposed there: use robotmcp/ros-mcp-server. We do not claim to be better on every axis. What we offer: native rclpy access (no bridge process between the agent and DDS), a read-only mode that fails closed (a tool is registered in that mode only if it was explicitly reviewed as read-only, and a test fails when a new tool is unclassified), images, maps and point clouds returned as PNGs, the complete action lifecycle (goal, result, feedback, status, cancel), and commercial support if you need it. Something here is wrong or out of date? Open an issue and we will fix it.\n\n🗺️ Roadmap (proposed, not committed)\n\nWhat users ask for that is **not** in ROS2 MCP today. No dates; open an issue or a Discussion to vote or to pay for one.\n\n- **Parameter tools**: get / set / list node parameters.\n- **TF tools**: transform lookup and frame tree.\n- **Authentication for Streamable HTTP** (bearer token or a documented reverse-proxy recipe). The transport itself is in #55; until auth exists, keep it on loopback or a private network.\n- **Node tools** (list nodes, node info), Nav2-specific helpers, a PyPI package, ROS 1 (not planned).\n\n🤖 For AI agents\n\nIf you are an agent (or building one) using this server:\n\n- **Discover before you act.** Call ros2_topic_list, ros2_service_list, ros2_list_actions and ros2_interface_list, then ros2_get_message_fields for the exact message type, before any publish, service call or action goal. Do not guess field names.\n- **Prefer read-only.** If ros2_topic_publish, ros2_service_call, ros2_send_action_goal are absent from list_tools, the operator started the server in read-only mode; do not try to work around it.\n- **Mutating tools move real machines.** Confirm intent with the human before calling a ⚠ tool, start with small values and a short duration, and keep ros2_cancel_action_goal in mind.\n- **Right after container start the graph may be incomplete** (DDS discovery). If a topic you expect is missing, retry once before concluding it does not exist; see DDS discovery warm-up.\n- **Machine-readable references:** docs/generated/tools.json (every tool and its input schema), server.json (MCP registry manifest), AGENTS.md (repo layout, how to build, test and add a tool), wisevision.tech/llms.txt.\n\n🏢 Commercial support & integration\n\nROS2 MCP is free software under the **MPL-2.0**: you can use it in a commercial product without asking us. Nothing in this section changes that.', 'readme_raw_len': 14949, 'one_line_zh': '面向 AI 驱动机器人应用与服务的 ROS 2 集成', 'one_line_plain': 'ROS 2 integration for AI-driven robotics applications and services'}, {'id': 4307, 'key': 'tan-yong-sheng-ai-vision-mcp', 'kind': 'mcp', 'name': 'tan-yong-sheng/ai-vision-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Image, video and object-detection analysis with Google Gemini and Vertex AI for UI/UX evaluation,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol (MCP) server that provides vision capabilities to analyze image and video', 'url': 'https://github.com/tan-yong-sheng/ai-vision-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/tan-yong-sheng/ai-vision-mcp', 'downloads': None, 'stars': 80, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/tan-yong-sheng.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Image, video and object-detection analysis with Google Gemini and Vertex AI for UI/UX evaluation, visual regression testing and interface understanding.', 'stars_own': 80, 'stars_creator': None, 'stars_own_repo': 'tan-yong-sheng/ai-vision-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "AI Vision MCP Server\n\nA powerful Model Context Protocol (MCP) server that provides AI-powered image and video analysis using Google Gemini and Vertex AI models.\n\nFeatures\n\n- **Dual Provider Support**: Choose between Google Gemini API and Vertex AI\n- **Multimodal Analysis**: Support for both image and video content analysis\n- **Flexible File Handling**: Upload via multiple methods (URLs, local files, base64)\n- **Storage Integration**: Built-in Google Cloud Storage support\n- **Comprehensive Validation**: Zod-based data validation throughout\n- **Error Handling**: Robust error handling with retry logic and circuit breakers\n- **TypeScript**: Full TypeScript support with strict type checking\n\nQuick Start\n\nPre-requisites\n\nYou could choose either to use google provider or vertex_ai provider. For simplicity, google provider is recommended.\n\nBelow are the environment variables you need to set based on your selected provider. (Note: It’s recommended to set the timeout configuration to more than 5 minutes for your MCP client).\n\n(i) **Using Google AI Studio Provider**\n\n \n\nGet your Google AI Studio's api key here\n\n(ii) **Using Vertex AI Provider**\n\n \n\nRefer to the guideline here on how to set this up.\n\nInstallation\n\nBelow are the installation guide for this MCP on different MCP clients, such as Claude Desktop, Claude Code, Cursor, Cline, etc.\n\n \n Claude Desktop \n\nAdd to your Claude Desktop configuration:\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n\n \n\n \n Claude Code \n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n\nNote: Increase the MCP startup timeout to 1 minutes and MCP tool execution timeout to about 5 minutes by updating ~\\.claude\\settings.json as follows:\n\n \n\n \n\n \n Cursor \n\nGo to: Settings -> Cursor Settings -> MCP -> Add new global MCP server\n\nPasting the following configuration into your Cursor ~/.cursor/mcp.json file is the recommended approach. You may also install in a specific project by creating .cursor/mcp.json in your project folder. See Cursor MCP docs for more info.\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n \n\n \n Cline \n\nCline uses a JSON configuration file to manage MCP servers. To integrate the provided MCP server configuration:\n\n1. Open Cline and click on the MCP Servers icon in the top navigation bar.\n2. Select the Installed tab, then click Advanced MCP Settings.\n3. In the cline_mcp_settings.json file, add the following configuration:\n\n(i) Using Google AI Studio Provider\n \n\n(ii) Using Vertex AI Provider\n \n \n\n \n\n Other MCP clients \n\nThe server uses stdio transport and follows the standard MCP protocol. It can be integrated with any MCP-compatible client by running:\n\n \n \n\nMCP Tools\n\nThe server provides four main MCP tools:\n\n1) analyze_image\n\nAnalyzes an image using AI and returns a detailed description.\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the image\n- prompt (string): Question or instruction for the AI\n- mode (string, optional): Analysis mode - one of:\n - general (default) - General image analysis\n - palette - Extract design tokens (colors, spacing, typography)\n - hierarchy - Analyze visual hierarchy and eye flow\n - components - Catalog UI components and design system maturity\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Examples:**\n\n1. **General image analysis:**\n \n\n2. **Extract design tokens:**\n \n\n3. **Analyze visual hierarchy:**\n \n\n4. **Component inventory:**\n \n\n2) compare_images\n\nCompares multiple images using AI and returns a detailed comparison analysis.\n\n**Parameters:**\n- imageSources (array): Array of image sources (URLs, base64 data, or file paths) - minimum 2, maximum 4 images\n- prompt (string): Question or instruction for comparing the images\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Examples:**\n\n1. **Compare images from URLs:**\n \n\n2. **Compare mixed sources:**\n \n\n3) detect_objects_in_image\n\nDetects objects in an image using AI vision models and generates annotated images with bounding boxes. Returns detected objects with coordinates and either saves the annotated image to a file or temporary directory.\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the image\n- prompt (string): Custom detection prompt describing what to detect or recognize in the image\n- outputFilePath (string, optional): Explicit output path for the annotated image\n\n**Configuration:**\nThis function uses optimized default parameters for object detection and does not accept runtime options parameter. To customize the AI parameters (temperature, topP, topK, maxTokens), use environment variables:\n\n \n\n**File Handling Logic:**\n1. **Explicit outputFilePath provided** → Saves to the exact path specified\n2. **If not explicit outputFilePath** → Automatically saves to temporary directory\n\n**Response Types:**\n- Returns file object when explicit outputFilePath is provided\n- Returns tempFile object when explicit outputFilePath is not provided so the image file output is auto-saved to temporary folder\n- Always includes detections array with detected objects and coordinates\n- Includes summary with percentage-based coordinates for browser automation\n\n**Examples:**\n\n1. **Basic object detection:**\n \n\n2. **Save annotated image to specific path:**\n \n\n3. **Custom detection prompt:**\n \n\n4) audit_design\n\nAudits UI/UX design compliance with pixel-level analysis and AI critique.\n\nThis tool provides automated design compliance auditing using pure TypeScript/JavaScript pixel analysis combined with Gemini Vision API critique. It extracts dominant colors, detects visual complexity, validates WCAG contrast ratios, and generates actionable design recommendations.\n\n**Inspired by:** Automating UX/UI Design Analysis with Python, Machine Learning, and LLMs by Jade Graham\n\n**Parameters:**\n- imageSource (string): URL, base64 data, or file path to the design image\n- prompt (string, optional): Custom audit context or focus areas\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Features:**\n- **Dominant Colors**: K-means clustering to extract 5 primary colors\n- **Edge Complexity**: Sobel operator for visual structure analysis\n- **WCAG Contrast**: W3C relative luminance formula validation (AA/AAA)\n- **Luminance Stats**: Mean brightness and standard deviation calculations\n- **Design Issues**: Automated detection of contrast, complexity, and brightness problems\n- **AI Critique**: Gemini-powered recommendations for design improvements\n\n**Examples:**\n\n1. **Basic design audit:**\n \n\n2. **Audit local design file:**\n \n\n5) analyze_video\n\nAnalyzes a video using AI and returns a detailed description.\n\n**Parameters:**\n- videoSource (string): YouTube URL, GCS URI, or local file path to the video\n- prompt (string): Question or instruction for the AI\n- options (object, optional): Analysis options including temperature and max tokens\n\n**Supported video sources:**\n- YouTube URLs (e.g., https://www.youtube.com/watch?v=...)\n- Local file paths (e.g., C:\\Users\\username\\Downloads\\video.mp4)\n\n**Examples:**\n\n1. **Analyze video from YouTube URL:**\n \n\n2. **Analyze local video file:**\n \n\n**Note:** Only YouTube URLs are supported for public video URLs. Other public video URLs are not currently supported.\n\nEnvironment Configuration\n\nFor basic setup, you only need to configure the provider selection and required credentials:\n\nGoogle AI Studio Provider (Recommended)\n \n\nVertex AI Provider (Production)\n \n\n📖 **Detailed Configuration Guide**\n\nFor comprehensive environment variable documentation, including:\n- Complete configuration reference (60+ environment variables)\n- Function-specific optimization examples\n- Advanced configuration patterns\n- Troubleshooting guidance\n\n👉 **See Environment Variable Guide**\n\nConfiguration Priority Overview\n\nThe server uses a hierarchical configuration system where more specific settings override general ones:\n\n1. **LLM-assigned values** (ru", 'readme_raw_len': 10342, 'one_line_zh': '使用 Google Gemini 和 Vertex AI 进行图像、视频和物体检测分析，用于 UI/UX 评估', 'one_line_plain': 'Analyse images, video and objects with Google Gemini and Vertex AI for UI/UX evaluation'}, {'id': 213, 'key': 'hamflx-imagen3-mcp', 'kind': 'mcp', 'name': 'hamflx/imagen3-mcp', 'one_line': "📇 🏠 🪟 🍎 🐧 - Generate images from text prompts with Google's Imagen 3 API, with photography, artistic and…", 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/hamflx/imagen3-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/hamflx/imagen3-mcp', 'downloads': None, 'stars': 76, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/hamflx.png?size=64', 'official_desc': "📇 🏠 🪟 🍎 🐧 - Generate images from text prompts with Google's Imagen 3 API, with photography, artistic and photorealistic controls.", 'stars_own': 76, 'stars_creator': None, 'stars_own_repo': 'hamflx/imagen3-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': "Imagen3-MCP\n\nEnglish Version\n\n基于 Google 的 Imagen 3.0 的图像生成工具，通过 MCP（Model Control Protocol）提供服务。\n\n效果\n\n画一只奔跑的杰克罗素犬，长焦镜头，阳光透过狗狗的毛发，照片级画质\n\n \n\n画一个科技感十足的苹果\n\n \n\n安装要求\n\n- 有效的 Google Gemini API 密钥\n\n安装步骤——Cherry Studio\n\n1. 从 GitHub Releases 下载最新版本的可执行文件\n2. 将下载的可执行文件放置在系统中的任意位置，例如 C:\\bin\\imagen3-mcp.exe\n3. 在 Cherry Studio 中配置：\n - Command 字段填写可执行文件路径，例如 C:\\bin\\imagen3-mcp.exe\n - 环境变量 GEMINI_API_KEY 中填写你的 Gemini API 密钥\n - [可选] 环境变量 BASE_URL 中填写代理地址，例如 https://lingxi-proxy.hamflx.dev/api/provider/google（这个地址可以解决 GFW 的问题，但是解决不了 Google 对 IP 的限制问题，因此还是得挂梯子）。\n - [可选] 环境变量 SERVER_LISTEN_ADDR：设置服务器监听的 IP 地址（默认为 127.0.0.1）。\n - [可选] 环境变量 SERVER_PORT：设置服务器监听的端口和图片 URL 使用的端口（默认为 9981）。\n - [可选] 环境变量 IMAGE_RESOURCE_SERVER_ADDR：设置图片 URL 中使用的服务器地址（默认为 127.0.0.1）。这在服务器运行在容器或远程机器上时很有用。\n\n \n\n安装步骤——Cursor\n\n \n\n许可证\n\nMIT\n\n---\n\nImagen3-MCP (English)\n\nAn image generation tool based on Google's Imagen 3.0, providing services through MCP (Model Control Protocol).\n\nExamples\n\nA running Jack Russell Terrier, telephoto lens, sunlight filtering through the dog's fur, photorealistic quality\n\n \n\nA high-tech apple\n\n \n\nRequirements\n\n- Valid Google Gemini API key\n\nInstallation Steps—Cherry Studio\n\n1. Download the latest executable from GitHub Releases\n2. Place the downloaded executable anywhere in your system, e.g., C:\\bin\\imagen3-mcp.exe\n3. Configure in Cherry Studio:\n - Fill in the Command field with the executable path, e.g., C:\\bin\\imagen3-mcp.exe\n - Enter your Gemini API key in the GEMINI_API_KEY environment variable\n - [Optional] Enter a proxy URL in the BASE_URL environment variable, e.g., https://your-proxy.com.\n - [Optional] Set the SERVER_LISTEN_ADDR environment variable: The IP address the server listens on (defaults to 127.0.0.1).\n - [Optional] Set the SERVER_PORT environment variable: The port the server listens on and uses for image URLs (defaults to 9981).\n - [Optional] Set the IMAGE_RESOURCE_SERVER_ADDR environment variable: The server address used in the image URLs (defaults to 127.0.0.1). Useful if the server runs in a container or remote machine.\n\n \n\nInstallation Steps—Cursor\n\n \n\nLicense\n\nMIT", 'readme_raw_len': 2098, 'one_line_zh': '📇 🏠 🪟 🍎 🐧 - 通过 Google Imagen 3 API 依据文本 Prompt 生成图像，支持摄影、艺术等风格…', 'one_line_plain': "Generate images from text prompts with Google's Imagen 3 API"}, {'id': 3976, 'key': 'pab1it0-tripadvisor-mcp', 'kind': 'mcp', 'name': 'pab1it0/tripadvisor-mcp', 'one_line': '📇 🐍 - Tripadvisor API access: location data, reviews and photos', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol (MCP) server for Tripadvisor Content API. This provides access to Tripadvisor location data, reviews, and photos through standardized MCP interfaces, allowing AI assistants to search for travel destinations and experiences.', 'url': 'https://github.com/pab1it0/tripadvisor-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/pab1it0/tripadvisor-mcp', 'downloads': None, 'stars': 64, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/pab1it0.png?size=64', 'official_desc': '📇 🐍 - Tripadvisor API access: location data, reviews and photos.', 'stars_own': 64, 'stars_creator': None, 'stars_own_repo': 'pab1it0/tripadvisor-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Tripadvisor MCP Server\n\nA [Model Context Protocol][mcp] (MCP) server for Tripadvisor Content API.\n\nThis provides access to Tripadvisor location data, reviews, and photos through standardized MCP interfaces, allowing AI assistants to search for travel destinations and experiences.\n\n[mcp]: https://modelcontextprotocol.io\n\nFeatures\n\n- [x] Search for locations (hotels, restaurants, attractions) on Tripadvisor\n- [x] Get detailed information about specific locations\n- [x] Retrieve reviews and photos for locations\n- [x] Search for nearby locations based on coordinates\n- [x] API Key authentication\n- [x] Docker containerization support\n\n- [x] Provide interactive tools for AI assistants\n\nThe list of tools is configurable, so you can choose which tools you want to make available to the MCP client.\n\nUsage\n\n1. Get your Tripadvisor Content API key from the Tripadvisor Developer Portal.\n\n2. Configure the environment variables for your Tripadvisor Content API, either through a .env file or system environment variables:\n\n \n\n3. Add the server configuration to your client configuration file. For example, for Claude Desktop:\n\n \n\n> Note: if you see Error: spawn uv ENOENT in Claude Desktop, you may need to specify the full path to uv or set the environment variable NO_UV=1 in the configuration.\n\nDocker Usage\n\nThis project includes Docker support for easy deployment and isolation.\n\nBuilding the Docker Image\n\nBuild the Docker image using:\n\n \n\nRunning with Docker\n\nYou can run the server using Docker in several ways:\n\nUsing docker run directly:\n\n \n\nUsing docker-compose:\n\nCreate a .env file with your Tripadvisor API key and then run:\n\n \n\nRunning with Docker in Claude Desktop\n\nTo use the containerized server with Claude Desktop, update the configuration to use Docker with the environment variables:\n\n \n\nThis configuration passes the environment variables from Claude Desktop to the Docker container by using the -e flag with just the variable name, and providing the actual values in the env object.\n\nDevelopment\n\nContributions are welcome! Please open an issue or submit a pull request if you have any suggestions or improvements.\n\nThis project uses uv to manage dependencies. Install uv following the instructions for your platform:\n\n \n\nYou can then create a virtual environment and install the dependencies with:\n\n \n\nProject Structure\n\nThe project has been organized with a src directory structure:\n\n \n\nTesting\n\nThe project includes a test suite that ensures functionality and helps prevent regressions.\n\nRun the tests with pytest:\n\n \n\nTools\n\n| Tool | Category | Description |\n| --- | --- | --- |\n| search_locations | Search | Search for locations by query text, category, and other filters |\n| search_nearby_locations | Search | Find locations near specific coordinates |\n| get_location_details | Retrieval | Get detailed information about a location |\n| get_location_reviews | Retrieval | Retrieve reviews for a location |\n| get_location_photos | Retrieval | Get photos for a location |\n\nLicense\n\nMIT\n\n---\n\n[mcp]: https://modelcontextprotocol.io', 'readme_raw_len': 3053, 'one_line_zh': '提供 Tripadvisor API 访问：获取地点数据、评论和照片', 'one_line_plain': 'Get Tripadvisor location data, reviews and photos'}, {'id': 371, 'key': 'raveenb-fal-mcp-server', 'kind': 'mcp', 'name': 'raveenb/fal-mcp-server', 'one_line': '🐍 ☁️ - Generate images, videos and music with Fal.ai models such as FLUX, Stable Diffusion and MusicGen', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/luminarylane/fal-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/raveenb/fal-mcp-server', 'downloads': None, 'stars': 61, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/raveenb.png?size=64', 'official_desc': 'Fal.ai MCP Server A Model Context Protocol (MCP) server that enables Claude Desktop (and other MCP clients) to generate images, videos, music, and audio using Fal.ai models.', 'stars_own': 61, 'stars_creator': None, 'stars_own_repo': 'luminarylane/fal-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': '🎨 Fal.ai MCP Server\n\n \n \n \n \n \n \n \n \n\nA Model Context Protocol (MCP) server that enables Claude Desktop (and other MCP clients) to generate images, videos, music, and audio using Fal.ai models.\n\n \n \n \n\n✨ Features\n\n🚀 Performance\n- **Native Async API** - Uses fal_client.run_async() for optimal performance\n- **Queue Support** - Long-running tasks (video/music) use queue API with progress updates\n- **Non-blocking** - All operations are truly asynchronous\n\n🌐 Transport Modes (New!)\n- **STDIO** - Traditional Model Context Protocol communication\n- **HTTP/SSE** - Web-based access via Server-Sent Events\n- **Dual Mode** - Run both transports simultaneously\n\n🎨 Media Generation (18 Tools)\n\n**Image Generation:**\n- 🖼️ **generate_image** - Create images from text prompts (Flux, SDXL, etc.)\n- 🎯 **generate_image_structured** - Fine-grained control over composition, lighting, subjects\n- 🔄 **generate_image_from_image** - Transform existing images with style transfer\n\n**Image Editing:**\n- ✂️ **remove_background** - Remove backgrounds from images (transparent PNG)\n- 🔍 **upscale_image** - Upscale images 2x or 4x while preserving quality\n- ✏️ **edit_image** - Edit images using natural language instructions\n- 🎭 **inpaint_image** - Edit specific regions using masks\n- 📐 **resize_image** - Smart resize for social media (Instagram, YouTube, TikTok, etc.)\n- 🏷️ **compose_images** - Overlay images (watermarks, logos) with precise positioning\n\n**Video Tools:**\n- 🎬 **generate_video** - Text-to-video and image-to-video generation\n- 📹 **generate_video_from_image** - Animate images into videos\n- 🔀 **generate_video_from_video** - Video restyling and motion transfer\n\n**Audio Tools:**\n- 🎵 **generate_music** - Create instrumental music or songs with vocals\n\n**Utility Tools:**\n- 🔍 **list_models** - Discover 600+ available models with smart filtering\n- 💡 **recommend_model** - AI-powered model recommendations for your task\n- 💰 **get_pricing** - Check costs before generating content\n- 📊 **get_usage** - View spending history and usage stats\n- ⬆️ **upload_file** - Upload local files for use with generation tools\n\n🔍 Dynamic Model Discovery (New!)\n- **600+ Models** - Access all models available on Fal.ai platform\n- **Auto-Discovery** - Models are fetched dynamically from the Fal.ai API\n- **Smart Caching** - TTL-based cache for optimal performance\n- **Flexible Input** - Use full model IDs or friendly aliases\n\n🚀 Quick Start\n\nPrerequisites\n\n- Python 3.10 or higher\n- Fal.ai API key (free tier available)\n- Claude Desktop (or any MCP-compatible client)\n\nInstallation\n\nOption 0: Claude Code Plugin (Simplest for Claude Code Users) 🔌\n\nIf you\'re using Claude Code, install directly via the plugin system:\n\n \n\nOr install directly without adding the marketplace:\n \n\n> **Note:** You\'ll need to set FAL_KEY in your environment before using the plugin.\n\nOption 1: uvx (Recommended - Zero Install) ⚡\n\nRun directly without installation using uv:\n\n \n\n**Claude Desktop Configuration for uvx:**\n \n\n> **Note:** Install uv first: curl -LsSf https://astral.sh/uv/install.sh | sh\n\nOption 2: Docker (Recommended for Production) 🐳\n\nOfficial Docker image available on GitHub Container Registry.\n\n**Step 1: Start the Docker container**\n\n \n\n**Step 2: Configure Claude Desktop to connect**\n\nAdd to your Claude Desktop config file:\n- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n- Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n\n \n\n> **Note:** This uses mcp-remote to connect to the HTTP/SSE endpoint. Alternatively, if you have curl available: "command": "curl", "args": ["-N", "http://localhost:8080/sse"]\n\n**Step 3: Restart Claude Desktop**\n\nThe fal-ai tools should now be available.\n\n**Docker Environment Variables:**\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| FAL_KEY | (required) | Your Fal.ai API key |\n| FAL_MCP_TRANSPORT | http | Transport mode: http, stdio, or dual |\n| FAL_MCP_HOST | 0.0.0.0 | Host to bind the server to |\n| FAL_MCP_PORT | 8080 | Port for the HTTP server |\n\n**Using Docker Compose:**\n\n \n\n**⚠️ File Upload with Docker:**\n\nThe upload_file tool requires volume mounts to access host files:\n\n \n\nThen use container paths like /downloads/image.png instead of host paths.\n\n| Feature | stdio (uvx) | Docker (HTTP/SSE) |\n|---------|:-----------:|:-----------------:|\n| upload_file | ✅ Full filesystem | ⚠️ Needs volume mounts |\n| Security | Runs as user | Sandboxed container |\n\nOption 3: Install from PyPI\n\n \n\nOr with uv:\n \n\nOption 4: Install from source\n\n \n\nConfiguration\n\n1. Get your Fal.ai API key from fal.ai\n\n2. Configure Claude Desktop by adding to:\n - macOS: ~/Library/Application Support/Claude/claude_desktop_config.json\n - Windows: %APPDATA%\\Claude\\claude_desktop_config.json\n\nFor PyPI/pip Installation:\n \n\n> **Note:** For Docker configuration, see Option 2: Docker above.\n\nFor Source Installation:\n \n\n3. Restart Claude Desktop\n\n💬 Usage\n\nWith Claude Desktop\n\nOnce configured, ask Claude to:\n\n- "Generate an image of a sunset"\n- "Create a video from this image"\n- "Generate 30 seconds of ambient music"\n- "Convert this text to speech"\n- "Transcribe this audio file"\n\nDiscovering Available Models\n\nUse the list_models tool to discover available models:\n\n- "What image models are available?"\n- "List video generation models"\n- "Search for flux models"\n\nUsing Any Fal.ai Model\n\nYou can use any model from the Fal.ai platform:\n\n \n\nHTTP/SSE Transport (New!)\n\nRun the server with HTTP transport for web-based access:\n\n \n\nConnect from web clients via Server-Sent Events:\n- SSE endpoint: http://localhost:8080/sse (Docker) or http://localhost:8000/sse (pip)\n- Message endpoint: POST http://localhost:8080/messages/\n\nSee Docker Documentation and HTTP Transport Documentation for details.\n\n📦 Supported Models\n\nThis server supports **600+ models** from the Fal.ai platform through dynamic discovery. Use the list_models tool to explore available models, or use any model ID directly.\n\nPopular Aliases (Quick Reference)\n\nThese friendly aliases are always available for commonly used models:\n\n| Alias | Model ID | Type |\n|-------|----------|------|\n| flux_schnell | fal-ai/flux/schnell | Image |\n| flux_dev | fal-ai/flux/dev | Image |\n| flux_pro | fal-ai/flux-pro | Image |\n| sdxl | fal-ai/fast-sdxl | Image |\n| stable_diffusion | fal-ai/stable-diffusion-v3-medium | Image |\n| svd | fal-ai/stable-video-diffusion | Video |\n| animatediff | fal-ai/fast-animatediff | Video |\n| kling | fal-ai/kling-video | Video |\n| musicgen | fal-ai/musicgen-medium | Audio |\n| musicgen_large | fal-ai/musicgen-large | Audio |\n| bark | fal-ai/bark | Audio |\n| whisper | fal-ai/whisper | Audio |\n\nUsing Full Model IDs\n\nYou can also use any model directly by its full ID:\n\n \n\nUse list_models with category filters to discover more:\n- list_models(category="image") - All image generation models\n- list_models(category="video") - All video generation models\n- list_models(category="audio") - All audio models\n- list_models(search="flux") - Search for specific models\n\n📚 Documentation\n\n| Guide | Description |\n|-------|-------------|\n| Installation Guide | Detailed setup instructions for all platforms |\n| API Reference | Complete tool documentation with parameters |\n| Examples | Usage examples for image, video, and audio generation |\n| Docker Guide | Container deployment and configuration |\n| HTTP Transport | Web-based SSE transport setup |\n| Local Testing | Running CI locally with act |\n\n📖 **Full documentation site**: raveenb.github.io/fal-mcp-server\n\n🔌 Claude Code Plugin Marketplace\n\nThis project is part of the **Luminary Lane Tools** marketplace for Claude Code plugins.\n\n**Add the marketplace:**\n \n\n**Available plugins:**\n| Plugin | Description |\n|--------|-------------|\n| fal-ai | Generate images, videos, and music using 600+ Fal.ai models |\n\n*More plugins coming soon!*\n\n🔧 Troubleshooting\n\nCommon Errors\n\nFAL_KEY not set\n \n**Solution:** Set your Fal.ai API key:\n \n\nModel not found\n \n**Solution:** Use list_models to discover available models, or check the ', 'readme_raw_len': 9308, 'one_line_zh': '使用 FLUX、Stable Diffusion、MusicGen 等 Fal.ai 模型生成图像、视频和音乐', 'one_line_plain': 'Fal.ai models such as FLUX, Stable Diffusion and MusicGen let users generate images, videos and music'}]/2026-10-20 https://anclum.com/zh/[{'id': 88, 'key': 'code-yeongyu-oh-my-openagent', 'kind': 'repo', 'name': 'code-yeongyu/oh-my-openagent', 'one_line': 'OmO: Just type "mass ulw" keyword with your prompt', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'OmO: Just type "mass ulw" keyword with your prompt. Now you are the master of graph engineering.', 'url': 'https://github.com/code-yeongyu/oh-my-openagent', 'source': 'github', 'source_url': 'https://github.com/code-yeongyu/oh-my-openagent', 'downloads': None, 'stars': 70200, 'installs': None, 'mentions': 2, 'score': 342.84, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 31, 'icon': 'https://github.com/code-yeongyu.png?size=64', 'official_desc': 'OmO: Just type "mass ulw" keyword with your prompt. Now you are the master of graph engineering.', 'stars_own': 70200, 'stars_creator': None, 'stars_own_repo': 'code-yeongyu/oh-my-openagent', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npm i -g omo-ai', 'install_src': 'HEAD/README.md', 'install_conf': 'low', 'desc_src': 'github-api', 'readme_body': '> [!NOTE]\n> **OmO 5.0: OmO ❤️ Pi**\n> Install it with curl -fsSL https://get.omo.dev/install.sh | bash. Memory system, CodeMode, Anthropic subscriptions, all covered.\n> \n> *Just type "mass ulw" with your prompt - now you are the master of graph engineering. Multi-model ultracode, together with a better memory system. (The right panel is omo-herdr-dag.)*\n\n> **Sponsors**\n> These are our sponsors. They help keep OmO going as a personal side project.\n> | | **OpenGateway** sponsors OmO. An OpenAI-compatible gateway that puts many model providers behind one API. Thank you for backing open source. |\n> | :-----| :----- |\n\n> [!NOTE]\n>\n> \n> > **OmO is maintained by Jobdori, the AI assistant shown above. Meet your own Jobdori, Dori. Join the waitlist here.**\n\n> [!TIP]\n> Be with us!\n>\n> | | Join our Discord community to connect with contributors and fellow OmO users. |\n> | :-----| :----- |\n> | | Updates for OmO used to be posted on my X account. Since it was mistakenly suspended, @justsisyphus now posts updates on my behalf. |\n> | | Follow @code-yeongyu on GitHub for more projects. |\n\n -->\n\n \n\n \n\nOmO\n\n**Your tool for real work. But it\'s an agent.**\n\n \n \n \n \n \n \n\nEnglish | 简体中文 | 日本語 | Русский | 한국어\n\n \n\n -->\n\nOmO is one omo command that turns your tokens into finished work: research across ten thousand sources, a deck people actually get, backends, frontends, code. It runs on senpi, our fork of pi, with everything below built in.\n\nInstall\n\n \n\nOn Windows, in PowerShell: irm https://get.omo.dev/install.ps1 | iex. The script installs the native omo binary for your OS and CPU into ~/.local/bin, checks it against the release checksums, and falls back to GitHub Releases when the mirror is unreachable. Add -s -- beta (or a version such as -s -- 5.1.1) after bash to pick another channel. Windows CMD, fixing your PATH, updating and uninstalling are covered at omo.dev/docs/install.\n\nPrefer a package manager? bun add -g omo-ai (or npm i -g omo-ai) installs the same OmO. The package is omo-ai; the unrelated omo package on npm is someone else\'s.\n\nOpen your project, run omo, describe the job. That\'s the whole setup.\n\nIn a session, /thinking sets how hard the model thinks (/thinking alone opens a picker), Shift+Tab cycles through the levels, and /efforts sets the reasoning effort directly on models with effort grades. To pick up earlier work, /resume (or /sessions) opens your past sessions; from the shell, omo -c continues the last one and omo -r opens the picker. /help lists everything else.\n\nComing from the OpenCode edition or LazyCodex? Run omo setup once. It carries your provider keys, custom providers, MCP servers, skills and model picks across. omo doctor then shows which task categories your providers can run and what the old install left behind. The full walkthrough is Migrating from OpenCode.\n\nTo update, run the install line again (a package-manager install updates with omo update). rm ~/.local/bin/omo removes the binary install; bun remove -g omo-ai (or npm uninstall -g omo-ai) removes the package install.\n\nWhy OmO\n\n**ultrawork.** Skip the study. If a task feels hard, add ultrawork (or ulw) to your prompt. The agent reads the codebase before touching a line, plans, proves each step before moving on, checks the result on the real surface, and stops when it\'s done.\n\n**Hundreds of agents, one keyword.** Type mass ulw and the whole job becomes a graph of agents: deep research over thousands of sources, a full machine-learning pipeline, a migration nobody wants to do. Each part runs on the model that fits it, in parallel, and gets checked before anything is called done.\n\n**Kibitzer. Your agent doesn\'t forget you, or your work.** Memory lives in a git repository of markdown files. A small, cheap model sits beside the expensive main agent, reads what you\'ve done before, and nudges the main agent before it repeats a mistake. You stop re-explaining yourself.\n\n**Mix models without thinking about it.** Use the frontier models you love together with the cheap ones. Just sign in: /login covers Claude, ChatGPT, Kimi and GLM subscriptions. Every model ships with a system prompt tuned for it, so you never pick the wrong one.\n\n**High-quality skills, only the ones you need.** Your model is smart, but it still doesn\'t know a lot about solving real-world problems. OmO ships the missing knowledge as skills, browser use included, and loads only what the task needs.\n\n**Everything else is crafted.** Independent tool calls run together in one small program, so twenty reads are one round trip. Malformed tool arguments are repaired before they run. Waiting on a build or a deploy is a subscription, not polling. Long work keeps a record of what was checked, so a restarted session picks up where it stopped. Config, skills and prompts reload live.\n\nConfiguration\n\nOpinionated defaults, adjustable if you insist. Your settings live in ~/.omo/omo.jsonc, and a project can add its own .omo/omo.jsonc; the closest file wins. Every key is in the configuration reference.\n\nDocs\n\n- omo.dev/docs\n- Migrating from OpenCode\n- Configuration reference\n- Features\n- Computer use (experimental)\n- Ultrawork Manifesto\n\nReviews\n\n> "It made me cancel my Cursor subscription. Unbelievable things are happening in the open source community." - Arthur Guiot\n\n> "If Claude Code does in 7 days what a human does in 3 months, OmO does it in 1 hour." - B, Quant Researcher\n\n> "You guys should pull this into core and recruit him. Seriously. It\'s really, really, really good." - Henning Kilset\n\nAuthor\'s Note\n\nWhen I have to take the wheel back from an agent, that isn\'t teamwork. The system failed. OmO is built on that idea. You say what you want; the agent researches, plans, writes code you couldn\'t tell from a senior engineer\'s, verifies it, and keeps going until it\'s finished. It spends tokens when they buy real speed and routes the easy parts to cheap models.\n\nI\'m this project\'s most obsessive user. Which model has the sharpest logic? Who writes the best prose? What did everyone else ship this week? OmO is where those answers land. Have a better way? PRs welcome.\n\nThe goal is an agent you stop noticing. You flip the switch, the light turns on. The long version is the Ultrawork Manifesto.\n\nNo affiliation with any project or model mentioned. The Ultragoal and UltraQA ideas come from oh-my-codex, reimplemented from concept for OmO.\n\nLoved by professionals at\n\nIndent, makers of Spray (influencer marketing solution), vovushop (cross-border commerce platform) and vreview (AI commerce review marketing solution). Google. Microsoft. Vercel. ELESTYLE, makers of elepay (multi-mobile payment gateway) and OneQR (mobile application SaaS for cashless solutions). Deepgram.', 'readme_raw_len': 6698, 'one_line_zh': 'OmO：在 Prompt 中输入 mass ulw 关键词即可', 'one_line_plain': 'Type "mass ulw" keyword with your prompt to trigger OmO'}, {'id': 2762, 'key': 'upstash-context7', 'kind': 'mcp', 'name': 'upstash/context7', 'one_line': '📇 ☁️ - Up-to-date code documentation for LLMs and AI code editors', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Context7 Platform Up-to-date docs for AI agents', 'url': 'https://github.com/upstash/context7', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/upstash/context7', 'downloads': None, 'stars': 63321, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/upstash.png?size=64', 'official_desc': '📇 ☁️ - Up-to-date code documentation for LLMs and AI code editors.', 'stars_own': 63321, 'stars_creator': None, 'stars_own_repo': 'upstash/context7', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Context7 - Up-to-date Docs for AI Agents\n\n \n\n \n\n❌ Without Context7\n\nLLMs rely on outdated or generic information about the libraries you use. You get:\n\n- ❌ Code examples are outdated and based on year-old training data\n- ❌ Hallucinated APIs that don\'t even exist\n- ❌ Generic answers for old package versions\n\n✅ With Context7\n\nContext7 pulls up-to-date, version-specific documentation and code examples straight from the source — and gives them to your coding agent.\n\n \n\n \n\n \n\nContext7 fetches up-to-date code examples and documentation right into your coding agent\'s context. No tab-switching, no hallucinated APIs that don\'t exist, no outdated code generation.\n\nWorks in two modes:\n\n- **CLI + Skills** — installs a skill that guides your agent to fetch docs using ctx7 CLI commands (no MCP required)\n- **MCP** — registers a Context7 MCP server so your agent can call documentation tools natively\n\nInstallation\n\n> [!NOTE]\n> **API Key Recommended**: Get a free API key at context7.com/dashboard for higher rate limits.\n\nSet up Context7 for your coding agents with a single command. The ctx7 CLI requires Node.js 22.13 or newer.\n\n \n\nAuthenticates via OAuth, generates an API key, and installs the appropriate skill. You can choose between CLI + Skills or MCP mode. Use --cursor, --claude, or --opencode to target a specific agent.\n\nTo remove the generated setup later, run npx ctx7 remove. If you globally installed the CLI with npm install -g ctx7, remove that package separately with npm uninstall -g ctx7.\n\nTo configure manually, use the Context7 server URL https://mcp.context7.com/mcp with your MCP client and pass your API key via the Authorization: Bearer YOUR_API_KEY header. See the link below for client-specific setup instructions.\n\n**Manual Installation / Other Clients →**\n\nImportant Tips\n\nUse Library Id\n\nIf you already know exactly which library you want to use, add its Context7 ID to your prompt. That way, Context7 can skip the library-matching step and directly retrieve docs.\n\n \n\nThe slash syntax tells Context7 exactly which library to load docs for.\n\nSpecify a Version\n\nTo get documentation for a specific library version, just mention the version in your prompt:\n\n \n\nContext7 will automatically match the appropriate version.\n\nAdd a Rule\n\nIf you installed via ctx7 setup, a skill is configured automatically that triggers Context7 for library-related questions. To set up a rule manually instead, add one to your coding agent:\n\n- **Cursor**: Cursor Settings > Rules\n- **Claude Code**: CLAUDE.md\n- Or the equivalent in your coding agent\n\n**Example rule:**\n\n \n\nAvailable Tools\n\nCLI Commands\n\n- ctx7 library : Searches the Context7 index by library name and returns matching libraries with their IDs.\n- ctx7 docs : Retrieves documentation for a library using a Context7-compatible library ID (e.g., /mongodb/docs, /vercel/next.js).\n\nMCP Tools\n\n- resolve-library-id: Resolves a general library name into a Context7-compatible library ID.\n - query (required): The user\'s question or task (used to rank results by relevance)\n - libraryName (required): The name of the library to search for\n- query-docs: Retrieves documentation for a library using a Context7-compatible library ID.\n - libraryId (required): Exact Context7-compatible library ID (e.g., /mongodb/docs, /vercel/next.js)\n - query (required): The question or task to get relevant documentation for\n - language (optional): The programming language you write in, used to rank code examples in that language first\n\nMore Documentation\n\n- CLI Reference - Full CLI documentation\n- MCP Clients - Manual MCP installation for 30+ clients\n- Adding Libraries - Submit your library to Context7\n- Troubleshooting - Common issues and solutions\n- API Reference - REST API documentation\n- Developer Guide - Run Context7 MCP locally\n\nPackages\n\n- @upstash/context7-mcp - MCP server\n- ctx7 - CLI\n- @upstash/context7-sdk - TypeScript SDK\n- @upstash/context7-tools-ai-sdk - Vercel AI SDK tools\n- @upstash/context7-pi - pi.dev extension\n\nDisclaimer\n\n1- Context7 projects are community-contributed and while we strive to maintain high quality, we cannot guarantee the accuracy, completeness, or security of all library documentation. Projects listed in Context7 are developed and maintained by their respective owners, not by Context7. If you encounter any suspicious, inappropriate, or potentially harmful content, please use the "Report" button on the project page to notify us immediately. We take all reports seriously and will review flagged content promptly to maintain the integrity and safety of our platform. By using Context7, you acknowledge that you do so at your own discretion and risk.\n\n2- This repository hosts the MCP server’s source code. The supporting components — API backend, parsing engine, and crawling engine — are private and not part of this repository.\n\n🤝 Connect with Us\n\nStay updated and join our community:\n\n- 📢 Follow us on X for the latest news and updates\n- 🌐 Visit our Website\n- 💬 Join our Discord Community\n\n📺 Context7 In Media\n\n- Better Stack: "Free Tool Makes Cursor 10x Smarter"\n- Cole Medin: "This is Hands Down the BEST MCP Server for AI Coding Assistants"\n- Income Stream Surfers: "Context7 + SequentialThinking MCPs: Is This AGI?"\n- Julian Goldie SEO: "Context7: New MCP AI Agent Update"\n- JeredBlu: "Context 7 MCP: Get Documentation Instantly + VS Code Setup"\n- Income Stream Surfers: "Context7: The New MCP Server That Will CHANGE AI Coding"\n- AICodeKing: "Context7 + Cline & RooCode: This MCP Server Makes CLINE 100X MORE EFFECTIVE!"\n- Sean Kochel: "5 MCP Servers For Vibe Coding Glory (Just Plug-In & Go)"\n\n📄 License\n\nMIT', 'readme_raw_len': 5621, 'one_line_zh': '向 LLM 和 AI 代码编辑器提供最新代码文档', 'one_line_plain': 'Provide up-to-date code documentation for LLMs and AI code editors'}, {'id': 1454, 'key': 'idosal-git-mcp', 'kind': 'mcp', 'name': 'idosal/git-mcp', 'one_line': '📇 ☁️ - Generic remote server that provides documentation for any GitHub repository or project', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Put an end to code hallucinations GitMCP is a free, open-source, remote MCP server for any GitHub project', 'url': 'https://github.com/idosal/git-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/idosal/git-mcp', 'downloads': None, 'stars': 8471, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/idosal.png?size=64', 'official_desc': '📇 ☁️ - Generic remote server that provides documentation for any GitHub repository or project.', 'stars_own': 8471, 'stars_creator': None, 'stars_own_repo': 'idosal/git-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'GitMCP\n\n \n \n \n\n \n What is GitMCP •\n Features •\n Getting Started •\n How It Works •\n Badge •\n Examples •\n FAQ •\n Privacy •\n Contributing •\n License \n \n \n\n \n \n \n \n\n \n \n \n\n🤔 What is GitMCP?\n**Stop vibe-hallucinating and start vibe-coding!**\n\nGitMCP is a free, open-source, remote Model Context Protocol (MCP) server that transforms **any** GitHub project (repositories or GitHub pages) into a documentation hub. It enables AI tools like Cursor to access up-to-date documentation and code, even if the LLM has never encountered them, thereby eliminating code hallucinations seamlessly.\n\nGitMCP supports **two flavors** -\n\n* **Specific Repository (gitmcp.io/{owner}/{repo} or {owner}.gitmcp.io/{repo}):** Use these when you primarily work with a select number of libraries. This ensures your AI assistant always targets the correct project, enhancing security and relevance by preventing access to unintended repositories.\n* **Generic Server (gitmcp.io/docs):** Use this for maximum flexibility when you need to switch between different repositories frequently. The AI assistant will prompt you (or decide based on context) which repository to access for each request. Be mindful that this relies on correctly identifying the target repository each time.\n\n**With GitMCP:**\n\n* AI assistants access the *latest* documentation and code directly from the source.\n* Get accurate API usage and reliable code examples.\n* Work effectively even with niche, new, or rapidly changing libraries.\n* Significantly reduced hallucinations and improved code correctness.\n\nFor example, this side-by-side comparison shows the result for the same one-shot prompt in Cursor when creating a three.js scene -\n\nhttps://github.com/user-attachments/assets/fbf1b4a7-f9f0-4c0e-831c-4d64faae2c45\n\n✨ Features\n\n- 😎 **Latest Documentation on ANY GitHub Project**: Grant your AI assistant seamless access to the GitHub project\'s documentation and code. The built-in smart search capabilities help find exactly what the AI needs without using too many tokens!\n- 🧠 **No More Hallucinations**: With GitMCP, your AI assistant can provide accurate and relevant answers to your questions.\n- ☁️ **Zero Setup**: GitMCP runs in the cloud. Simply add the chosen GitMCP URL as an MCP server in your IDE — no downloads, installations, signups, or changes are required.\n- 💬 **Embedded Chat**: Start quickly by chatting directly with the repository\'s documentation through our in-browser chat!\n- ✅ **Open, Free, and Private**: GitMCP is open-source and completely free to use. It doesn\'t collect personal information or store queries. You can even self-host it!\n\n \n\n🚀 Getting Started\n\nUsing GitMCP is easy! Simply follow these steps:\n\nStep 1: Choose the type of server you want\n\nChoose one of these URL formats depending on what you want to connect to:\n\n- For GitHub repositories: gitmcp.io/{owner}/{repo}\n- For GitHub Pages sites: {owner}.gitmcp.io/{repo}\n- For a generic tool that supports any repository (dynamic): gitmcp.io/docs\n\nReplace {owner} with the GitHub username or organization name, and {repo} with the repository name.\n\nFor your convenience, you can also use the conversion tool on the landing page to format the GitHub URL into an MCP URL!\n\nStep 2: Connect your AI assistant\n\nSelect your AI assistant from the options below and follow the configuration instructions:\n\nConnecting Cursor\n\nUpdate your Cursor configuration file at ~/.cursor/mcp.json:\n \n\nConnecting Claude Desktop\n\n1. In Claude Desktop, go to Settings > Developer > Edit Config\n2. Replace the configuration with:\n \n\nConnecting Windsurf\n\nUpdate your Windsurf configuration file at ~/.codeium/windsurf/mcp_config.json:\n \n\nConnecting VSCode\n\nUpdate your VSCode configuration file at .vscode/mcp.json:\n \n\nConnecting Cline\n\nUpdate your Cline configuration file at ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json:\n \n\nConnecting Highlight AI\n\n1. Open Highlight AI and click the plugins icon (@ symbol) in the sidebar\n2. Click **Installed Plugins** at the top of the sidebar\n3. Select **Custom Plugin**\n4. Click **Add a plugin using a custom SSE URL**\n\nPlugin name: gitmcp\nSSE URL: https://gitmcp.io/{owner}/{repo}\n\nFor more details on adding custom MCP servers to HighlightAI, refer to the documentation.\n\nConnecting Augment Code\n\n1. Open Augment Code settings\n2. Navigate to the MCP section\n3. Add a new MCP server with the following details:\n\nName the MCP server: git-mcp Docs\n\nUse this command:\n \n\nOr use the following configuration:\n \n\nConnecting Msty AI\n1. Open Msty Studio\n2. Go to Tools > Import Tools from JSON Clipboard\n3. Paste the following configuration:\n\n \n\nFor more details on configuring MCP servers in Augment Code, visit the Augment Code documentation.\n\n> **Note:** Remember to replace {owner} and {repo} with the actual GitHub username/organization and repository name. You can also use the dynamic endpoint https://gitmcp.io/docs to allow your AI to access any repository on demand.\n\n⚙ How It Works\n\nGitMCP connects your AI assistant to GitHub repositories using the Model Context Protocol (MCP), a standard that lets AI tools request additional information from external sources.\n\nWhat happens when you use GitMCP:\n\n1. **You provide the GitMCP URL** to your AI assistant (e.g., gitmcp.io/microsoft/typescript). GitMCP exposes tools like documentation fetching, smart search, code search, etc.\n2. **Prompt the AI assistant** on documentation/code-related questions.\n3. **Your AI sends requests** to GitMCP to use its tools (with your approval).\n4. **GitMCP executes the AI\'s request** and returns the requested data.\n5. **Your AI receives the information** and generates a more accurate, grounded response without hallucinations.\n\nSupported Documentation\n\nGitMCP currently supports the following documents (in order of priority):\n1. llms.txt\n2. AI-optimized version of the project\'s documentation\n3. README.md/root\n\n💡 Examples\n\nHere are some examples of how to use GitMCP with different AI assistants and repositories:\n\nExample 1: Using Windsurf with a specific repository\n\nFor the GitHub repository https://github.com/microsoft/playwright-mcp, add https://gitmcp.io/microsoft/playwright-mcp as an MCP server to Windsurf.\n\n**Prompt to Claude:**\n> "How do I use the Playwright MCP"\n\nWindsurf will pull the relevant documentation from GitMCP to implement the memory feature correctly.\n\nExample 2: Using Cursor with a GitHub Pages site\n\nFor the GitHub Pages site langchain-ai.github.io/langgraph, add https://langchain-ai.gitmcp.io/langgraph as an MCP server to Cursor.\n\n**Prompt to Cursor:**\n> "Add memory to my LangGraph agent"\n\nCursor will pull the relevant documentation and code from GitMCP to correctly implement the memory feature.\n\nExample 3: Using Claude Desktop with the dynamic endpoint\n\nYou don\'t have to pick specific repositories. The generic gitmcp.io/docs endpoint allows AI to pick the GitHub project on the fly!\n\n**Prompt to any AI assistant:**\n> "I want to learn about the OpenAI Whisper speech recognition model. Explain how it works."\n\nClaude will pull the data from GitMCP and answer the question.\n\n🛠️ Tools\n\nGitMCP provides AI assistants with several valuable tools to help them access, understand, and query GitHub repositories.\n\nfetch_ _documentation\n\nThis tool gets the primary documentation from a GitHub repository. It works by retrieving relevant documentation (e.g., llms.txt). This gives the AI a good overview of what the project is about\n\n**When it\'s useful:** For general questions about a project\'s purpose, features, or how to get started\n\nsearch_ _documentation\n\nThis tool lets the AI search through a repository\'s documentation by providing a specific search query. Instead of loading all the documentation (which could be very large), it uses intelligent search to find just the relevant parts.\n\n**When it\'s useful:** For specific questions about particular features, functions, or concepts within a project\n\nfetch_url_content\n\nThis tool helps the AI get informati', 'readme_raw_len': 12214, 'one_line_zh': '通用远程服务器，为任意 GitHub 仓库或项目提供文档', 'one_line_plain': 'Provide documentation for any GitHub repository or project from a generic remote server'}, {'id': 1441, 'key': 'haris-musa-excel-mcp-server', 'kind': 'mcp', 'name': 'haris-musa/excel-mcp-server', 'one_line': '🐍 🏠 - Excel workbook creation, data operations, formatting, charts, pivot tables and formulas', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Excel MCP server: let AI (Claude, Cursor, Codex) create and edit Excel files with charts, PivotTables, slicers and formulas. No Excel required.', 'url': 'https://github.com/haris-musa/excel-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/haris-musa/excel-mcp-server', 'downloads': None, 'stars': 4248, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/haris-musa.png?size=64', 'official_desc': '🐍 🏠 - Excel workbook creation, data operations, formatting, charts, pivot tables and formulas.', 'stars_own': 4248, 'stars_creator': None, 'stars_own_repo': 'haris-musa/excel-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Excel MCP Server\n\nAn open-source Model Context Protocol (MCP) server that lets\nClaude, Cursor, VS Code Copilot, Codex and other AI clients create, read and edit Excel files:\ncharts, PivotTables, formulas, tables and more. It is built on openpyxl, needs **no Microsoft\nExcel**, and runs on Windows, macOS and Linux.\n\n**Install and try it at excelmcpserver.com**, or see\nQuick start below.\n\nFeatures\n\n**Charts and PivotTables**\n- Create Excel charts with AI: column, bar, line, area, pie, doughnut, radar, scatter and\n bubble, with combos, secondary axes, trendlines and error bars\n- The Excel 2016 chart types: waterfall, histogram, Pareto, box and whisker, treemap, sunburst\n and funnel\n- Real PivotTables that Excel refreshes (grouping, calculated fields, "show values as",\n layouts, filters), plus slicers and timelines for PivotTables and tables\n- Sparklines (line, column, win/loss)\n\n**Formulas and dynamic arrays**\n- Formulas are calculated without Excel by a built-in engine (about 260 functions, checked\n against more than 2,500 formulas recorded from real Excel), so read_range returns values\n for files that were never opened in Excel\n- Dynamic arrays (FILTER, SORT, UNIQUE, SEQUENCE, LET) are stored as Excel stores\n them and spill into the cells next to them\n\n**Data, tables and rules**\n- Read and write cells, dates and hyperlinks; tables with totals rows and calculated columns\n- Conditional formatting (scales, data bars, icon sets, top/bottom, duplicates, text, dates),\n data validation (dropdowns, limits, input messages)\n- Sort, filter (with criteria), find and replace, remove duplicates, text to columns, fill\n series, paste special\n- Fonts, fills, borders, number formats, merged cells, frozen panes, grouping, print setup,\n sheet protection, images, notes and defined names. Inserting or deleting rows and columns\n updates every reference, as in Excel\n\n**Large files**\n- Reads stream the workbook and return pages, so memory stays flat on sheets with hundreds of\n thousands of rows; every call is bounded by size and cell limits\n\n**Macros (opt-in)**\n- Read the VBA code in .xlsm files. Writing VBA is off unless you start the server with\n --allow-vba-write; the server never runs macros\n\n**Safe by design**\n- Optional folder confinement, a formula safety check, read-only mode, localhost-only HTTP by\n default, and atomic saves that never leave a half-written file\n\nWorks with .xlsx, .xlsm (macros are preserved), .xltx and .xltm files. Existing\nthreaded comments, shapes, form controls and other content the tools cannot edit are kept as\nthey are.\n\nQuick start\n\nYou need uv. Every client runs the\nserver with uvx excel-mcp-server stdio; replace /path/to/workbooks with the folder the\nserver may use. excelmcpserver.com has the same steps with copy\nbuttons and one-click install links for Cursor and VS Code.\n\n**Claude Desktop (Chat)**: download\nexcel-mcp-server.mcpb\nfrom the latest release and open it. Claude asks which folder the server may use.\n\n**Claude Code** (the CLI, and the Code tab in Claude Desktop):\n\n \n\n**Cursor** (~/.cursor/mcp.json), and most clients that use an mcpServers config:\n\n \n\n**VS Code with GitHub Copilot** (.vscode/mcp.json, note the servers key):\n\n \n\n \n OpenAI Codex, Gemini CLI, Devin Desktop, and Claude Desktop without the bundle \n\n**OpenAI Codex** (CLI, IDE extension and app):\n\n \n\n**Gemini CLI**:\n\n \n\n**Devin Desktop**:\n\n \n\n**Claude Desktop, manual setup**: open Settings, Developer, Edit Config, add the mcpServers\nJSON above to claude_desktop_config.json, and restart Claude.\n\n \n\nDesktop apps often cannot find uvx, because they do not see your shell\'s PATH (common\non macOS). Use its full path instead, which which uvx prints.\n\nChoosing which files it can use\n\nWith --allow-dir DIR, the server only opens workbooks inside DIR (subfolders included)\nand relative paths such as reports/q1.xlsx start there. Repeat the flag to allow several\nfolders, or set EXCEL_FILES_PATH (separate folders with : on macOS/Linux and ; on\nWindows).\n\nWithout --allow-dir, any absolute path to an Excel file works. In every mode the server\nonly touches Excel files and never silently overwrites an existing workbook.\n\nRemote use (Streamable HTTP)\n\n \n\nClients connect to http://127.0.0.1:8017/mcp. Workbooks live in the --allow-dir folder\n(default ./excel_files), and export_workbook / import_workbook move files between the\nserver and the client.\n\nThe server listens on localhost only. To accept other machines, set a token and a host:\n\n \n\nClients then send Authorization: Bearer change-me. Put a TLS-terminating reverse proxy in\nfront of it for use across networks.\n\nDocker\n\n \n\nConfiguration\n\n| Flag | Environment variable | Default | Meaning |\n| --- | --- | --- | --- |\n| --allow-dir DIR | EXCEL_FILES_PATH | none (stdio), ./excel_files (HTTP) | Folders workbooks must be in |\n| --read-only | EXCEL_MCP_READ_ONLY=1 | off | Only offer tools that do not change files |\n| --allow-vba-write | EXCEL_MCP_ALLOW_VBA_WRITE=1 | off | Add tools that write VBA macros (see warning below); ignored with --read-only |\n| --max-file-mb N | | 100 | Largest workbook the server opens. Also sets the unpacked-size limit (10 times this) that every opened or uploaded workbook must stay under; parts compressed over 500:1 are refused |\n| --log-level LEVEL | | WARNING | Logging on stderr |\n| --host HOST | EXCEL_MCP_HOST | 127.0.0.1 | HTTP listen address |\n| --port PORT | EXCEL_MCP_PORT | 8017 | HTTP port |\n| | EXCEL_MCP_AUTH_TOKEN | none | Bearer token required on HTTP requests |\n| --allow-unauthenticated | | off | Allow a non-local --host without a token |\n\nTools\n\n| Area | Tools |\n| --- | --- |\n| Workbooks | create_workbook, describe_workbook, set_workbook_settings, list_workbooks, export_workbook, import_workbook |\n| Sheets | describe_sheet, create_sheet, rename_sheet, copy_sheet, delete_sheet, insert_rows_or_columns, delete_rows_or_columns |\n| Cells | read_range, write_range, clear_range, copy_range, sort_range, transform_range, find_cells, replace_cells |\n| Formatting | format_range, merge_cells, set_sheet_layout, add_conditional_format, add_data_validation |\n| Objects | set_table, create_chart, delete_chart, create_pivot_table, delete_pivot_table, add_slicer, delete_slicer, insert_image, delete_image, add_sparklines, delete_sparklines |\n| Names and notes | set_defined_name, delete_defined_name, set_note, delete_note |\n| Macros | read_vba; with --allow-vba-write: write_vba_module, delete_vba_module |\n\nEvery parameter is documented in TOOLS.md. Parameters mean the same in every tool:\n\n| Parameter | Meaning |\n| --- | --- |\n| path, sheet | The workbook and the worksheet |\n| range, at | A block of cells (A1:D20), and the top-left cell of whatever is placed there: written values, copies, charts, images, PivotTables, slicers |\n| source | Where data comes from, optionally on another sheet (Data!A1:E200) |\n| name | The name of a table, chart, image, PivotTable, slicer or defined name |\n| _cm, _pt, _chars | Units of sizes: width_cm, row_heights_pt, column_widths_chars |\n\nTools that change a workbook return what they changed (sheet, range, name) so that the\nnext call can refer to it. Charts and images are selected by name, as listed by describe_sheet.\n\nWriting macros (opt-in)\n\nWith --allow-vba-write (or EXCEL_MCP_ALLOW_VBA_WRITE=1) the server can set the code of\nstandard, class, workbook and sheet modules in .xlsm and .xltm files, delete standard and\nclass modules, and create new .xlsm/.xltm workbooks. The server only stores the code; it\nnever runs it, and Excel asks before enabling macros. Digitally signed VBA projects are\nrefused, since any change would invalidate the signature.\n\n> **Warning:** macro code runs with your user\'s rights once you enable macros in Excel. A\n> model that reads untrusted content could be tricked into writing harmful code. Enable this\n> option only for trusted workflows, keep --allow-dir narrow, and **read the code before\n> you enable macros**. The tools are never offered in --read-only mode.\n\nSecurity\n\n- Fo', 'readme_raw_len': 13970, 'one_line_zh': '创建 Excel 工作簿，支持数据操作、格式设置、图表、数据透视表和公式', 'one_line_plain': 'Create Excel workbooks with data operations, formatting, charts, pivot tables, and formulas'}, {'id': 5317, 'key': 'evalstate-fast-agent', 'kind': 'repo', 'name': 'evalstate/fast-agent', 'one_line': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'url': 'https://github.com/evalstate/fast-agent', 'source': 'github', 'source_url': 'https://github.com/evalstate/fast-agent', 'downloads': None, 'stars': 3943, 'installs': None, 'mentions': 1, 'score': 110.86, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-19', 'times_seen': 18, 'icon': 'https://github.com/evalstate.png?size=64', 'official_desc': 'Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support', 'stars_own': 3943, 'stars_creator': None, 'stars_own_repo': 'evalstate/fast-agent', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'uvx fast-agent-mcp@latest', 'install_src': 'HEAD/README.md', 'install_conf': 'medium', 'desc_src': 'github-api', 'readme_body': 'Start Here\n\n> [!TIP]\n> Please see https://fast-agent.ai for latest documentation.\n\n**fast-agent** is a flexible way to interact with LLMs, excellent for use as a Coding Agent, Development Toolkit, Evaluation or Workflow platform.\n\nTo start an interactive session with shell support, install uv and run\n\n \n\nTo start coding with Hugging Face inference providers or use your OpenAI Codex plan:\n\n \n\nEnter a shell with !, or run shell commands e.g. ! cd web && npm run build.\n\nManage skills with the /skills command, and connect to MCP Servers with /connect. The default **fast-agent** registry contains skills to let you set up LSP, Agent and Tool Hooks, Compaction strategies, Automation and more.\n\n \n\nIt\'s recommended to install **fast-agent** to set up the shell aliases and other tooling.\n\n \n\nUse local models with the generic provider, or automatically create the correct configuration for llama.cpp:\n\n \n\nAny **fast-agent** setup or program can be used with any ACP client - the simplest way is to use fast-agent-acp:\n\n \n\n**fast-agent** enables you to create and interact with sophisticated multimodal Agents and Workflows in minutes. It is the first framework with complete, end-to-end tested MCP Feature support including Sampling and Elicitations.\n\nfast-agent is CLI-first, with an optional prompt_toolkit-powered interactive terminal prompt (TUI-style input, completions, and in-terminal menus); responses can stream live to the terminal via rich without relying on full-screen curses UIs or external GUI overlays.\n\n \n\nThe simple declarative syntax lets you concentrate on composing your Prompts and MCP Servers to build effective agents.\n\nModel support is comprehensive with native support for Anthropic, OpenAI and Google providers as well as Azure, Ollama, Deepseek and dozens of others via TensorZero. Structured Outputs, PDF and Vision support is simple to use and well tested. Passthrough and Playback LLMs enable rapid development and test of Python glue-code for your applications.\n\nRecent features include:\n\n- Agent Skills (SKILL.md)\n- MCP Apps\n- OpenAI Apps SDK\n- Shell Mode\n- Advanced MCP Transport Diagnsotics\n- MCP Elicitations\n\n \n\nfast-agent is the only tool that allows you to inspect Streamable HTTP Transport usage - a critical feature for ensuring reliable, compliant deployments. OAuth is supported with KeyRing storage for secrets. Use the fast-agent auth command to manage.\n\n> [!IMPORTANT]\n>\n> Documentation is included in this repository under docs/. Use the docs helper script from the\n> repository root to install, generate, build, serve, screenshot, and assess the site.\n\nAgent Application Development\n\nPrompts and configurations that define your Agent Applications are stored in simple files, with minimal boilerplate, enabling simple management and version control.\n\nChat with individual Agents and Components before, during and after workflow execution to tune and diagnose your application. Agents can request human input to get additional context for task completion.\n\nSimple model selection makes testing Model MCP Server interaction painless. You can read more about the motivation behind this project here\n\n \n\nGet started:\n\nStart by installing the uv package manager for Python. Then:\n\n \n\nWith no configured model, an interactive run opens the model picker.\nAutomation and server runs must pass --model, set FAST_AGENT_MODEL, configure\ndefault_model, or declare a model on the agent.\n\nFor packaged starter agents, use fast-agent go --pack --model .\nThis installs the pack into the selected fast-agent home if needed, then\nstarts go normally. --model is a fallback for cards without an explicit\nmodel setting; a model declared directly in an AgentCard still wins.\n\nOther quickstart examples include a Researcher Agent (with Evaluator-Optimizer workflow) and Data Analysis Agent (similar to the ChatGPT experience), demonstrating MCP Roots support.\n\n> [!TIP]\n> Windows Users - there are a couple of configuration changes needed for the Filesystem and Docker MCP Servers - necessary changes are detailed within the configuration files.\n\nBasic Agents\n\nDefining an agent is as simple as:\n\n \n\nWe can then send messages to the Agent:\n\n \n\nOr start an interactive chat with the Agent:\n\n \n\nHere is the complete sizer.py Agent application, with boilerplate code:\n\n \n\nThe Agent can then be run with uv run sizer.py.\n\nSpecify a model with the --model switch - for example uv run sizer.py --model sonnet.\n\nModel strings also accept query overrides. For example:\n\n- uv run sizer.py --model "gpt-5?reasoning=low"\n- uv run sizer.py --model "claude-sonnet-4-6?web_search=on"\n- uv run sizer.py --model "claude-sonnet-4-5?context=1m"\n- uv run sizer.py --model "gpt-5?poll_period=240"\n\nFor Anthropic models, ?context=1m is only needed for earlier Sonnet 4 / Sonnet 4.5\nmodels that still require the explicit 1M context opt-in. Claude Sonnet 4.6 and\nClaude Opus 4.6 already use their long context window by default, so ?context=1m\nis accepted for backward compatibility but is unnecessary there.\n\nCombining Agents and using MCP Servers\n\n_To generate examples use fast-agent quickstart workflow. This example can be run with uv run workflow/chaining.py. Place fast-agent.yaml in the active fast-agent home, or pass an explicit config path when needed._\n\nAgents can be chained to build a workflow, using MCP Servers defined in the fast-agent.yaml file:\n\n \n\nAll Agents and Workflows respond to .send("message") or .prompt() to begin a chat session.\n\nSaved as social.py we can now run this workflow from the command line with:\n\n \n\nAdd the --quiet switch to disable progress and message display and return only the final response - useful for simple automations.\n\nMAKER\n\nMAKER (“Massively decomposed Agentic processes with K-voting Error Reduction”) wraps a worker agent and samples it repeatedly until a response achieves a k-vote margin over all alternatives (“first-to-ahead-by-k” voting). This is useful for long chains of simple steps where rare errors would otherwise compound.\n\n- Reference: Solving a Million-Step LLM Task with Zero Errors\n- Credit: Lucid Programmer (PR author)\n\n \n\nAgents As Tools\n\nThe Agents As Tools workflow takes a complex task, breaks it into subtasks, and calls other agents as tools based on the main agent instruction.\n\nThis pattern is inspired by the OpenAI Agents SDK Agents as tools feature.\n\nWith child agents exposed as tools, you can implement routing, parallelization, and orchestrator-workers decomposition directly in the instruction (and combine them). Multiple tool calls per turn are supported and executed in parallel.\n\nCommon usage patterns may combine:\n\n- Routing: choose the right specialist tool(s) based on the user prompt.\n- Parallelization: fan out over independent items/projects, then aggregate.\n- Orchestrator-workers: break a task into scoped subtasks (often via a simple JSON plan), then coordinate execution.\n\n \n\nExtended example and all params sample is available in the repository as\nexamples/workflows/agents_as_tools_extended.py.\n\nMCP OAuth (v2.1)\n\nFor SSE and HTTP MCP servers, OAuth is enabled by default with minimal configuration. A local callback server is used to capture the authorization code, with a paste-URL fallback if the port is unavailable.\n\n- Minimal per-server settings in fast-agent.yaml:\n\n \n\n- The OAuth client uses PKCE and in-memory token storage (no tokens written to disk).\n- Token persistence: by default, tokens are stored securely in your OS keychain via keyring. If a keychain is unavailable (e.g., headless container), in-memory storage is used for the session.\n- To force in-memory only per server, set:\n\n \n\n- To disable OAuth for a specific server , set auth.oauth: false for that server.\n\nMCP Ping (optional)\n\nThe MCP ping utility can be enabled by either peer (client or server). See the Ping overview.\n\nClient-side pinging is configured per server (default: 30s interval, 3 missed pings):\n\n \n\n**Global tools (@fast.tool)** — available to all agents that don\'t declare their own tools:\n\n \n\nAgen', 'readme_raw_len': 10194, 'one_line_zh': '编写、构建与评估 Agent，出色的 Model 与 Skills/MCP/ACP/A2A 支持', 'one_line_plain': 'Create, run and test code agents with support for Model, Skills, MCP, ACP and A2A protocols'}, {'id': 4118, 'key': 'taylorwilsdon-google-workspace-mcp', 'kind': 'mcp', 'name': 'taylorwilsdon/google_workspace_mcp', 'one_line': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace integration covering Calendar, Drive, Gmail, Docs, Forms, Chat, Slides and…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search & Drive with AI - Comprehensive Google Workspace MCP Server & CLI Tool', 'url': 'https://github.com/taylorwilsdon/google_workspace_mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/taylorwilsdon/google_workspace_mcp', 'downloads': None, 'stars': 3376, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/taylorwilsdon.png?size=64', 'official_desc': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace integration covering Calendar, Drive, Gmail, Docs, Forms, Chat, Slides and Sheets.', 'stars_own': 3376, 'stars_creator': None, 'stars_own_repo': 'taylorwilsdon/google_workspace_mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'Google Workspace MCP Server \n\n \n \n \n \n \n \n\n \n\n*Full natural language control over Google Calendar, Drive, Gmail, Docs, Sheets, Slides, Forms, Tasks, Contacts, and Chat through all MCP clients, AI assistants and developer tools.*\n*Includes a full featured CLI & Code Mode for use with tools like Claude Code and Codex!*\n\n**The most feature-complete Google Workspace MCP server** is in a class of it\'s own: it can do things that Google\'s own tooling and the built in integrations with Claude and ChatGPT can\'t come close to with multi-user support, rich fine-grained editing tools and the most extensive coverage of any Workspace AI integration in existence. \n\nBy leveraging native OAuth 2.1, stateless deployment capability and external auth server & gateway passthrough auth support, it\'s also the only Workspace MCP you can host for your whole organization centrally & securely!\n\nSupports all free Google accounts & Google Workspace plans with expanded app options like Chat & Spaces. Interested in a managed cloud instance? That can be arranged (starting at $5/mo).\n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n---\n\n**See it in action:**\n \n \n \n\n---\n\nWhat It Does\n\nWorkspace MCP connects AI assistants to all twelve major Google Workspace services - 120+ tools behind a single MCP server, with OAuth 2.1 multi-user auth, three progressive tool tiers, read-only mode, a full CLI, and stateless container deployment. It runs locally over stdio for legacy clients and remotely over streamable HTTP with full implementation of the latest MCP spec.\n\nThe README covers just enough to get you running, with extensive documentation on the website:\n\n| Where to go | What you\'ll find |\n|:---|:---|\n| **Quick Start** | Google Cloud setup, credentials, and client connection with screenshots |\n| **Full Documentation** | Every tool, parameter, and auth mode |\n| **Advanced Deployment** | Reverse proxy & nginx config, origin validation, credential store backends (GCS/CMEK), trusted-gateway identity, and the complete environment variable reference |\n| **Client Setup Guides** | Claude Desktop/web Connectors, ChatGPT Developer Mode, and more |\n| **FAQ & Troubleshooting** | Enabling Google APIs, OAuth errors, redirect URIs, Google Chat setup, client quirks |\n\nSecurity & Compliance \n\n \n \n \n\n**For Security Teams**\n\nBy default, this server sends no data anywhere except Google\'s APIs, on behalf of the authenticated user, using your own OAuth client credentials. There is no usage reporting, analytics, license server, or SaaS dependency outside optional OTel support for your own usage.\n\n- **Fully open source** — every line is auditable in this repo\n- **Your OAuth client, your GCP project** — credentials never leave your environment & you control scopes\n- **You control the network** — deploy behind your reverse proxy, in your VPC, on your own terms\n- **Stateless mode** — zero disk writes for locked-down container environments\n- **Sensitive path blocking** — local file reads default to the managed attachment directory, and validate_file_path() still blocks .env* files plus common home-directory credential stores such as ~/.ssh/ and ~/.aws/ even if ALLOWED_FILE_DIRS is broadened\n\nFull dependency tree in pyproject.toml, pinned in uv.lock.\n\n \n \n\n**For Legal & Procurement**\n\nThis project is MIT licensed — not "open core," not "source available," not "free with a CLA." There is no dual licensing, no commercial tier gating features, and no contributor license agreement.\n\n- **Use commercially without restriction** — build products, sell services, deploy internally\n- **Fork, embed, redistribute** — MIT requires only attribution\n- **No CLA** — contributions remain under MIT\n- **No built-in telemetry to disclose** — optional tracing is off unless you configure it\n- **No network effects** — the server never contacts any endpoint you didn\'t configure\n- **Standard dependency licenses** — MIT, Apache 2.0, and BSD throughout the dependency chain; no copyleft, no AGPL\n \n \n \n\nServices\n\n \n \n \n 📧 Gmail \n 15 tools - search, send, draft, labels, filters, attachments \n \n \n 📁 Drive \n 16 tools - search, create, share, import Office files \n \n \n 📅 Calendar \n 7 tools - events, free/busy, Out of Office, Focus Time \n \n \n 📝 Docs \n 19 tools - edit, style, tables, tabs, comments, export \n \n \n \n \n 📊 Sheets \n 14 tools - ranges, tables, formatting, conditional rules \n \n \n 🖼️ Slides \n 7 tools - create, batch update, thumbnails, comments \n \n \n 📋 Forms \n 6 tools - build forms, publish, read responses \n \n \n ✅ Tasks \n 6 tools - tasks & lists with hierarchy \n \n \n \n \n 👤 Contacts \n 8 tools - people, groups, batch operations \n \n \n 💬 Chat \n 6 tools - spaces, messages, search, reactions \n \n \n 🔍 Custom Search \n 2 tools - programmable web search \n \n \n ⚡ Apps Script \n 15 tools - write, deploy, run & debug scripts \n \n \n \n\nEach page lists every tool with its tier, parameters, required scopes, and example prompts. The complete reference covers all twelve in one place.\n\n> 💬 **Google Chat** needs a one-time Chat app configuration and a Workspace account - see the Chat setup FAQ.\n\nQuick Start\n\n> Set credentials → pick a launch command → connect your client. Full walkthrough with screenshots: **workspacemcp.com/quick-start**\n\nYou\'ll need an OAuth client from Google Cloud Console in a project with the Google APIs enabled for the services you plan to use. The docs have one-click enable links for every API plus a single gcloud services enable command that covers them all, and the quick start guide walks through the whole setup in about five minutes.\n\n \n \n \n\n**Confidential Client**\n\n \n\n \n \n\n**OAuth 2.1 (PKCE)**\n\n \n\n \n \n \n\n**Tool tiers** keep context windows lean: core is the essential set, extended adds management operations, complete loads everything. Combine with --tools ..., --read-only, or per-service --permissions, and subtract individual tools with --disabled-tools ... - details in the server modes docs.\n\nConnect Your Client\n\n**Claude Desktop, web & mobile** - run the server in HTTP mode and add it as a **Connector** (Settings → Connectors → Add custom connector). This is the recommended path; the Connector guide has step-by-step screenshots. Legacy stdio configuration remains available for clients without Connector support - see the FAQ.\n\n**Claude Code**\n\n \n\n**ChatGPT** - connect via Developer Mode with the ChatGPT guide.\n\n**VS Code, LM Studio, Open WebUI, and everything else** - any MCP client works over streamable HTTP (recommended) or stdio. Client-specific walkthroughs live in the guides and FAQ.\n\nCLI\n\nworkspace-cli lists and calls tools against a running server with encrypted, disk-backed OAuth token caching - authenticate once, script forever:\n\n \n\nInstall globally with uv tool install . from this repo. ⚠️ Don\'t use uvx workspace-cli - an abandoned PyPI package squats that name.\n\nDeployment & Advanced Configuration\n\nEverything you need to run this in production lives in two places. The documentation covers auth modes and server configuration:\n\n- **OAuth 2.1 multi-user auth** - bearer tokens, required for remote or shared HTTP endpoints\n- **Stateless container mode** - zero disk writes for locked-down deployments\n- **OAuth proxy storage backends** - memory, disk, or Valkey/Redis for distributed setups\n- **External OAuth provider mode** - bring your own auth server, validate bearer tokens only\n- **Service accounts with domain-wide delegation** - per-request user impersonation with an optional domain allowlist\n- **Trusted-gateway identity** - proxy-verified per-user isolation with Pomerium, Cloudflare Access, oauth2-proxy, or any JWKS-verifiable gateway\n- **OpenTelemetry tracing** - optional, off unless you configure an OTLP endpoint\n- **Docker** - docker build -t workspace-mcp . && docker run -p 8000:8000 workspace-mcp\n\nThe **Advanced Deployment guide** covers self-hosting specifics: reverse proxy setup with WORKSPACE_EXTERNAL_URL (including the nginx Origin: null consent workaround, the WORKSPACE_MCP_ALLOW_NULL_ORIGIN_CONSENT escape', 'readme_raw_len': 11461, 'one_line_zh': '🐍 ☁️ 🍎 🪟 🐧 - Google Workspace 集成，覆盖 Calendar、Drive、Gmail、Docs、Forms、Chat、Slides 等', 'one_line_plain': 'Access Google Workspace tools including Calendar, Drive, Gmail, Docs, Forms, Chat, and Slides'}, {'id': 5242, 'key': 'xiaomacoltai-math-modeling-skill', 'kind': 'repo', 'name': 'XiaoMaColtAI/math-modeling-skill', 'one_line': '数学建模技能 - 面向 CUMCM、MCM/ICM 等数学建模竞赛的三阶段工作流：建模分析、Python/MATLAB 编程与 DOCX 论文生成', 'one_line_ok': 1, 'why_bad': None, 'fields': '["Claude 技能"]', 'desc_raw': '数学建模技能 - 面向 CUMCM、MCM/ICM 等数学建模竞赛的三阶段工作流：建模分析、Python/MATLAB 编程与 DOCX 论文生成。包含丰富的算法资源库(优化/预测/评价/图论/机器学习等)、角色指导文档、论文模板和实用工具脚本', 'url': 'https://github.com/XiaoMaColtAI/math-modeling-skill', 'source': 'github', 'source_url': 'https://github.com/XiaoMaColtAI/math-modeling-skill', 'downloads': None, 'stars': 1876, 'installs': None, 'mentions': 1, 'score': 106.72, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-10', 'last_seen': '2026-10-19', 'times_seen': 19, 'icon': 'https://github.com/XiaoMaColtAI.png?size=64', 'official_desc': '数学建模技能 - 面向 CUMCM、MCM/ICM 等数学建模竞赛的三阶段工作流：建模分析、Python/MATLAB 编程与 DOCX 论文生成。包含丰富的算法资源库(优化/预测/评价/图论/机器学习等)、角色指导文档、论文模板和实用工具脚本', 'stars_own': 1876, 'stars_creator': None, 'stars_own_repo': 'XiaoMaColtAI/math-modeling-skill', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': 'npx skills', 'install_src': 'HEAD/README.md', 'install_conf': 'low', 'desc_src': 'github-api', 'readme_body': '🎯 Math Modeling Skill\n\n \n\n**面向数学建模竞赛与建模项目的三阶段工作流**\n\n \n \n\n**关注我**\n\n \n \n \n\n \n\n---\n\n📖 简介\n\n本 Skill 将数学建模任务拆分为 **建模分析 → 代码实现 → 论文撰写** 三个阶段。既可以按顺序完成整道题，也可以只执行其中一个阶段。\n\n当前版本：1.3.0\n\n> 生成的论文仅供参考。论文结构与格式必须以目标竞赛当届官方规则和官方模板为准。\n\n✨ 核心能力\n\n- 🧠 **建模分析**：读题、检查附件、拆分子问题、选择模型、设计求解与验证方案。\n- 💻 **双语言实现**：支持 Python 和 MATLAB，按选中的模型与功能动态检查依赖。\n- 📊 **完整结果输出**：生成结果表格、原始数据图、模型运行过程图和最终结果图，以及必须的总体建模流程图。\n- 🎨 **出版级科学可视化**：先剖析数据和论证目标再选图，提供 Python/MATLAB 统一样式、色觉友好编码、SVG + 300 DPI PNG 导出与成图自检闭环。\n- 🔁 **可复现运行**：记录随机种子、输入文件 SHA-256、运行时与依赖版本、关键参数和唯一复现命令。\n- 🔎 **双引擎论文搜索**：并行调用 OpenAlex 与 AnySearch，按 DOI 或题名交叉核验。\n- 📄 **Word / LaTeX 论文生成**：支持官方模板、嵌套主入口、整篇 LaTeX→DOCX、Word 原生 OMML 公式、真实 PDF 编译、权威资源—源码—产物哈希绑定和完整质量门禁。默认只生成 Word 论文，LaTeX 可选。\n- 🧾 **论文组件规范**：使用“符号、含义、单位”三列符号说明表和标准数学排版，按当届规则组织附录。\n- 🛡️ **阶段内独立质检**：默认只在建模终检、最小可运行结果、编程终检、论文证据大纲和论文终检节点派发只读 Subagent，发现问题立即返工复验。\n- 🤝 **可选 Subagent 协作**：用户可按需启用规则核验、附件盘点、文献与模型调研、算法原型、独立实验、双语言对照或术语核验；默认全部关闭。\n- 🧩 **渐进式加载**：只读取当前阶段需要的角色规范、算法资料和工具说明。\n\n🔄 三阶段工作流\n\n \n \n \n\n| 阶段 | 角色 | 核心任务 | 独立门禁 | 固定交付物 |\n|:---:|---|---|---|---|\n| ① | 建模手 | 理解题目、设计模型、定义算法和验证方案 | M1 建模终检 | 题目分析报告.md、术语表格.md |\n| ② | 编程手 | 编写并运行 Python/MATLAB，生成结果与图 | P1 最小可运行结果、P2 编程终检 | 代码、结果表格、三类各至少 3 张且覆盖全部子问题的候选图、至少 1 幅总体建模流程图、results/复现清单.json |\n| ③ | 论文手 | 基于真实结果构建论证并生成 Word 论文 | W1 证据大纲、W2 论文终检 | 至少 8 幅且覆盖全部子问题的正式图；默认交付 完整论文.docx；用户显式要求时同时交付 LaTeX 源码项目、PDF 与哈希清单 |\n\n质检 Subagent 是阶段内只读验收者，不是第四个固定角色。默认只启用固定质检；其他协作仅在用户明确选择后运行。P1 在全量计算和正式出图前执行，W1 在长篇正文和双格式排版前执行；禁止等全流程结束后才首次质检。完整协议见 Subagent 调度与阶段门禁。\n\n阶段反馈\n\n- 编程手发现公式、约束或参数无法实现时，携带实际报错返回建模手修正。\n- 论文手发现关键结论缺少真实结果、图表或文献支撑时，返回对应阶段补齐。\n- 任一独立门禁返回 FAIL 时，由原阶段执行者按证据修正并重新派发复验；主 Agent 不得自行覆盖失败结论。\n- 修正后从被阻断阶段继续，不重复已经通过的阶段。\n\n🚀 快速开始\n\n推荐 Agent\n\n本 Skill 可用于支持本地 Skills 或 Agent 工作流的工具，例如 Claude Code、Codex、Cursor、Trae 和 Qoder。具体加载方式以对应工具的当前文档为准。\n\nDeepSeek Harness 插件\n\n本仓库同时提供 DeepSeek Harness（dsh）的 **Agent 预设**：dsh-plugin/math-modeling-agent/，把三阶段工作流、五门禁质检、任务看板与完成判定封装为 mm_* 工具，供 dsh 桌面端使用。\n\n**安装**：把整个 dsh-plugin/math-modeling-agent/ 目录复制到本机 dsh 预设根目录（ \\.agent-presets\\，Windows 默认 C:\\Users\\ \\AppData\\Roaming\\dsh-desktop\\dsh-home\\.agent-presets），目录名即预设 id（如 math-modeling）。也可直接复制 dsh-plugin/README.md 中附带的安装提示词给 dsh Agent 自动完成安装。\n\n**使用**：新建 dsh 会话 → 选择预设「数学建模 Workbench」，即可使用 mm_project_init / mm_phase_enter / mm_todo / mm_gate / mm_check_deliverables / mm_complete / mm_state 等工具，并通过 skill 工具加载内置 math-modeling 知识库。\n\n> 插件为**自包含**设计：知识库随预设持久化，不依赖外部仓库路径，可整体复制到任意机器使用。\n\n安装\n\nGit 克隆\n\n \n\n克隆后，将仓库放入所用 Agent 的 Skills 目录或按其方式加载本目录。\n\nnpx 安装\n\n \n\n也可以下载仓库 ZIP，解压后放入对应 Skills 目录。\n\n使用示例\n\n完整流程：\n\n \n\n单阶段执行：\n\n \n\n主入口见 SKILL.md。\n\n📁 工作目录约定\n\n- SKILL_ROOT：本仓库根目录，只读；角色规范、算法资料、脚本和模板从这里读取。\n- PROJECT_ROOT：用户题目所在目录；所有运行产物只写入这里。\n- 题目与附件保持只读；需要修改模板时，先复制到 PROJECT_ROOT。\n\n典型产物结构：\n\n \n\n🛠️ 集成工具\n\n| 工具 | 用途 |\n|---|---|\n| 科研可视化 | 数据剖析、选图决策、Nature/SCI 出版级绘制、建模流程图、自检闭环、多格式导出 |\n| 双引擎论文搜索 | OpenAlex + AnySearch 搜索、融合和交叉核验 |\n| DOCX 工具 | 官方模板、递归 LaTeX→DOCX、警告发布门禁、OMML 公式、三线表、修订、批注和校验 |\n| LaTeX 工具 | 环境诊断、官方模板溯源、真实编译、哈希绑定、引用与 PDF 质量校验 |\n| Excel 工具 | XLSX 模板处理、公式重算和错误检查 |\n| PDF 工具 | 读取题目 PDF，提取文本、表格和图片 |\n\n双引擎论文搜索\n\n \n\n- OpenAlex 可通过 --email 提供礼貌池邮箱。\n- AnySearch 需要密钥时设置环境变量 ANYSEARCH_API_KEY。\n- 正式检索默认同时运行两个引擎；单引擎参数只用于诊断。\n\n动态依赖检查\n\nPython 只检查实际需要的功能：\n\n \n\nMATLAB 使用：\n\n \n\n🧮 算法资料\n\n算法资料覆盖七类问题：\n\n| 类别 | 代表方向 |\n|---|---|\n| 优化 | 线性、整数、非线性、多目标和启发式优化 |\n| 预测 | 灰色预测、时间序列、回归和机器学习预测 |\n| 评价 | AHP、TOPSIS、熵权、灰色关联和 DEA |\n| 图论 | 最短路、网络流、生成树和匹配 |\n| 统计 | 检验、聚类、降维和多元统计 |\n| 综合 | 蒙特卡洛、排队、博弈、马尔科夫和微分方程 |\n| 机器学习 | 随机森林、集成学习和异常检测 |\n\n先读取 算法索引，再按问题类型加载对应资料。每道子问题最多使用两个独立模型体系；物理题中同一机理的基础近似与高精度展开按一个模型族计数。\n\n📄 论文生成\n\n默认只生成 Word 论文；用户显式要求时同时生成 LaTeX/PDF 论文。当届官方提交要求仍决定实际可提交的版本。\n\n- **Word**：从官方模板构建，LaTeX 严格转换为原生 OMML 公式，校验篇幅、公式、图表、编号引用、参考文献、DOCX 结构与渲染页数；已有 LaTeX 主稿可用 Pandoc 整篇转换。\n- **LaTeX（可选）**：完整复制官方模板项目并记录哈希，绑定权威代码/图表后真实编译 PDF，校验资源/源码/PDF 哈希、空白页、页面尺寸、字体嵌入与图片 DPI。\n- 没有官方模板时才使用内置构建基线；同时生成两种格式时必须使用相同的数据、图表、公式、参考文献和结论。\n- CUMCM 默认以约 15000 字词单位、约 20 页作为完整度质量目标（非官方要求）；所有竞赛采用相同的至少 8 幅正式图质量基线，每个子问题至少一幅正式结果图。\n\n完整流程与门禁见 tools/docx/SKILL.md、tools/latex/SKILL.md。\n\n📸 示例展示\n\n以下图表展示本项目可视化规范生成的候选图效果。\n\n2025 年国赛 A 题：烟幕干扰弹的投放策略\n\n \n \n \n 投放方案对比与关键参数分析 \n \n \n \n Pareto 前沿与策略效果对比 \n \n\n2025 年国赛 B 题：碳化硅外延层厚度的确定\n\n \n \n \n 厚度拟合结果与方法对比 \n \n \n \n 误差分布与预测一致性分析 \n \n\n🏆 适用场景\n\n工作流可用于 CUMCM、MCM/ICM、APMCM、MathorCup、认证杯、数维杯等数学建模竞赛和一般建模项目。不同竞赛的页面、摘要、编号、页数和提交格式必须按当届官方要求配置。\n\n📂 仓库结构\n\n \n\n✅ 验证\n\n \n\n回归测试覆盖双引擎搜索、公式转换、DOCX、LaTeX 模板与校验、Excel 重算、论文结构、动态依赖、复现清单和科学绘图工具。\n\n📋 版本与更新日志\n\n当前版本：1.3.0\n\n1.3.0 完善算法资料与建模方法论，新增评阅人抓分方法论、求解稳健性、机器学习算法扩充，并补充交付与截止时间保护协议与 DeepSeek Harness 插件支持。详细内容见 CHANGELOG.md。\n\n采用语义化版本 MAJOR.MINOR.PATCH：\n\n- MAJOR：固定交付物、目录契约、命令参数或数据结构发生不兼容变化。\n- MINOR：增加向后兼容的新能力。\n- PATCH：向后兼容的错误修复、文档校正或测试补充。\n\n完整记录见 CHANGELOG.md。\n\n⭐ GitHub Star 历史\n\n \n\n \n\n该图每日读取 GitHub 官方累计 Star 数自动更新；画布为 16:9，纵轴每 50 Star 一格，并在当前数值上方保留一个完整刻度。\n\n \n\n🙏 致谢\n\n- AnySearch Skill：为学术垂直搜索提供参考。\n- Nature Skills：为科学可视化与写作方法提供参考。\n- SciPilot Figure Skill：为数据剖析、图型决策、色觉可达性和成图自检闭环提供参考。\n\n---\n\n \n\n**算法索引 · 使用文档 · 角色说明 · 更新日志**', 'readme_raw_len': 4790, 'one_line_zh': None, 'one_line_plain': None}, {'id': 5749, 'key': 'skillsmp-com-creators-daymade-claude-code-skills-frontend-visual-qa', 'kind': 'skill', 'name': 'frontend-visual-qa', 'one_line': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/daymade/claude-code-skills/frontend-visual-qa', 'downloads': None, 'stars': 1452, 'installs': None, 'mentions': 5, 'score': 503.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/daymade.png?size=64', 'official_desc': 'Audits already-rendered UI and Markdown/Obsidian image layout — web app, deck/slide, dashboard, design-system, or Electron/native app — via real-browser/native-app journeys and a Playwright sweep. Use after UI implementation to find typography, overflow, responsive, routing, data-viz, browser-output', 'stars_own': None, 'stars_creator': 1477, 'stars_own_repo': None, 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'daymade/claude-code-skills', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '审计已渲染的界面，以及 Markdown 与 Obsidian 中的图片排版。', 'one_line_plain': 'The tool audits already-rendered UI and Markdown/Obsidian image layout for web apps, decks, dashboards, and more'}, {'id': 988, 'key': 'softeria-ms-365-mcp-server', 'kind': 'mcp', 'name': 'softeria/ms-365-mcp-server', 'one_line': '📇 ☁️ - Connect to Microsoft Office and the Microsoft 365 suite via the Graph API, including Outlook mail,…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A Model Context Protocol (MCP) server for interacting with Microsoft 365 and Microsoft Office services through the Graph API', 'url': 'https://github.com/softeria/ms-365-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/softeria/ms-365-mcp-server', 'downloads': None, 'stars': 1058, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/softeria.png?size=64', 'official_desc': '📇 ☁️ - Connect to Microsoft Office and the Microsoft 365 suite via the Graph API, including Outlook mail, files, Excel and calendar.', 'stars_own': 1058, 'stars_creator': None, 'stars_own_repo': 'softeria/ms-365-mcp-server', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'ms-365-mcp-server\n\n \n\nMicrosoft 365 MCP Server\n\nA Model Context Protocol (MCP) server for interacting with Microsoft 365 and Microsoft Office services through the Graph\nAPI.\n\nSupported Clouds\n\nThis server supports multiple Microsoft cloud environments:\n\n| Cloud | Description | Auth Endpoint | Graph API Endpoint |\n| -------------------- | ---------------------------------- | ------------------------- | ------------------------------- |\n| **Global** (default) | International Microsoft 365 | login.microsoftonline.com | graph.microsoft.com |\n| **China** (21Vianet) | Microsoft 365 operated by 21Vianet | login.chinacloudapi.cn | microsoftgraph.chinacloudapi.cn |\n\nTo route Graph traffic through a proxy of your own (one that terminates the client\'s plain HTTP and originates TLS to Microsoft), set MS365_MCP_GRAPH_BASE_URL to its base URL; see the environment variable list under CLI Options, including what still bypasses it.\n\nPrerequisites\n\n- Node.js >= 20 (recommended)\n- Node.js 14+ may work with dependency warnings\n\nFeatures\n\n- Authentication via Microsoft Authentication Library (MSAL)\n- Comprehensive Microsoft 365 service integration\n- Read-only mode support for safe operations\n- Tool filtering for granular access control\n- Tool presets and dynamic discovery to shrink the tool surface and token usage\n\nOutput Format: JSON vs TOON\n\nThe server supports two output formats that can be configured globally:\n\nJSON Format (Default)\n\nStandard JSON output with pretty-printing:\n\n \n\n(experimental) TOON Format\n\nToken-Oriented Object Notation for efficient LLM token usage:\n\n \n\n**Benefits:**\n\n- 30-60% fewer tokens vs JSON\n- Best for uniform array data (lists of emails, calendar events, files, etc.)\n- Ideal for cost-sensitive applications at scale\n\n**Usage:**\n(experimental) Enable TOON format globally:\n\nVia CLI flag:\n\n \n\nVia Claude Desktop configuration:\n\n \n\nVia environment variable:\n\n \n\nSupported Services & Tools\n\nThe server provides 300+ tools covering most of the Microsoft Graph API surface. Each tool maps 1-to-1 to a Graph API endpoint and is defined declaratively in src/endpoints.json.\n\nPersonal Account Tools (Available by default)\n\nEmail (Outlook), Calendar, OneDrive Files, Excel, OneNote, To Do Tasks, Planner, Contacts, User Profile, Search\n\nOrganization Account Tools (Requires --org-mode flag)\n\nTeams & Chats, Online Meetings, Transcripts & Recordings, Attendance Reports, SharePoint Sites & Lists, Shared Mailboxes & Calendars, User Management, Presence, Virtual Events\n\nCustom Teams emojis are available in organization mode through list-custom-emojis\nand create-custom-emoji (teams and work presets). These use the Microsoft Graph\nbeta API and request the delegated permissions TeamworkCustomEmoji.Read and\nTeamworkCustomEmoji.Create, respectively. Read-only mode exposes only the list tool.\nExisting deployments may need consent for these new scopes and reauthentication;\nadding tool support does not upgrade an already-issued token.\n\nListing can return contentBytes: null by default. To request base64 PNG/GIF images,\npass select: "displayName,contentBytes" or select: ["displayName", "contentBytes"].\nUse a small top to keep image responses manageable.\nTo create an emoji, pass body: { displayName, contentBytes }\nwith the exact approved name and base64 PNG/GIF file bytes. See Microsoft\'s\nlist\nand create\ncontracts. These tools do not post messages or reactions.\n\nRequired Graph API Permissions\n\nPermissions are requested dynamically based on which tools are enabled. Use --list-permissions to see the exact permissions for your configuration:\n\n \n\nThis is useful for enterprise environments where Graph API permissions must be pre-approved and admin-consented before deploying a new version.\n\nThe --list-permissions JSON includes:\n\n- toolPermissions: permissions implied by the tool surface before --allowed-scopes filtering\n- effectivePermissions: permissions implied by the tools that remain enabled after --allowed-scopes\n- permissions: legacy alias for effectivePermissions, kept for compatibility with existing scripts\n- allowedScopes: the configured scope allowlist, when provided\n- disabledTools: tools hidden because their required Graph scopes are not covered by allowedScopes\n- missingAllowedScopesForTools: unique missing scopes across disabled tools\n- extraAllowedScopesNotUsedByTools: allowed scopes that are not used by the current tool surface\n\nAllowed Scopes\n\nBy default, MSAL requests the scopes implied by the enabled tools, and the tool surface is controlled by --enabled-tools, --preset, --org-mode, and --read-only.\n\nEnterprise and headless deployments can add a scope boundary with --allowed-scopes or MS365_MCP_ALLOWED_SCOPES. When configured, the server first computes the normal tool surface, then hides Graph tools whose required scopes are not covered by the allowlist. OAuth metadata and login flows request only the effective permissions for the tools that remain enabled.\n\n \n\nCLI value takes precedence over MS365_MCP_ALLOWED_SCOPES; if neither is set, the default tool-derived scope behavior is unchanged. Supplying an empty value fails at startup so deployments do not accidentally fall back to a wider tool surface.\n\nScope coverage is hierarchy-aware: for example, Mail.ReadWrite covers tools that require Mail.Read, and Files.ReadWrite.All covers tools that require Files.Read.\n\nSharePoint supports two enterprise permission models:\n\n- Broad tenant scopes such as Sites.Read.All, Sites.ReadWrite.All, and Sites.Manage.All.\n- Microsoft Graph Sites.Selected, where SharePoint site access is granted to the app on specific site collections and Graph evaluates the signed-in user\'s own permissions at request time.\n\nThe default org-mode behavior continues to request the broad SharePoint scopes used by existing deployments. Enterprises that want selected-site SharePoint access can set an allowlist containing Sites.Selected instead of broad Sites.*.All scopes. Direct site/list/item tools that target an explicit SharePoint site, and the /drives/{drive-id}/... item tools (list, get, upload, folder, move/rename, copy, versions) for drives of a granted site, can run with Sites.Selected; tenant-wide SharePoint discovery and search tools still require broad SharePoint scopes.\n\n \n\nIn HTTP mode, OAuth discovery advertises the effective filtered permissions so clients request the same consent surface. On-Behalf-Of mode (--obo) still advertises /access_as_user for protected-resource metadata and requests it on /authorize; --allowed-scopes does not override OBO, it only narrows the tool surface.\n\nRestricting user profile fields\n\nDeployments can set a comma-separated field filter for selected user-directory paths with\n--user-fields or MS365_MCP_USER_FIELDS. It narrows the $select sent to Microsoft Graph\non /users reads and projects responses on the covered paths listed below. This is a\npath-specific data filter, **not a complete Graph authorization boundary**: other Graph\nroutes and resource shapes can expose overlapping profile data.\n\n \n\nThe filter is keyed on known Graph paths rather than tool names. It applies to direct tools,\ndiscovery mode\'s execute-tool, and matching graph-batch subrequests. Batch subrequest URLs\nare rewritten on covered /users reads, and responses are projected on all covered profile\npaths. The byte-passthrough tools (download-bytes, download-bytes-to-file,\nget-download-url) return responses verbatim and refuse targets matching the covered user\nprofile paths while the filter is active.\n\nTwo levels of enforcement apply, depending on what Graph returns:\n\n| Path | Enforcement |\n| ------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| /users, /users/{id} | $select is narrowed', 'readme_raw_len': 50658, 'one_line_zh': '📇 ☁️ - 通过 Graph API 连接 Microsoft Office 和 Microsoft 365 套件，包括 Outlook 邮件、…', 'one_line_plain': 'Connect to Microsoft Office and the Microsoft 365 suite via the Graph API, including Outlook mail'}, {'id': 1170, 'key': 'xing5-mcp-google-sheets', 'kind': 'mcp', 'name': 'xing5/mcp-google-sheets', 'one_line': '🐍 ☁️ - Create, read, update and manage spreadsheets through the Google Sheets API', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'This MCP server integrates with your Google Drive and Google Sheets, to enable creating and modifying spreadsheets.', 'url': 'https://github.com/xing5/mcp-google-sheets', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/xing5/mcp-google-sheets', 'downloads': None, 'stars': 1012, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/xing5.png?size=64', 'official_desc': '🐍 ☁️ - Create, read, update and manage spreadsheets through the Google Sheets API.', 'stars_own': 1012, 'stars_creator': None, 'stars_own_repo': 'xing5/mcp-google-sheets', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'mcp-google-sheets \n\n \n \n Your AI Assistant\'s Gateway to Google Sheets! 📊\n \n\n \n \n \n \n \n\n---\n\n🤔 What is this?\n\nmcp-google-sheets is a Python-based MCP server that acts as a bridge between any MCP-compatible client (like Claude Desktop) and the Google Sheets API. It allows you to interact with your Google Spreadsheets using a defined set of tools, enabling powerful automation and data manipulation workflows driven by AI.\n\n---\n\n🚀 Quick Start (Using uvx)\n\nEssentially the server runs in one line: uvx mcp-google-sheets@latest. \n\nThis command will automatically download the latest code and run it. **We recommend always using @latest** to ensure you have the newest version with the latest features and bug fixes.\n\n_Refer to the ID Reference Guide for more information about the IDs used below._\n\n1. **☁️ Prerequisite: Google Cloud Setup**\n * You **must** configure Google Cloud Platform credentials and enable the necessary APIs first. We strongly recommend using a **Service Account**.\n * ➡️ Jump to the **Detailed Google Cloud Platform Setup** guide below.\n\n2. **🐍 Install uv**\n * uvx is part of uv, a fast Python package installer and resolver. Install it if you haven\'t already:\n \n *Follow instructions in the installer output to add uv to your PATH if needed.*\n\n3. **🔑 Set Essential Environment Variables (Service Account Recommended)**\n * You need to tell the server how to authenticate. Set these variables in your terminal:\n * **(Linux/macOS)**\n \n * **(Windows CMD)**\n \n * **(Windows PowerShell)**\n \n * ➡️ See **Detailed Authentication & Environment Variables** for other options (OAuth, CREDENTIALS_CONFIG).\n\n4. **🏃 Run the Server!**\n * uvx will automatically download and run the latest version of mcp-google-sheets:\n \n * The server will start and print logs indicating it\'s ready.\n * \n * > **💡 Pro Tip:** Always use @latest to ensure you get the newest version with bug fixes and features. Without @latest, uvx may use a cached older version.\n\n5. **🔌 Connect your MCP Client**\n * Configure your client (e.g., Claude Desktop) to connect to the running server.\n * Depending on the client you use, you might not need step 4 because the client can launch the server for you. But it\'s a good practice to test run step 4 anyway to make sure things are set up properly.\n * ➡️ See **Usage with Claude Desktop** for examples.\n\n6. **⚡ Optional: Enable Tool Filtering (Reduce Context Usage)**\n * By default, all 19 tools are enabled (~13K tokens). To reduce context usage, enable only the tools you need.\n * ➡️ See **Tool Filtering** for details.\n\nYou\'re ready! Start issuing commands via your MCP client.\n\n---\n\n✨ Key Features\n\n* **Seamless Integration:** Connects directly to Google Drive & Google Sheets APIs.\n* **Comprehensive Tools:** Offers a wide range of operations (CRUD, listing, batching, sharing, formatting, etc.).\n* **Flexible Authentication:** Supports **Service Accounts (recommended)**, OAuth 2.0, and direct credential injection via environment variables.\n* **Easy Deployment:** Run instantly with uvx (zero-install feel) or clone for development using uv.\n* **AI-Ready:** Designed for use with MCP-compatible clients, enabling natural language spreadsheet interaction.\n* **Tool Filtering:** Reduce context window usage by enabling only the tools you need with --include-tools or ENABLED_TOOLS environment variable.\n\n---\n\n🎯 Tool Filtering (Reduce Context Usage)\n\n**Problem:** By default, this MCP server exposes all 19 tools, consuming ~13,000 tokens before any conversation begins. If you only need a few tools, this wastes valuable context window space.\n\n**Solution:** Use tool filtering to enable only the tools you actually use.\n\nHow to Enable Tool Filtering\n\nYou can filter tools using either:\n\n1. **Command-line argument** --include-tools:\n \n\n2. **Environment variable** ENABLED_TOOLS:\n \n\nAvailable Tool Names\n\nWhen filtering, use these exact tool names (comma-separated, no spaces):\n\n**Most Common Tools (recommended subset):**\n- get_sheet_data - Read from spreadsheets\n- update_cells - Write to spreadsheets\n- list_spreadsheets - Find spreadsheets\n- list_sheets - Navigate tabs\n\n**All Available Tools:**\n- add_columns\n- add_rows\n- batch_update\n- batch_update_cells\n- copy_sheet\n- create_sheet\n- create_spreadsheet\n- find_in_spreadsheet\n- get_multiple_sheet_data\n- get_multiple_spreadsheet_summary\n- get_sheet_data\n- get_sheet_formulas\n- list_folders\n- list_sheets\n- list_spreadsheets\n- rename_sheet\n- search_spreadsheets\n- share_spreadsheet\n- update_cells\n\n**Note:** If neither --include-tools nor ENABLED_TOOLS is specified, all tools are enabled (default behavior).\n\n---\n\n🛠️ Available Tools & Resources\n\nThis server exposes the following tools for interacting with Google Sheets:\n\n_Refer to the ID Reference Guide for more information about the IDs used below._\n\n*(Input parameters are typically strings unless otherwise specified)*\n\n* **list_spreadsheets**: Lists spreadsheets in the configured Drive folder (Service Account) or accessible by the user (OAuth).\n * folder_id (optional string): Google Drive folder ID to search in. Get from its URL. If omitted, uses the configured default folder or searches \'My Drive\'.\n * _Returns:_ List of objects [{id: string, title: string}]\n* **create_spreadsheet**: Creates a new spreadsheet.\n * title (string): The desired title for the spreadsheet. Example: "Quarterly Report Q4".\n * folder_id (optional string): Google Drive folder ID where the spreadsheet should be created. Get from its URL. If omitted, uses configured default or root.\n * _Returns:_ Object with spreadsheet info, including spreadsheetId, title, and folder.\n* **get_sheet_data**: Reads data from a range in a sheet/tab.\n * spreadsheet_id (string): The spreadsheet ID (from its URL).\n * sheet (string): Name of the sheet/tab (e.g., "Sheet1").\n * range (optional string): A1 notation (e.g., \'A1:C10\', \'Sheet1!B2:D\'). If omitted, reads the whole sheet/tab specified by sheet.\n * include_grid_data (optional boolean, default False): If True, returns full grid data including formatting and metadata (much larger). If False, returns values only (more efficient).\n * _Returns:_ If include_grid_data=True, full grid data with metadata (get response). If False, a values result object from the Values API (values.get response).\n* **get_sheet_formulas**: Reads formulas from a range in a sheet/tab.\n * spreadsheet_id (string): The spreadsheet ID (from its URL).\n * sheet (string): Name of the sheet/tab (e.g., "Sheet1").\n * range (optional string): A1 notation (e.g., \'A1:C10\', \'Sheet1!B2:D\'). If omitted, reads all formulas in the sheet/tab specified by sheet.\n * _Returns:_ 2D array of cell formulas (array of arrays) (values.get response).\n* **update_cells**: Writes data to a specific range. Overwrites existing data.\n * spreadsheet_id (string): The spreadsheet ID (from its URL).\n * sheet (string): Name of the sheet/tab (e.g., "Sheet1").\n * range (string): A1 notation range to write to (e.g., \'A1:C3\').\n * data (array of arrays): 2D array of values to write. Example: [[1, 2, 3], ["a", "b", "c"]].\n * _Returns:_ Update result object (values.update response).\n* **batch_update_cells**: Updates multiple ranges in one API call.\n * spreadsheet_id (string): The spreadsheet ID (from its URL).\n * sheet (string): Name of the sheet/tab (e.g., "Sheet1").\n * ranges (object): Dictionary mapping range strings (A1 notation) to 2D arrays of values. Example: { "A1:B2": [[1, 2], [3, 4]], "D5": [["Hello"]] }.\n * _Returns:_ Result of the operation (values.batchUpdate response).\n* **add_rows**: Adds (inserts) empty rows to a sheet/tab at a specified index.\n * spreadsheet_id (string): The spreadsheet ID (from its URL).\n * sheet (string): Name of the sheet/tab (e.g., "Sheet1").\n * count (integer): Number of empty rows to insert.\n * start_row (optional integer, default 0): 0-based row index to start inserting rows. If omitted, defaults to 0 (inserts at the beginning).\n * _Returns:_ Result of the operation (batchUpdate response).\n* **list_sheets**: Lists all sheet/tab n', 'readme_raw_len': 23184, 'one_line_zh': '通过 Google Sheets API 创建、读取、更新和管理电子表格', 'one_line_plain': 'Create, read, update, and manage spreadsheets via the Google Sheets API'}, {'id': 743, 'key': 'aymericzip-intlayer', 'kind': 'mcp', 'name': 'aymericzip/intlayer', 'one_line': '📇 ☁️ 🏠 - IDE assistance for the Intlayer i18n/CMS tool: CLI access and documentation', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Per-component Internationalisation solution for JS application. Type-Safe. Translate with AI. Edit Visually.', 'url': 'https://github.com/aymericzip/intlayer', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/aymericzip/intlayer', 'downloads': None, 'stars': 855, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/aymericzip.png?size=64', 'official_desc': '📇 ☁️ 🏠 - IDE assistance for the Intlayer i18n/CMS tool: CLI access and documentation.', 'stars_own': 855, 'stars_creator': None, 'stars_own_repo': 'aymericzip/intlayer', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Per-component i18n \n \n\n \n AI-powered translation. Visual Editor. Multilingual CMS. \n \n\n \n\n \n Docs •\n Next.js •\n React + Vite •\n CMS •\n Discord \n \n \n \n \n \n \n \n \n \n\n \n\n \n \n \n\nWhat is Intlayer?\n\nMost i18n libraries are either too complex, too rigid, or not built for modern frameworks.\n\nIntlayer is a **modern i18n solution** for web and mobile apps. \nIt’s framework-agnostic, **AI-powered**, and includes a free **CMS & visual editor**.\n\nWith **per-locale content files**, **TypeScript autocompletion**, **tree-shakable dictionaries**, and **CI/CD integration**, Intlayer makes internationalization **faster, cleaner, and smarter**.\n\nKeys benefits of Intlayer:\n\n| Feature | Description |\n| --------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| | **Cross-Frameworks Support** Intlayer is compatible with all major frameworks and libraries, including Next.js, React, Vite, Vue.js, Nuxt, Preact, Express, and more. |\n| | **JavaScript-Powered Content Management** Harness the flexibility of JavaScript to define and manage your content efficiently. - Content declaration |\n| | **Per-Locale Content Declaration File** Speed up your development by declaring your content once, before auto generation. - Per-Locale Content Declaration File |\n| | **Compiler** The Intlayer Compiler extract automatically the content from the components and generate the dictionary files. - Compiler |\n| | **Type-Safe Environment** Leverage TypeScript to ensure your content definitions and code are error-free, while also benefiting from IDE autocompletion. - TypeScript configuration |\n| | **Simplified Setup** Get up and running quickly with minimal configuration. Adjust settings for internationalization, routing, AI, build, and content handling with ease. - Explore Next.js integration |\n| | **Simplified Content Retrieval** No need to call your t function for each piece of content. Retrieve all your content directly using a single hook. - React integration |\n| | **Consistent Server Component Implementation** Perfectly suited for Next.js server components, use the same implementation for both client and server components, no need to pass your t function across each server component. - Server Components |\n| | **Organized Codebase** Keep your codebase more organized: 1 component = 1 dictionary in the same folder. Translations close to their respective components, enhance maintainability and clarity. - How Intlayer works |\n| | **Enhanced Routing** Full support of app routing, adapting seamlessly to complex application structures, for Next.js, React, Vite, Vue.js, etc. - Explore Next.js integration |\n| | **Markdown Support** Import and interpret, locale files and remote Markdown for multilingual content like privacy policies, documentation, etc. Interpret and make Markdown metadata accessible in your code. - Content files |\n| | **Free Visual Editor & CMS** A free visual editor and CMS are available for content writers, removing the need for a localization platform. Keep your content synchronized using Git, or externalize it totally or partially with the CMS. - Intlayer Editor - Intlayer CMS |\n| | **Tree-shakable Content** Tree-shakable content, reducing the size of the final bundle. Loads content per component, excluding any unused content from your bundle. Supports lazy loading to enhance app loading efficiency. - App build optimization |\n| | **Static Rendering** Doesn't block Static Rendering. - Next.js integration |\n| | **AI-Powered Translation** Transform your website into 231 languages with just one click using Intlayer's advanced AI-powered translation tools using your own AI provider / API key. - CI/CD integration - Intlayer CLI - Auto fill |\n| | **MCP Server Integration** Provides an MCP (Model Context Protocol) server for IDE automation, enabling seamless content management and i18n workflows directly within your development environment. - MCP Server |\n| | **VSCode Extension** Intlayer provides a VSCode extension to help you manage your content and translations, building your dictionaries, translating your content, and more. - VSCode Extension |\n| | **Interoperability** Allow interoperability with react-i18next, next-i18next, next-intl, react-intl, vue-i18n. - Intlayer and react-intl - Intlayer and next-intl - Intlayer and next-i18next - Intlayer and vue-i18n - Intlayer compat adapters |\n| | **Performances & Benchmark** Uses advanced tree-shaking and dynamic loading to boost performances and keep the solution as light as possible. - Performances & Benchmark |\n\n📦 Installation\n\nStart your journey with Intlayer today and experience a smoother, more powerful approach to internationalization.\n\n \n \n \n\n \n\n⚡ Quick Start (Next.js)\n\n \n\n \n\n \n\n Get the full guide → \n\n🎥 Live tutorial on YouTube\n\n \n\n \n \n \n\nTable of Contents\n\nExplore our comprehensive documentation to get started with Intlayer and learn how to integrate it into your projects.\n\n \n 📘 Get Started \n \n Why Intlayer? \n Introduction \n \n \n\n \n ⚙️ Concept \n \n How Intlayer Works \n Configuration \n Intlayer CLI \n Compiler \n\n Intlayer Editor \n Intlayer CMS \n Dictionary \n \n Per-Locale Content Declaration File \n Translation \n Enumeration \n Condition \n Nesting \n Markdown \n Function Fetching \n Insertion \n File \n \n \n \n \n\n \n 🌐 Environment \n \n Intlayer with Next.js 16 \n \n Next.js 15 \n Next.js 14 (App Router) \n Next.js Page Router \n Next.js using Compiler \n \n \n React CRA \n Vite + React \n Vite + React using Compiler \n React-router-v7 \n Remix 3 \n Tanstack start \n \n Solid \n \n \n Astro \n \n React \n Vue \n Svelte \n Solid \n Lit \n \n \n\n React Native \n Vite + Svelte \n SvelteKit \n Vite + Preact \n Vite + Vue \n Vite + Nuxt \n Vite + Solid \n Solid Start \n Angular \n \n Backend \n \n Express \n NestJS \n Fastify \n AdonisJS \n Hono \n Elysia \n \n\n \n \n \n\n \n 📊 Benchmark \n \n Next.js \n TanStack Start \n Vue \n Solid \n Svelte \n \n \n\n \n 📰 Blog \n \n What is i18n \n i18n and SEO \n Intlayer and i18next \n Intlayer and react-intl \n Intlayer and next-intl \n \n \n\nMultilingual content management system\n\nMore than an i18n library, Intlayer is a complete **multilingual content management system**. A full CMS is available for free at app.intlayer.org.\n\nIntlayer connects **developers**, **copywriters**, and **AI agents** in one workflow for creating and maintaining multilingual websites effortlessly.Intlayer replaces the following stack in a single solution:\n\n- i18n solutions (e.g. i18next, next-intl, vue-i18n)\n- TMSs (Translation Management Systems) (e.g. Crowdin, Phrase, Lokalise)\n- Feature flags\n- Headless CMSs (e.g. Contentful, Strapi, Sanity)\n\n \n\n🌐 Readme in other languages\n\n \n English •\n 简体中文 •\n Русский •\n 日本語 •\n Français •\n 한국어 •\n Español •\n Deutsch •\n العربية •\n Italiano •\n English (UK) •\n Português •\n हिन्दी •\n Türkçe \n \n\n🤝 Community\n\nIntlayer is built with and for the community and we’d love your input!\n\n- Have a suggestion? Open an issue\n- Found a bug or improvement? Submit a PR\n- Need help or want to connect? Join our Discord\n\nYou can also follow us on :\n\n \n \n \n \n \n \n \n \n \n \n \n \n\nContribution\n\nFor more detailed guidelines on contributing to this project, please refer to the CONTRIBUTING.md file. It contains essential information on our development process, commit message conventions, and release procedures. Your contributions are valuable to us, and we appreciate your efforts in making this project better!\n\nContribute on GitHub, GitLab, or Bitbucket.\n\nThank You for the Support\n\nIf yo", 'readme_raw_len': 8106, 'one_line_zh': '📇 ☁️ 🏠 - 为 Intlayer i18n/CMS 工具提供 IDE 辅助：CLI 访问与文档', 'one_line_plain': 'Access IDE assistance for the Intlayer i18n/CMS tool via CLI and documentation'}, {'id': 496, 'key': 'co-browser-browser-use-mcp-server', 'kind': 'mcp', 'name': 'co-browser/browser-use-mcp-server', 'one_line': '🐍 - Browser automation with browser-use, including a Dockerfile to run Chromium in Docker with a VNC server', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/kontext-security/browser-use-mcp-server', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/co-browser/browser-use-mcp-server', 'downloads': None, 'stars': 847, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/co-browser.png?size=64', 'official_desc': 'browser-use-mcp-server An MCP server that enables AI agents to control web browsers using browser-use.** Want to Vibe Browse the Web?** Open-source AI-powered web browser - **Vibe Browser**.', 'stars_own': 847, 'stars_creator': None, 'stars_own_repo': 'kontext-security/browser-use-mcp-server', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'browser-use-mcp-server\n\n \n\n \n \n \n\n**An MCP server that enables AI agents to control web browsers using\nbrowser-use.**\n\n> **🌐 Want to Vibe Browse the Web?** Open-source AI-powered web browser - **Vibe Browser**.\n>\n> **🔗 Managing multiple MCP servers?** Simplify your development workflow with agent-browser\n\n \n\nPrerequisites\n\n- uv - Fast Python package manager\n- Playwright - Browser automation\n- mcp-proxy - Required for stdio mode\n\n \n\nEnvironment\n\nCreate a .env file:\n\n \n\nInstallation\n\n \n\nUsage\n\nSSE Mode\n\n \n\nstdio Mode\n\n \n\nClient Configuration\n\nSSE Mode Client Configuration\n\n \n\nstdio Mode Client Configuration\n\n \n\nConfig Locations\n\n| Client | Configuration Path |\n| ---------------- | ----------------------------------------------------------------- |\n| Cursor | ./.cursor/mcp.json |\n| Windsurf | ~/.codeium/windsurf/mcp_config.json |\n| Claude (Mac) | ~/Library/Application Support/Claude/claude_desktop_config.json |\n| Claude (Windows) | %APPDATA%\\Claude\\claude_desktop_config.json |\n\nFeatures\n\n- [x] **Browser Automation**: Control browsers through AI agents\n- [x] **Dual Transport**: Support for both SSE and stdio protocols\n- [x] **VNC Streaming**: Watch browser automation in real-time\n- [x] **Async Tasks**: Execute browser operations asynchronously\n\nLocal Development\n\nTo develop and test the package locally:\n\n1. Build a distributable wheel:\n\n \n\n2. Install it as a global tool:\n\n \n\n3. Run from any directory:\n\n \n\n4. After making changes, rebuild and reinstall:\n \n\nDocker\n\nUsing Docker provides a consistent and isolated environment for running the server.\n\n \n\n*Note: The :ro flag in the volume mount (-v) makes the password file read-only inside the container for added security.*\n\nVNC Viewer\n\n \n\nDefault password: browser-use (unless overridden using the custom password method)\n\n \n \n \n \n \n\nExample\n\nTry asking your AI:\n\n \n\nSupport\n\nFor issues or inquiries: cobrowser.xyz\n\nStar History', 'readme_raw_len': 1898, 'one_line_zh': '🐍 - 基于 browser-use 的浏览器自动化，并含 Dockerfile，可在 Docker 中以 VNC 运行 Chromium', 'one_line_plain': 'Automate browser tasks with browser-use, including a Dockerfile for Chromium in Docker with a VNC server'}, {'id': 5603, 'key': 'skillsmp-com-creators-thedivergentai-game-development-agentic-skills-skills-bevy-testing-p', 'kind': 'skill', 'name': 'bevy-testing-patterns', 'one_line': 'Docs-first stub for Cargo tests around Bevy work', 'one_line_ok': 1, 'why_bad': None, 'fields': '[]', 'desc_raw': '', 'url': 'https://skillsmp.com/creators/thedivergentai/game-development-agentic-skills/skills-bevy-testing-patterns', 'source': 'skillsmp', 'source_url': 'https://skillsmp.com/creators/thedivergentai/game-development-agentic-skills/skills-bevy-testing-patterns', 'downloads': None, 'stars': 834, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/thedivergentai.png?size=64', 'official_desc': 'Docs-first stub for Cargo tests around Bevy work. Godot counterpart: godot-testing-patterns. Not a GdUnit port. Keywords: Bevy, cargo test, Rust, libtest.', 'stars_own': None, 'stars_creator': 838, 'stars_own_repo': None, 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': 'thedivergentai/game-development-agentic-skills', 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'none', 'readme_body': None, 'readme_raw_len': None, 'one_line_zh': '面向 Bevy 开发、文档优先的 Cargo 测试桩', 'one_line_plain': 'Add a docs-first stub for writing Cargo tests involving Bevy work'}, {'id': 1658, 'key': 'vercel-next-devtools-mcp', 'kind': 'mcp', 'name': 'vercel/next-devtools-mcp', 'one_line': '🎖️ 📇 🏠 - Next.js runtime diagnostics, route inspection, dev server logs, docs search and upgrade guides for…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Next.js Development for Coding Agent', 'url': 'https://github.com/vercel/next-devtools-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/vercel/next-devtools-mcp', 'downloads': None, 'stars': 831, 'installs': None, 'mentions': 10, 'score': 1003.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/vercel.png?size=64', 'official_desc': '🎖️ 📇 🏠 - Next.js runtime diagnostics, route inspection, dev server logs, docs search and upgrade guides for coding agents.', 'stars_own': 831, 'stars_creator': None, 'stars_own_repo': 'vercel/next-devtools-mcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "Next.js DevTools MCP\n\n \n\nnext-devtools-mcp is a Model Context Protocol (MCP) server that connects coding agents like Claude and Cursor to your running Next.js dev server.\n\nIt is a **thin connector**. It discovers running Next.js 16+ dev servers and proxies their built-in MCP endpoint (/_next/mcp) so agents get live runtime errors, routes, and logs. It also ships two **gateways** that point agents at tools they run directly: version-accurate docs and the agent-browser CLI.\n\n> [!NOTE]\n> Docs and migration workflows no longer live in this server. Next.js bundles its own docs at node_modules/next/dist/docs/, and upgrade / Cache Components workflows are distributed as agent skills. See Migrating from 0.3.x.\n\nRequirements\n\n- Node.js v20.19 or a newer LTS version\n- npm or pnpm\n- Next.js 16+ with a running dev server (for nextjs_index / nextjs_call)\n\nInstall\n\nInstall for all your coding agents with add-mcp:\n\n \n\nAdd -y to skip the prompt and install to all detected agents. Add -g to install globally across all projects.\n\nOr add the config to your MCP client manually:\n\n \n\n> [!NOTE]\n> next-devtools-mcp@latest keeps your client on the latest version.\n\nClient-specific setup\n\n \n Amp \n\n \n\nOr follow Amp's MCP docs with the config above.\n\n \n\n \n Claude Code \n\n \n\nOr edit your MCP settings file with the config above.\n\n \n\n \n Codex \n\n \n\n**Windows 11:** add environment variables and a longer startup timeout to .codex/config.toml:\n\n \n\n \n\n \n Cursor \n\nInstall in Cursor\n\nOr go to Cursor Settings → MCP → New MCP Server and use the config above.\n\n \n\n \n Gemini \n\n \n\n \n\n \n Google Antigravity \n\nAdd to .gemini/antigravity/mcp_config.json:\n\n \n\nSee the Antigravity MCP docs.\n\n \n\n \n VS Code / Copilot \n\n \n\nOr follow the official VS Code MCP setup guide.\n\n \n\n \n Warp \n\nSettings | AI | Manage MCP Servers → + Add:\n- Name: next-devtools\n- Command: npx\n- Arguments: -y, next-devtools-mcp@latest\n\n \n\nQuick Start\n\nStart your Next.js dev server:\n\n \n\nNext.js 16+ enables its MCP endpoint by default at http://localhost:3000/_next/mcp. next-devtools-mcp discovers and connects to it automatically — no config needed.\n\nThen ask your agent about the running app:\n\n \n\nThe agent calls nextjs_index to discover servers, then nextjs_call to query their real state.\n\nTools\n\n| Tool | What it does |\n| -------------- | ----------------------------------------------------------------------------------------- |\n| nextjs_index | Discover running Next.js dev servers and list each one's runtime MCP tools. |\n| nextjs_call | Call a runtime tool on a discovered server (errors, routes, logs, Server Actions). |\n| nextjs_docs | **Gateway.** Point the agent at version-accurate docs in node_modules/next/dist/docs/. |\n| browser_eval | **Gateway.** Point the agent at the agent-browser CLI for browser automation. |\n\nThe gateways do not do the work themselves — they tell the agent where the docs are or how to install/run the CLI, and the agent runs it directly (faster than proxying through MCP).\n\n \n nextjs_index — discover servers \n\nScans common ports for running Next.js 16+ dev servers and lists each server's built-in runtime tools at /_next/mcp. No parameters.\n\nRuntime tools exposed by Next.js (varies by version):\n- get_errors — current build, runtime, and type errors\n- get_logs — path to the dev log file (browser console + server output)\n- get_page_metadata — routes, pages, component metadata\n- get_project_metadata — project structure, config, dev server URL\n- get_server_action_by_id — resolve a Server Action ID to its source file\n\nOutput: JSON listing discovered servers (port, PID, URL) and their tools.\n\n \n\n \n nextjs_call — run a runtime tool \n\nCalls one runtime tool on a discovered server. Run nextjs_index first to find the port and tool name.\n\nInput:\n- port (required) — dev server port\n- toolName (required) — runtime tool to invoke\n- args (optional) — arguments object, only if the tool requires them\n\n \n\nOutput: JSON with the tool's result. Upstream requests, including response-body reads, have a 60-second deadline. Cancelling an MCP request aborts its upstream network work; discovery also releases response bodies used only to detect the protocol.\n\n \n\n \n nextjs_docs — find version-accurate docs \n\nDoes **not** fetch docs. Recent Next.js releases bundle their docs (markdown, matching your installed version) at node_modules/next/dist/docs/. This tool checks for those files before returning reading instructions. If an installed release has no bundled docs (including early 16.x releases), it offers https://nextjs.org/docs as a fallback and asks the agent to verify APIs against the installed version. Missing dependencies receive installation guidance instead. On Next.js below 16, it recommends npx @next/codemod@latest upgrade latest.\n\nInput: topic (optional), project_path (optional, defaults to cwd). The tool resolves next/package.json from that project, including hoisted workspace dependencies, and returns an absolute docsPath for the installed package.\n\n \n\n \n browser_eval — set up browser automation \n\nDoes **not** drive the browser. It detects whether agent-browser is installed and returns either the entry point (agent-browser skills get core --full) or the install steps (npm install -g agent-browser, then agent-browser install), so the agent runs the CLI directly.\n\nInput: task (optional) — used only to tailor the guidance.\n\n \n\nMigrating from 0.3.x\n\nStarting in 0.4.0, next-devtools-mcp is a thin connector.\n\n**Changed:**\n- nextjs_docs no longer fetches docs over the network. It points the agent at the docs Next.js bundles at node_modules/next/dist/docs/ (or recommends upgrading). The nextjs-docs://llms-index resource is removed.\n\n**Removed:**\n- init tool — it only enforced the old docs-fetch workflow.\n- upgrade_nextjs_16 and enable_cache_components tools and their prompts — now distributed as agent skills.\n- All cache-components://, nextjs16://, and nextjs-fundamentals:// resources — superseded by the bundled docs.\n\nWhat remains: nextjs_index, nextjs_call, nextjs_docs, and browser_eval.\n\nPrivacy & Telemetry\n\nnext-devtools-mcp collects anonymous usage telemetry to improve the tool:\n\n- **Tool usage** — which MCP tools are invoked (e.g. nextjs_index, nextjs_call)\n- **Error events** — anonymous error messages when tools fail\n- **Session metadata** — session ID, timestamps, basic environment (OS, Node.js version)\n\n**Not collected:** your code, file contents or paths, personal data, credentials, or tool arguments (only tool names).\n\nLocal files live under ~/.next-devtools-mcp/ (anonymous telemetry-id, telemetry-salt, and a debug log mcp.log).\n\n**Opt out** by setting the environment variable (add it to ~/.zshrc / ~/.bashrc to persist):\n\n \n\nDelete local telemetry data anytime:\n\n \n\nTroubleshooting\n\n**ERR_MODULE_NOT_FOUND referencing next-devtools-mcp/dist** — clear your npx cache and restart your MCP client. The server reinstalls fresh.\n\n**[error] No server info found** — nextjs_index / nextjs_call need a running Next.js 16+ dev server:\n1. Start it: npm run dev\n2. Confirm Next.js 16+ (the /_next/mcp endpoint only exists there)\n3. Verify it started without errors\n\nbrowser_eval and nextjs_docs work without a dev server.\n\nLocal Development\n\n \n\nPoint your MCP client at the local build:\n\n \n\nOr with Codex:\n\n \n\nSee the Next.js MCP documentation for how MCP works with Next.js and coding agents.\n\nLicense\n\nMIT", 'readme_raw_len': 7346, 'one_line_zh': 'Next.js 运行时诊断、路由检查、开发服务器日志、文档搜索与升级指南', 'one_line_plain': 'Run Next.js diagnostics, inspect routes, view dev server logs, search docs and find upgrade guides'}, {'id': 1611, 'key': 'sbroenne-mcp-server-excel', 'kind': 'mcp', 'name': 'sbroenne/mcp-server-excel', 'one_line': '', 'one_line_ok': 0, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Automate real Microsoft Excel with AI via MCP Server or CLI — Power Query, DAX, VBA, PivotTables, charts, and 412 operations.', 'url': 'https://github.com/sbroenne/mcp-server-excel', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/sbroenne/mcp-server-excel', 'downloads': None, 'stars': 828, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/sbroenne.png?size=64', 'official_desc': '#️⃣ 🏠 🪟 - Automate the Excel app on Windows: Power Query, DAX, VBA, PivotTables, tables, charts, ranges and formatting.', 'stars_own': 828, 'stars_creator': None, 'stars_own_repo': 'sbroenne/mcp-server-excel', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'ExcelMcp - MCP Server for Microsoft Excel\n\n \n \n\n \n \n\n \n \n \n \n\n**Website** ·\n**Installation** ·\n**Features** ·\n**Troubleshooting** ·\n**2-minute demo**\n\n**Automate real Microsoft Excel with AI.** Excel MCP Server lets GitHub Copilot,\nClaude, ChatGPT, and other agents control Excel through natural-language\nrequests—using either MCP or a token-efficient CLI.\n\nUnlike file-parser tools, ExcelMcp drives the **actual Excel application** through\nits official COM API. It can refresh Power Query, recalculate formulas, evaluate\nDAX, run VBA and Python =PY(), and preserve PivotTables, charts, macros, the\nData Model, and workbook formatting.\n\n**60 MCP tools across 31 feature areas, with 414 operations**, cover end-to-end Excel automation.\n\n> [!IMPORTANT]\n> Requires **Windows**, **Microsoft Excel 2016 or later**, and an interactive\n> desktop. It is not intended for Linux, macOS, or server-side batch processing.\n\n🚀 Get Started\n\n| Use case | Recommended path |\n|---|---|\n| **VS Code** | Install the extension |\n| **Claude Desktop or another MCP client** | Install the MCP Server |\n| **Coding agents (fewer AI tokens)** | Install the CLI |\n| **Not sure which to choose?** | Read the installation overview |\n\nFor **GitHub Copilot CLI**, install either plugin from the\nAwesome Copilot Excel listings:\n\n \n\nClose open Excel workbooks before starting; ExcelMcp requires exclusive access\nwhile automating them.\n\nWhat You Can Automate\n\n- **Data & analytics:**\n Power Query, DAX, Power Pivot, Excel Tables, PivotTables, and data connections.\n- **Cells & workbooks:**\n ranges, formulas, formatting, worksheets, files, calculation, and named ranges.\n- **Charts & visuals:**\n charts, slicers, conditional formatting, screenshots, drawings, and sparklines.\n- **Automation & advanced:**\n VBA, Python in Excel, Goal Seek, scenarios, data tables, windows, and XML Maps.\n\nExplore the capabilities behind all 414 operations.\nCurrent command details come from CLI help or the MCP tool descriptions.\n\nSee It in Action\n\n \n\nAsk in plain language:\n\n- *"Import products.csv with Power Query and load it to the Data Model."*\n- *"Create a PivotTable showing revenue by region, then add a column chart."*\n- *"Use Goal Seek to find the price that produces $100,000 profit."*\n- *"Show me Excel while you work."*\n\nBecause Excel performs the work, you can inspect results live and continue editing\nthe workbook normally.\n\nSee more examples and use cases.\n\nMCP Server or CLI?\n\nBoth entry points expose the same Core commands and behavior, so the choice is\ntechnical. Coding agents can use either one.\n\n| Interface | Works with | Why |\n|---|---|---|\n| **MCP Server** | Any MCP-compatible client | Tool discovery and persistent sessions |\n| **CLI (excelcli)** | Agents that can run terminal commands | Built-in help without loading MCP tool definitions, usually with fewer AI tokens |\n\nThe MCP Server calls the ExcelMcp service in-process. The CLI uses a background\ndaemon so workbook sessions persist across commands.\n\n \n\nRead the architecture or browse the\nMCP Server and\nCLI guides.\n\n⭐ GitHub Star History\n\n \n\n📋 Additional Information\n\nDocumentation ·\nChangelog ·\nContributing ·\nSecurity ·\nPrivacy\n\n**License:** MIT License - see LICENSE file\n\nRelated Projects\n\nOther projects by the author:\n\n- PowerPoint MCP Server — AI-powered PowerPoint automation via MCP, the sister project to this one\n- pytest-skill-engineering — LLM-powered testing framework for AI agents\n- Windows MCP Server — AI-powered Windows automation via MCP\n- OBS Studio MCP Server — AI-powered OBS Studio automation', 'readme_raw_len': 3550, 'one_line_zh': '#️⃣ 🏠 🪟 - 在 Windows 上自动操作 Excel 应用：Power Query、DAX、VBA、数据透视表、表格、图表、单元格区域等……', 'one_line_plain': None}, {'id': 805, 'key': 'tufantunc-ssh-mcp', 'kind': 'mcp', 'name': 'tufantunc/ssh-mcp', 'one_line': '📇 🏠 🐧 🪟 - SSH control for Linux and Windows servers: execute remote shell commands with password or SSH key…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server exposing SSH control for Linux servers via Model Context Protocol.', 'url': 'https://github.com/tufantunc/ssh-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/tufantunc/ssh-mcp', 'downloads': None, 'stars': 816, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/tufantunc.png?size=64', 'official_desc': '📇 🏠 🐧 🪟 - SSH control for Linux and Windows servers: execute remote shell commands with password or SSH key authentication.', 'stars_own': 816, 'stars_creator': None, 'stars_own_repo': 'tufantunc/ssh-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'SSH MCP Server v2\n\n \n \n \n \n \n \n \n \n\n**SSH MCP Server** is a security-first Model Context Protocol server that gives LLM agents controlled SSH access to remote hosts — with command classification, policy-based authorization, human-in-the-loop approval, and full audit logging.\n\n> **The risk this server exists to manage.** Giving an LLM shell access on a remote host puts private data, untrusted input and network egress in one place — Simon Willison\'s "lethal trifecta". Prompt injection has no general fix, so ssh-mcp assumes any command may be attacker-influenced: it classifies before executing, authorizes against a role × host-group matrix, gates destructive work behind approval, and records the decision either way. That narrows the blast radius; it does not remove the risk. Two things stay yours: **never point it at a root account**, and **never set auto approval on a production profile**. SECURITY.md has the full threat model.\n\n---\n\nQuick Start\n\n1. Install\n\n \n\n2. Configure\n\nWithout a config the server still starts, so a client or directory can complete the MCP\nhandshake and read tools/list — but **every tool call is refused** until you configure it,\nwith a message naming the path below. Nothing runs on a host until this step is done.\n\nCreate the config file at the path for your platform:\n\n| Platform | Path |\n|---|---|\n| Linux | ~/.config/ssh-mcp/config.toml (or $XDG_CONFIG_HOME/ssh-mcp/config.toml) |\n| macOS | ~/Library/Application Support/ssh-mcp/config.toml |\n| Windows | %APPDATA%\\ssh-mcp\\config.toml |\n\n \n\n \n\nThe config decides which hosts, roles and policy rules this server honours, so it checks\nthat nobody but you can read it — and treats the two platforms differently, because the\nquestion has a much clearer answer on one of them.\n\n**Linux and macOS: enforced.** The mode check above, on the file *and* the directory —\nwhich is why chmod 700 is in that command, since mkdir -p under the default umask\nleaves the directory 0755. The server refuses to start otherwise. "Only the owner" is\nunambiguous here and chmod is a one-line fix.\n\n**Windows: split by what the ACL actually allows.** There are no mode bits, so the ACL is\nread instead — and read exposure and write exposure are not treated alike, because Windows\nis much clearer about one of them than the other.\n\n| The ACL lets another account… | Default |\n|---|---|\n| only **read** the config | reported, and the server starts |\n| **change** the config | refused |\n| nothing (no ACL at all) | refused — that is full control for everyone |\n| …and if the ACL could not be read | refused, except when icacls is absent or the check timed out |\n\nA config under %APPDATA% inherits access for you, SYSTEM and Administrators and needs\nnothing done to it. One created elsewhere does not: a file under C:\\ inherits *read* for\nevery local account and *modify* for every authenticated one. The message names the two\nicacls commands that fix it either way.\n\nRead exposure is reported rather than refused because that is where Windows is genuinely\nmuddier than POSIX, and refusing over it blocked a config at the documented location\n(#138). Write exposure is refused\nbecause it is not muddy at all: another account being able to rewrite the file that decides\nwhich hosts, roles and approval policy this server honours is an authorization bypass, not\na disclosure.\n\nTwo flags move the whole thing: --strictConfigAcl refuses everything the check objects\nto, read-only grants included; --allowUncheckedConfigAcl reports everything and refuses\nnothing. Neither combination leaves you without an exit, which is the lesson of #138.\n\nExit statuses\n\n| Status | Meaning |\n|---|---|\n| 0 | Clean shutdown |\n| 1 | A defect in the server — printed with a stack trace; please report it |\n| 2 | How it was invoked or configured — printed as a message, no stack |\n\nA supervisor that treats any non-zero status as a failure needs no change. One\nthat matched on 1 to detect a startup problem should match on 2 as well.\n\nStarting with nothing configured is **not** an exit-2 condition, as of the release that\nadded introspection without a config: the server starts so it can be described, and\nrefuses each tool call instead. A supervisor that used a non-zero exit to catch an\nunconfigured deployment should watch for starting unconfigured on stderr, or read\nconfigured from GET /health when running the HTTP transport.\n\n3. Set credentials via environment variables\n\n \n\n4. Connect from your MCP client\n\n**Claude Code:**\n \n\n**Claude Desktop / Cursor / Windsurf:**\n \n\n**Never pass passwords as CLI arguments** — they\'re visible via ps aux. Use env vars, config files, SSH agent, or OS keychain.\n\n---\n\nTools (14)\n\n| Tool | Purpose | readOnly | destructive |\n|------|---------|:--------:|:----------:|\n| list-connections | Discover available hosts and connection status | ✅ | — |\n| list-sessions | List active sessions per host | ✅ | — |\n| open-session | Create a named interactive (stateful) or background session | — | — |\n| close-session | Close a session. A background session\'s command is signalled (INT/TERM/KILL) before its channel is dropped | — | ✅ |\n| read-session-output | Read output from background sessions (e.g., tail -f) | ✅ | — |\n| read-command | Execute allowlisted read-only commands (ls, cat, grep, ...) | ✅ | — |\n| run-command | Execute arbitrary commands (destructive/privileged need approval, unless approvalPolicy = "auto") | — | — |\n| privileged-command | Execute with sudo (needs approval, unless approvalPolicy = "auto") | — | ✅ |\n| sftp-upload | Upload a file via SFTP (replaces the destination unconditionally) | — | ✅ |\n| sftp-download | Download a file via SFTP | ✅ | — |\n| sftp-list | List a remote directory, bounded in entries and bytes | ✅ | — |\n| sftp-upload-file | Stream a local file to the remote host, never through model context | — | ✅ |\n| sftp-download-file | Stream a remote file to local disk, never through model context | — | ✅ |\n| signal-process | Send INT/TERM/KILL to a remote PID | — | ✅ |\n\nStreaming file transfer\n\nsftp-upload/sftp-download move file *contents* through the model\'s context:\nthe text is an argument on the way out and a response on the way back. That is\nwhat you want for a config snippet and exactly what you do not want for a 200 MB\ntarball or anything binary.\n\nThe two also differ on what they do to an existing destination, and the approved\nstring now says which is which. sftp-upload replaces unconditionally and spells\n--overwrite every time, because that is what it always does; sftp-upload-file\nrefuses unless you pass overwrite: true, and only then carries the flag. Since\nsftp-upload takes its content as an argument rather than naming a local file,\nits string also carries --bytes= --sha256= — without that, two\nuploads to the same path are the same string, which means one approval covers\nboth and an auditor cannot tell which set of bytes landed. The digest is 128\nbits rather than a short prefix because that claim has to hold against a caller\nwho picks both payloads, not only against an accidental repeat.\n\n[policy].denylist patterns are tested against that whole string and, for every\nSFTP tool, against the remote path on its own — as given, lexically normalized,\nand read the way Windows reads it (see "Policy Engine"). A rule written for the\npath, such as authorized_keys$, therefore does not depend on where the string\nputs it or on how the file is spelled. A rule anchored on the *whole* string\n(^sftp:upload /root/.*$) still does: that layout is ours to change, so anchor\non the path instead.\n\nsftp-upload-file and sftp-download-file stream between the remote host and\nlocal disk instead. Neither the bytes nor a base64 encoding of them ever reaches\nthe model — the response is a byte count and two paths.\n\nThe two transfer tools are **off until you configure defaults.transferRoot**,\nand refuse with an explanation until then. (sftp-list has no local side and\nneeds none of this.) That directory is the whole of their local reach:\n\n \n\nChecked on every call, a', 'readme_raw_len': 42268, 'one_line_zh': '📇 🏠 🐧 🪟 - 管理 Linux 与 Windows 服务器的 SSH：以密码或 SSH 密钥执行远程 shell 命令…', 'one_line_plain': 'SSH control for Linux and Windows servers: execute remote shell commands with password or SSH key'}, {'id': 1387, 'key': 'ckreiling-mcp-server-docker', 'kind': 'mcp', 'name': 'ckreiling/mcp-server-docker', 'one_line': '🐍 🏠 - Manage Docker containers, images, volumes and networks', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for Docker', 'url': 'https://github.com/ckreiling/mcp-server-docker', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/ckreiling/mcp-server-docker', 'downloads': None, 'stars': 748, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/ckreiling.png?size=64', 'official_desc': '🐍 🏠 - Manage Docker containers, images, volumes and networks.', 'stars_own': 748, 'stars_creator': None, 'stars_own_repo': 'ckreiling/mcp-server-docker', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '🐋 Docker MCP server\n\nAn MCP server for managing Docker with natural language!\n\n🪩 What can it do?\n\n- 🚀 Compose containers with natural language\n- 🔍 Introspect & debug running containers\n- 📀 Manage persistent data with Docker volumes\n\n❓ Who is this for?\n\n- Server administrators: connect to remote Docker engines for e.g. managing a\n public-facing website.\n- Tinkerers: run containers locally and experiment with open-source apps\n supporting Docker.\n- AI enthusiasts: push the limits of that an LLM is capable of!\n\nDemo\n\nA quick demo showing a WordPress deployment using natural language:\n\nhttps://github.com/user-attachments/assets/65e35e67-bce0-4449-af7e-9f4dd773b4b3\n\n🏎️ Quickstart\n\nInstall\n\nClaude Desktop\n\nOn MacOS: ~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\nOn Windows: %APPDATA%/Claude/claude_desktop_config.json\n\n \n Install from PyPi with uv \n\nIf you don\'t have uv installed, follow the installation instructions for your\nsystem:\nlink\n\nThen add the following to your MCP servers file:\n\n \n\n \n\n \n Install with Docker \n\nPurely for convenience, the server can run in a Docker container.\n\nAfter cloning this repository, build the Docker image:\n\n \n\nAnd then add the following to your MCP servers file:\n\n \n\nNote that we mount the Docker socket as a volume; this ensures the MCP server\ncan connect to and control the local Docker daemon.\n\n \n\n📝 Prompts\n\n🎻 docker_compose\n\nUse natural language to compose containers. See above for a demo.\n\nProvide a Project Name, and a description of desired containers, and let the LLM\ndo the rest.\n\nThis prompt instructs the LLM to enter a plan+apply loop. Your interaction\nwith the LLM will involve the following steps:\n\n1. You give the LLM instructions for which containers to bring up\n2. The LLM calculates a concise natural language plan and presents it to you\n3. You either:\n - Apply the plan\n - Provide the LLM feedback, and the LLM recalculates the plan\n\nExamples\n\n- name: nginx, containers: "deploy an nginx container exposing it on port\n 9000"\n- name: wordpress, containers: "deploy a WordPress container and a supporting\n MySQL container, exposing Wordpress on port 9000"\n\nResuming a Project\n\nWhen starting a new chat with this prompt, the LLM will receive the status of\nany containers, volumes, and networks created with the given project name.\n\nThis is mainly useful for cleaning up, in-case you lose a chat that was\nresponsible for many containers.\n\n📔 Resources\n\nThe server exposes resource templates rather than enumerating currently-running containers:\n\n- docker://containers/{container_id}/logs (text/plain)\n- docker://containers/{container_id}/stats (application/json)\n\nRead either URI with a Docker container ID or name.\n\n🔨 Tools\n\nContainers\n\n- list_containers\n- create_container\n- run_container\n- recreate_container\n- start_container\n- fetch_container_logs\n- stop_container\n- remove_container\n\nImages\n\n- list_images\n- pull_image\n- push_image\n- build_image\n- remove_image\n\nNetworks\n\n- list_networks\n- create_network\n- remove_network\n\nVolumes\n\n- list_volumes\n- create_volume\n- remove_volume\n\n🚧 Disclaimers\n\nSensitive Data\n\n**DO NOT CONFIGURE CONTAINERS WITH SENSITIVE DATA.** This includes API keys,\ndatabase passwords, etc.\n\nAny sensitive data exchanged with the LLM is inherently compromised, unless the\nLLM is running on your local machine.\n\nIf you are interested in securely passing secrets to containers, file an issue\non this repository with your use-case.\n\nReviewing Created Containers\n\nBe careful to review the containers that the LLM creates. Docker is not a secure\nsandbox, and therefore the MCP server can potentially impact the host machine\nthrough Docker.\n\nFor safety reasons, this MCP server doesn\'t support sensitive Docker options\nlike --privileged or --cap-add/--cap-drop. If these features are of interest\nto you, file an issue on this repository with your use-case.\n\n🛠️ Configuration\n\nThis server uses the Python Docker SDK\'s from_env method. For configuration\ndetails, see\nthe documentation.\n\nConnect to Docker over SSH\n\nThis MCP server can connect to a remote Docker daemon over SSH.\n\nSimply set a ssh:// host URL in the MCP server definition:\n\n \n\n💻 Development\n\nPrefer using Devbox to configure your development environment. The server uses\nMCP Python SDK v2\'s high-level MCPServer API and can be inspected directly:\n\n \n\nRun the hermetic test and lint suite without a Docker daemon:\n\n \n\nSee the devbox.json for helpful development commands.\n\nAfter setting up devbox you can configure your Claude MCP config to use it:', 'readme_raw_len': 4511, 'one_line_zh': '管理 Docker 容器、镜像、数据卷和网络', 'one_line_plain': 'Manage Docker containers, images, volumes and networks'}, {'id': 1662, 'key': 'vivekvells-mcp-pandoc', 'kind': 'mcp', 'name': 'vivekvells/mcp-pandoc', 'one_line': 'Document format conversion using Pandoc: Markdown, HTML, PDF, DOCX, CSV and more', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'MCP server for document format conversion using pandoc.', 'url': 'https://github.com/vivekVells/mcp-pandoc', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/vivekVells/mcp-pandoc', 'downloads': None, 'stars': 583, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/vivekVells.png?size=64', 'official_desc': 'Document format conversion using Pandoc: Markdown, HTML, PDF, DOCX, CSV and more.', 'stars_own': 583, 'stars_creator': None, 'stars_own_repo': 'vivekVells/mcp-pandoc', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '-->\n \n \n\nmcp-pandoc: A Document Conversion MCP Server\n\n> Officially included in the Model Context Protocol servers open-source project. 🎉\n\nOverview\n\nA Model Context Protocol server for document format conversion using pandoc. This server provides tools to transform content between different document formats while preserving formatting and structure.\n\nPlease note that mcp-pandoc is currently in early development. PDF support is under development, and the functionality and available tools are subject to change and expansion as we continue to improve the server.\n\nCredit: This project uses the Pandoc Python package for document conversion, forming the foundation for this project.\n\n📋 Quick Reference\n\n**New to mcp-pandoc?** Check out **📖 CHEATSHEET.md** for\n\n- ⚡ Copy-paste examples for all formats\n- 🔄 Bidirectional conversion matrix\n- 🎯 Common workflows and pro tips\n- 🌟 Reference document styling guide\n\n_Perfect for quick lookups and getting started fast!_\n\nDemo\n\n \n\n> 🎥 Watch on YouTube\n\n \n Screenshots \n\n \n \n \n \n \n \n\nMore to come...\n\nTools\n\n1. convert-contents\n - Transforms content between supported formats\n - Inputs:\n - contents (string): Source content to convert (required if input_file not provided)\n - input_file (string): Complete path to input file (required if contents not provided)\n - input_format (string): Source format of the content (defaults to markdown)\n - output_format (string): Target format (defaults to markdown)\n - output_file (string): Complete path for output file (required for pdf, docx, rst, latex, epub, odt, pptx formats)\n - reference_doc (string): Path to a reference document to use for styling (supported for docx, odt and pptx output; the file must match the output format)\n - defaults_file (string): Path to a Pandoc defaults file (YAML) containing conversion options\n - filters (array): List of Pandoc filter paths to apply during conversion\n - Supported formats, by direction:\n\n | Format | Read | Write |\n | -------- | :--: | :---: |\n | markdown | ✅ | ✅ |\n | html | ✅ | ✅ |\n | docx | ✅ | ✅ |\n | odt | ✅ | ✅ |\n | rst | ✅ | ✅ |\n | latex | ✅ | ✅ |\n | epub | ✅ | ✅ |\n | ipynb | ✅ | ✅ |\n | txt | ✅ | ✅ |\n | **pdf** | ❌ | ✅ |\n | **pptx** | ❌ | ✅ |\n\n - Note: For advanced formats (pdf, docx, rst, latex, epub, odt, pptx), an output_file path is required\n\n🔧 Advanced Features\n\nDefaults Files (YAML Configuration)\n\nUse defaults files to create reusable conversion templates with consistent formatting:\n\n \n\nExample usage: "Convert paper.md to PDF using defaults academic-paper.yaml and save as paper.pdf"\n\nPandoc Filters\n\nApply custom filters for enhanced processing:\n\nExample usage: "Convert docs.md to HTML with filters [\'/path/to/mermaid-filter.py\'] and save as docs.html"\n\n> 💡 **For comprehensive examples and workflows**, see **CHEATSHEET.md**\n\n📊 Supported Formats & Conversions\n\nBidirectional Conversion Matrix\n\nRows are source formats, columns are targets. PDF and PPTX have no rows because pandoc cannot read them.\n\n| From\\To | MD | HTML | TXT | DOCX | ODT | RST | LaTeX | EPUB | IPYNB | PDF | PPTX |\n| ------------ | --- | ---- | --- | ---- | --- | --- | ----- | ---- | ----- | --- | ---- |\n| **Markdown** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **HTML** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **TXT** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **DOCX** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **ODT** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **RST** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **LaTeX** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **EPUB** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n| **IPYNB** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |\n\nA Note on Write-Only Formats\n\n**PDF** and **PPTX** can be produced but not read.\n\nPandoc has never shipped a PDF reader. Pandoc gained a PowerPoint reader in 3.8.3 (released 2025-12-01), but this project declares no minimum pandoc version, and Ubuntu 24.04 still ships pandoc 3.1.3, so pptx input is not offered. Tracked in #54 and #49.\n\nPPTX **output** needs no particular pandoc version. The PowerPoint writer has existed since pandoc 2.0.5.\n\nFormat Categories\n\n| Category | Formats | Requirements |\n| ------------ | ---------------------------------- | ------------------------------- |\n| **Basic** | MD, HTML, TXT, IPYNB | None, returned inline |\n| **Advanced** | DOCX, ODT, PDF, PPTX, RST, LaTeX, EPUB | Must specify output_file path |\n| **Styled** | DOCX, ODT, PPTX with reference doc | Custom template support ⭐ |\n\nRequirements by Format\n\n- **PDF (.pdf)** - requires TeX Live installation\n- **DOCX (.docx), ODT (.odt), PPTX (.pptx)** - support custom styling via reference documents, and the reference must be the same format as the output\n- **All others** - no additional requirements\n\nNote: For advanced formats:\n\n1. Complete file paths with filename and extension are required\n2. **PDF conversion requires TeX Live installation** (see Critical Requirements section -> For macOS: brew install texlive)\n3. When no output path is specified:\n - Basic formats: Displays converted content in the chat\n - Advanced formats: May save in system temp directory (/tmp/ on Unix systems)\n\nUsage & configuration\n\n**NOTE: Ensure to complete installing required packages mentioned below under "Critical Requirements".**\n\nTo use the published one\n\n \n\n**💡 Quick Start**: See **CHEATSHEET.md** for copy-paste examples and common workflows.\n\n⚠️ Important Notes\n\nCritical Requirements\n\n1. **Pandoc Installation**\n\n- **Required**: Install pandoc - the core document conversion engine\n- Installation:\n\n \n\n- **Verify**: pandoc --version\n\n2. **UV package installation**\n\n- **Required**: Install uv package (includes uvx command)\n- Installation:\n\n \n\n- **Verify**: uvx --version\n\n3. **PDF Conversion Prerequisites:** Only needed if you need to convert & save pdf\n\n- TeX Live must be installed before attempting PDF conversion\n- Installation commands:\n\n \n\n4. **File Path Requirements**\n\n- When saving or converting files, you MUST provide complete file paths including filename and extension\n- The tool does not automatically generate filenames or extensions\n\nExamples\n\n✅ Correct Usage:\n\n \n\n❌ Incorrect Usage:\n\n \n\nCommon Issues and Solutions\n\n1. **PDF Conversion Fails**\n\n - Error: "xelatex not found"\n - Solution: Install TeX Live first (see installation commands above)\n\n2. **File Conversion Fails**\n\n - Error: "Invalid file path"\n - Solution: Provide complete path including filename and extension\n - Example: /path/to/document.pdf instead of just /path/to/\n\n3. **Format Conversion Fails**\n\n - Error: "Unsupported format"\n - Solution: Use only supported formats:\n - Basic: txt, html, markdown\n - Advanced: pdf, docx, rst, latex, epub\n\n4. **Reference Document Issues**\n - Error: "Reference document not found"\n - Solution: Ensure the reference document path exists and is accessible\n - Error: "reference_doc must be a \'.odt\' file when output_format is \'odt\'"\n - Solution: The reference document must be the same format as the output. Pandoc does not check this itself: a mismatched reference is silently ignored for DOCX output, and produces an ODT file that cannot be opened\n - Note: Reference documents work with DOCX, ODT and PPTX output formats\n - How to create: pandoc -o reference.docx --print-default-data-file reference.docx, and likewise with reference.odt or reference.pptx\n\nQuickstart\n\n \n\nInstalling manually via claude_desktop_config.json config file\n\n- On MacOS: open ~/Library/Application\\ Support/Claude/claude_desktop_config.json\n- On Windows: %APPDATA%/Claude/claude_desktop_config.json\n\na) Only for local development & contribution to this repo\n\n \n Development/Unpublished Servers Configuration \n\nℹ️ Replace with your locally cloned project path\n\n \n\n \n\nb) Published Servers Configuration - Consumers should use this config\n\n \n\n \n\n- If you face any issue, use the "Published Servers Configuration" above directly instead of this cli.\n\n**Note**: To use locally configured mcp-pandoc, follow "Development/Unpublished Servers Configuration" step ab', 'readme_raw_len': 9320, 'one_line_zh': '使用 Pandoc 转换文档格式：Markdown、HTML、PDF、DOCX、CSV 等', 'one_line_plain': 'Convert document formats between Markdown, HTML, PDF, DOCX, CSV and more using Pandoc'}, {'id': 1597, 'key': 'quantgeekdev-docker-mcp', 'kind': 'mcp', 'name': 'QuantGeekDev/docker-mcp', 'one_line': '🏎️ 🏠 - Docker container management and operations', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'A docker MCP Server (modelcontextprotocol)', 'url': 'https://github.com/QuantGeekDev/docker-mcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/QuantGeekDev/docker-mcp', 'downloads': None, 'stars': 505, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/QuantGeekDev.png?size=64', 'official_desc': '— A docker MCP Server (modelcontextprotocol) ☆`505`', 'stars_own': 505, 'stars_creator': None, 'stars_own_repo': 'QuantGeekDev/docker-mcp', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': '🐳 docker-mcp\n\n \n \n \n \n\nA powerful Model Context Protocol (MCP) server for Docker operations, enabling seamless container and compose stack management through Claude AI.\n\n✨ Features\n\n- 🚀 Container creation and instantiation\n- 📦 Docker Compose stack deployment\n- 🔍 Container logs retrieval\n- 📊 Container listing and status monitoring\n\n🎬 Demos\nDeploying a Docker Compose Stack\n\nhttps://github.com/user-attachments/assets/b5f6e40a-542b-4a39-ba12-7fdf803ee278\n\nAnalyzing Container Logs\n\nhttps://github.com/user-attachments/assets/da386eea-2fab-4835-82ae-896de955d934\n\n🚀 Quickstart\n\nTo try this in Claude Desktop app, add this to your claude config files:\n \n\nInstalling via Smithery\n\nTo install Docker MCP for Claude Desktop automatically via Smithery:\n\n \n\nPrerequisites\n\n- UV (package manager)\n- Python 3.12+\n- Docker Desktop or Docker Engine\n- Claude Desktop\n\nInstallation\n\nClaude Desktop Configuration\n\nAdd the server configuration to your Claude Desktop config file:\n\n**MacOS**: ~/Library/Application\\ Support/Claude/claude_desktop_config.json \n**Windows**: %APPDATA%/Claude/claude_desktop_config.json\n\n \n 💻 Development Configuration \n\n \n \n\n \n 🚀 Production Configuration \n\n \n \n\n🛠️ Development\n\nLocal Setup\n\n1. Clone the repository:\n \n\n2. Create and activate a virtual environment:\n \n\n3. Install dependencies:\n \n\n🔍 Debugging\n\nLaunch the MCP Inspector for debugging:\n\n \n\nThe Inspector will provide a URL to access the debugging interface.\n\n📝 Available Tools\n\nThe server provides the following tools:\n\ncreate-container\nCreates a standalone Docker container\n \n\ndeploy-compose\nDeploys a Docker Compose stack\n \n\nget-logs\nRetrieves logs from a specific container\n \n\nlist-containers\nLists all Docker containers\n \n\n🚧 Current Limitations\n\n- No built-in environment variable support for containers\n- No volume management\n- No network management\n- No container health checks\n- No container restart policies\n- No container resource limits\n\n🤝 Contributing\n\n1. Fork the repository from docker-mcp\n2. Create your feature branch\n3. Commit your changes\n4. Push to the branch\n5. Open a Pull Request\n\n📜 License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n✨ Authors\n\n- **Alex Andru** - *Initial work | Core contributor* - @QuantGeekDev\n- **Ali Sadykov** - *Initial work | Core contributor* - @md-archive\n\n---\nMade with ❤️', 'readme_raw_len': 2339, 'one_line_zh': '🏎️ 🏠 - Docker 容器管理与运维操作', 'one_line_plain': 'Docker lets you create, start, and stop containers for running apps locally'}, {'id': 2736, 'key': 'shinpr-mcp-local-rag', 'kind': 'mcp', 'name': 'shinpr/mcp-local-rag', 'one_line': '📇 🏠 - Local document search: semantic search over PDF, DOCX, TXT and Markdown files with LanceDB vector…', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': 'Local-first RAG server for developers. Semantic + keyword search for code and technical docs. Works with MCP or CLI. Fully private, zero setup.', 'url': 'https://github.com/shinpr/mcp-local-rag', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/shinpr/mcp-local-rag', 'downloads': None, 'stars': 415, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/shinpr.png?size=64', 'official_desc': '📇 🏠 - Local document search: semantic search over PDF, DOCX, TXT and Markdown files with LanceDB vector storage and local embeddings.', 'stars_own': 415, 'stars_creator': None, 'stars_own_repo': 'shinpr/mcp-local-rag', 'stars_source': 'github_repo_page', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'github-api', 'readme_body': 'MCP Local RAG\n\n \n \n \n \n\n \n English |\n 简体中文 |\n Deutsch |\n Español |\n Português (Brasil) |\n Français \n \n\nSearch private documents from an MCP client or the terminal without sending them to an\nembedding API.\n\nmcp-local-rag indexes PDF, DOCX, Markdown, and text files on your machine. Search combines\nsemantic similarity with keyword matching, so queries can match both intent and exact technical\nterms such as API names, class names, and error codes. Results include source passages and,\nwhere available, headings, line numbers, or page numbers so you can check and cite the original\ndocument.\n\nNo API key, Docker, Python, or external database is required. After the initial model download,\ntext ingestion and search work offline.\n\nQuick Start\n\nRequirements\n\n- Node.js 22 or later\n- Internet access on first use to download the npm package and embedding model\n- A directory containing the documents you want to search\n\nSet BASE_DIR to that directory. It is also the security boundary for file operations. Replace\n/absolute/path/to/your/documents below with the directory\'s absolute path.\n\nUse one of the examples below, or register npx -y mcp-local-rag and set BASE_DIR using your\nclient\'s MCP configuration format.\n\nSet DB_PATH and CACHE_DIR to absolute paths as well. Relative paths resolve from the\nserver\'s working directory, so starting the server from different projects creates a separate\nindex and model cache in each.\n\n \n Claude Code \n\nRun this command:\n\n \n\n \n\n \n Codex \n\nAdd to ~/.codex/config.toml:\n\n \n\n \n\n \n OpenCode \n\nAdd to ~/.config/opencode/opencode.json (or opencode.jsonc):\n\n \n\n \n\n \n Cursor \n\nAdd to ~/.cursor/mcp.json:\n\n \n\n \n\nRestart the client, then ask it to build the index:\n\n \n\nThe first sync downloads the default embedding model (about 90 MB) and may take 1–2 minutes\nbefore ingestion starts. Later runs use the local cache.\n\nOnce the sync completes:\n\n \n\nCLI Quick Start\n\nTo use the CLI without an MCP client:\n\n \n\nThe CLI uses the current directory as its document root by default. Run both commands from the\nsame directory so they use the same default index, or set BASE_DIR and DB_PATH explicitly.\n\nSupported Content\n\n| Input | How to ingest |\n|---|---|\n| PDF, DOCX, TXT, Markdown | File ingestion or directory sync |\n| HTML already fetched by the client | ingest_data |\n| Plain text or Markdown held in memory | ingest_data with a stable source identifier |\n\nHTML fetching is not built into the server. An MCP client can fetch a page and pass its HTML to\ningest_data.\n\nExcel, PowerPoint, standalone images, and source-code file extensions are not supported by file\ningestion. PDFs can optionally use a local vision model to describe figures, but this is not\nOCR or image search.\n\nUsing the Index\n\nSync after adding, editing, or removing documents. For searches and follow-up reading, ask your\nMCP client:\n\n \n\nYou can also ingest a single file or HTML already fetched by the client. To refresh an existing\nentry, ingest the document again using the same path or source identifier. sync skips unchanged\nfiles. MCP file paths must be absolute and inside a configured document root.\n\nPDF section headings may be inaccurate. Check the original page when you need the exact heading.\n\n \n MCP Tools \n\n| Tool | Purpose |\n|---|---|\n| sync_start | Reconcile the index with all configured roots or one path |\n| sync_status | Poll a running sync job |\n| ingest_file | Ingest or replace one file |\n| ingest_data | Ingest text, Markdown, or HTML already held by the client |\n| query_documents | Search with semantic matching and keyword boost |\n| read_chunk_neighbors | Read surrounding chunks from a search result |\n| list_files | Show supported files and their ingestion state |\n| delete_file | Delete an indexed file or an ingest_data item |\n| status | Show index and search status |\n\n \n\nCLI\n\nUse the CLI to update the index, narrow searches, or remove indexed content:\n\n \n\ningest imports the selected files; sync also removes entries for deleted files and skips\nunchanged files. Use --scope to restrict search results to a path prefix, repeating it to\ninclude multiple prefixes.\n\nGlobal options such as --db-path, --cache-dir, and --model-name go before the subcommand.\nSubcommand options go after it:\n\n \n\nRun npx mcp-local-rag --help for the complete command reference.\n\nquery writes its results to stdout as JSON, best match first, so it can be piped into another\ntool. The field-by-field contract is in\ndocs/schema/query-output.schema.json.\n\nIf you move a project and want to keep its index, stop the MCP server and other database\nwriters, move the files and database together without changing the directory layout, then run:\n\n \n\nUpdate BASE_DIR/BASE_DIRS, DB_PATH, and any other moved paths in your MCP configuration,\nthen restart the client.\n\nAgent Skills\n\nAgent Skills provide query and ingestion guidance for AI assistants:\n\n \n\nInstalled skills cover query formulation, result refinement, and HTML ingestion. Ask the\nassistant to use the mcp-local-rag skill explicitly if it does not activate automatically.\n\nAdvanced Options\n\nStart with the defaults. Open the sections below when you need different document roots, better\nresults for your corpus, or searchable PDF figures.\n\n \n Storage and Document Roots \n\nThe MCP server reads environment variables. The CLI accepts the listed variables and flags.\nKeep the same DB_PATH when commands should use the same index.\n\n| Environment Variable | CLI Flag | Default | Description |\n|---------------------|----------|---------|-------------|\n| BASE_DIR | --base-dir | Current directory | One document root; the CLI flag is repeatable on ingest, list, and sync |\n| BASE_DIRS | N/A | (unset) | JSON array of document roots; takes precedence over BASE_DIR |\n| DB_PATH | --db-path | ./lancedb/ | Vector database location |\n| CACHE_DIR | --cache-dir | ./models/ | Model cache directory |\n| HF_ENDPOINT | N/A | https://huggingface.co | Hugging Face model download endpoint; use a mirror URL when direct downloads are blocked |\n| MAX_FILE_SIZE | --max-file-size | 104857600 (100MB) | Maximum file size in bytes |\n\nFile operations stay within configured roots. For multiple directories, set\nBASE_DIRS=\'["/absolute/docs","/absolute/specs"]\' or repeat CLI --base-dir. Precedence: CLI\nroots, BASE_DIRS, BASE_DIR, then the current directory. Only the highest-priority source is\nused; roots from different sources are not merged. Invalid BASE_DIRS is an error. Relative\nDB_PATH and CACHE_DIR are resolved from the working directory.\n\n \n\n \n Models and Search Tuning \n\nChoose an embedding model for your documents’ language and subject. Compare settings using\nquestions you actually ask and check which source passages are returned. The model must support\nmean pooling and L2 normalization, which this tool uses to produce embeddings.\n\n| Environment Variable | CLI Flag | Default | Description |\n|---------------------|----------|---------|-------------|\n| MODEL_NAME | --model-name | Xenova/all-MiniLM-L6-v2 | Hugging Face embedding model |\n| CHUNK_MIN_LENGTH | --chunk-min-length | 50 | Minimum length in characters (1–10000) for ordinary chunks; a fragment of content split to fit the model\'s token limit can be shorter |\n| EMBED_TITLE_PREFIX | N/A | false | Add the document title to each chunk\'s embedding input |\n| EMBED_HEADING_PREFIX | N/A | false | Add the heading hierarchy to each chunk\'s embedding input when it fits |\n| RAG_DEVICE | N/A | cpu | ONNX Runtime execution device |\n| RAG_DTYPE | N/A | fp32 | Embedding dtype passed to the selected model |\n\nBoth prefix options default to false and work independently. Try EMBED_TITLE_PREFIX when a\npassage needs the document’s overall topic, or EMBED_HEADING_PREFIX when it needs its\nsection’s topic. Enabling both is not always better. They affect embeddings, not the returned\ntext or keyword index; heading context is omitted when it would exceed the input budget.\n\nWhen changing embedding models, build a fresh index at a new DB_PATH. Vectors from different\nmodels are not comparable, even wh', 'readme_raw_len': 13904, 'one_line_zh': '本地文档搜索：借助 LanceDB 向量库对 PDF、DOCX、TXT 与 Markdown 文件做语义搜索…', 'one_line_plain': 'Search local documents semantically over PDF, DOCX, TXT and Markdown files with LanceDB vector…'}, {'id': 5614, 'key': 'razorpay-slashdocs', 'kind': 'repo', 'name': 'razorpay/slashdocs', 'one_line': 'Give your agents the power to publish reviewable HTML documents', 'one_line_ok': 1, 'why_bad': None, 'fields': '["开源项目"]', 'desc_raw': 'Give your agents the power to publish reviewable HTML documents. Self-hosted server + CLI + remote MCP; agents push, humans review with comments and versions.', 'url': 'https://github.com/razorpay/slashdocs', 'source': 'github', 'source_url': 'https://github.com/razorpay/slashdocs', 'downloads': None, 'stars': 388, 'installs': None, 'mentions': 1, 'score': 103.29, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-12', 'last_seen': '2026-10-19', 'times_seen': 10, 'icon': 'https://github.com/razorpay.png?size=64', 'official_desc': 'Give your agents the power to publish reviewable HTML documents. Self-hosted server + CLI + remote MCP; agents push, humans review with comments and versions.', 'stars_own': 388, 'stars_creator': None, 'stars_own_repo': 'razorpay/slashdocs', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': 'HEAD/README.md', 'install_conf': None, 'desc_src': 'github-api', 'readme_body': "slashdocs\n\n \n \n \n \n \n\nThe collaborative workspace for agentic organizations.\n\nslashdocs is where agents and people work on the same documents. Agents publish rich, self-contained HTML: reports, specs, dashboards, diagrams, and decision docs. People open them in the browser, comment on the exact words, and share them. Agents read the comments and publish the next version. Every version is kept.\n\nInstead of pasting giant reports into chat, give the work a shared home where your team and its agents improve it together.\n\nWhat you can build with it\n\n- Agent-generated architecture reviews\n- Incident reports and postmortems\n- Product specs and launch plans\n- Data analysis reports\n- Design proposals with Mermaid/SVG diagrams\n- QA findings and browser-test reports\n- Internal dashboards captured as portable HTML\n\nFeatures\n\n- One self-contained HTML file per document\n- Browser review UI with sandboxed rendering\n- Anchored comments and resolve/reopen workflow\n- Slack notifications, personal mute controls, and comment/heading links\n- Immutable versions with optimistic concurrency\n- OpenID Connect for human users (Google, Okta, Entra ID, Keycloak, Dex, ...)\n- Native service accounts for headless/agent workflows\n- Remote MCP endpoint with browser OAuth—no local MCP process or CLI required\n- Go CLI with compact default output and JSON mode\n- Generated OpenAPI/Swagger docs\n- Self-hostable Go server with embedded React frontend\n- Dockerfile for container deployments\n\nWhy one HTML file?\n\nA single HTML artifact is easy for agents to generate, easy to version, easy to archive, and easy to move through a CLI. Images can be embedded with base64 data URLs, and diagrams can be Mermaid, inline SVG, or rendered HTML/CSS.\n\n \n\nNo separate image hosting is required for v1.\n\nQuickstart (docker compose)\n\nThe fastest way to try slashdocs is the bundled compose stack, which brings up\nthe server with Postgres, versitygw (S3),\nand a Dex identity provider — no cloud accounts or OAuth\nsetup needed:\n\n \n\nThis pulls the latest published image, so nothing is compiled on your machine.\nTo run your local changes instead, use docker compose up --build. Building\nneeds Docker with Compose v2 and the\nBuildx plugin, because\nthe Dockerfile uses BuildKit features. Docker Desktop and Docker's official\nEngine packages include both. Distro packages may not: on Ubuntu or Debian, run\nsudo apt install docker-buildx. Check with docker buildx version.\n\nOpen and sign in as admin@example.com with password\npassword.\n\nDex is the **development** login only. Real deployments configure Google or\nany other OIDC provider — see Self-hosting.\n\nmake e2e boots the same stack and runs the full non-interactive smoke test\n(login, documents, comments, sharing, folders, sandboxed rendering, SSE).\nmake e2e-browser runs the advisory Chromium browser suite. It reuses a healthy\nslashdocs + Dex stack already listening on ports 8080 and 5556; otherwise it boots\nthe isolated slashdocs-e2e compose project and leaves it running for inspection.\nBrowser E2E is deliberately separate from make ci.\n\nTear down the isolated E2E project, including data volumes, with:\n\n \n\nIf you started the normal quickstart project with docker compose up, stop that\nproject with docker compose down -v instead.\n\nInstall from source\n\n \n\nBinaries are written to:\n\n \n\nRun with Docker\n\nEvery tagged release publishes a multi-arch (amd64, arm64) image to Docker Hub\nat razorpay/slashdocs:\n\n \n\nCopy .env.example to .env, fill it in, and run. Pin an exact\nversion (razorpay/slashdocs:0.1.0) or digest in production rather than :latest:\n\n \n\nEach GitHub Release also carries\nstandalone CLI archives and the packaged Helm chart. See\ndocs/releasing.md for how releases are cut.\n\nSee Self-hosting for production setup and all environment variables. A Kubernetes Helm chart is available in charts/slashdocs.\n\nConnect an MCP client\n\nThe application server exposes Streamable HTTP directly at:\n\n \n\nAdd that URL to a remote MCP client. Standards-based clients discover the\nserver's OAuth metadata, open the normal slashdocs sign-in and consent flow, and\nreceive a short-lived, audience-bound token using authorization code + PKCE.\nNo slashdocs CLI or local MCP bridge is involved. Unattended agents can instead\nsend a slashdocs service-account key as a bearer token.\n\nThe MCP surface covers document, version, comment, sharing, and folder\nworkflows, plus management of existing service accounts. Creating service\naccounts or bearer keys is intentionally not exposed over MCP; mint those\ncredentials in the web application or CLI. Client-local operations such as\ninstalling or updating the CLI, switching local contexts, or opening a browser\nare also not MCP tools.\n\nMCP mirrors the CLI's authoring conventions: get_authoring_guidelines\nserves the same sectioned guidelines as slashdocs guidelines (index first,\nthen per-section detail). Default sharing of new documents is\nserver-controlled: org deployments can set SLASHDOCS_DEFAULT_DOC_PERMISSION\nto viewer, commenter, or editor (default private), and both MCP and\nCLI creates report the applied default_grant so the share can be revoked.\n\nCLI quickstart\n\n \n\nUse --json for structured output:\n\n \n\nDevelopment\n\n \n\nUseful targets:\n\n \n\nBuild the server through make build-backend rather than a bare go build.\nThe target regenerates the iframe bridge and links its source fingerprint so\nstartup can reject missing, stale, or corrupt embedded assets.\n\nArchitecture\n\nEverything ships as one Go binary, slashdocs-server. It serves the REST API, the embedded React frontend, and the embedded per-platform CLI binaries (/install.sh and downloads). You bring Postgres and an S3-compatible bucket. The same binary answers on two hostnames: the app hostname for the authenticated UI and API, and a separate render hostname where document HTML is served inside a sandboxed frame — the browser's same-origin policy keeps untrusted documents away from your session.\n\n \n\nProject layout\n\n \n\nAPI docs\n\nWhen the server is running:\n\n \n\nSecurity model\n\nUploaded HTML should be treated as untrusted content. slashdocs renders documents in a sandboxed frame and requires app and render URLs on different hostnames. Scheme or port differences on the same hostname do not isolate cookies; the unsafe override is for local development only.\n\nTelemetry\n\nNone. slashdocs collects no telemetry, analytics, or usage data, and phones home to no one. The only outbound calls the server makes are to your Postgres, your blob storage, and your configured OpenID Connect provider during login.\n\nLicense\n\nMIT — see LICENSE.", 'readme_raw_len': 6571, 'one_line_zh': '让你的 Agent 能够发布可评审的 HTML 文档', 'one_line_plain': 'Give agents the power to publish reviewable HTML documents'}, {'id': 268, 'key': 'wegotdocs-open-mcp', 'kind': 'mcp', 'name': 'wegotdocs/open-mcp', 'one_line': '📇 🏠 🍎 🪟 🐧 - Turn a web API into an MCP server and add it to the open-source open-mcp.org registry', 'one_line_ok': 1, 'why_bad': None, 'fields': '["MCP 服务"]', 'desc_raw': '', 'url': 'https://github.com/boltmcp/boltmcp', 'source': 'awesome:punkpeye/awesome-mcp-servers', 'source_url': 'https://github.com/wegotdocs/open-mcp', 'downloads': None, 'stars': 370, 'installs': None, 'mentions': 16, 'score': 1603.0, 'featured': 0, 'hidden': 0, 'first_seen': '2026-10-09', 'last_seen': '2026-10-19', 'times_seen': 35, 'icon': 'https://github.com/wegotdocs.png?size=64', 'official_desc': '📇 🏠 🍎 🪟 🐧 - Turn a web API into an MCP server in 10 seconds and add it to the open source registry: [https://open-mcp.org](https://open-mcp.org)', 'stars_own': 370, 'stars_creator': None, 'stars_own_repo': 'boltmcp/boltmcp', 'stars_source': 'github_search_api', 'stars_checked_at': '2026-10-19T20:00:25+00:00', 'stars_creator_repo': None, 'install_cmd': None, 'install_src': None, 'install_conf': None, 'desc_src': 'readme-HEAD', 'readme_body': 'BoltMCP Installation\n\nBoltMCP installs on any Kubernetes cluster with a single Helm chart.\n\nInstall with Claude\n\n1. 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