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mcpjungle/MCPJungle

mcpjungle/MCPJungle

One place to manage & connect to all your MCP servers
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来源明细
收录自 abordage/awesome-mcp 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-14 · 最近见到 2026-10-16 · 共见到 4 次 · 被 3 个来源列出
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综合分 303 · 3 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
我们收录于 2026-10-14;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

MCPJungle

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

Run all your MCP servers behind one endpoint
MCPJungle is a self-hosted MCP gateway for developers and teams who want to manage multiple MCP servers without scattered client configurations, duplicated setup, or inconsistent access control.
Use it **locally** to keep your personal MCP setup clean, or run it as **shared infrastructure** for a team with centralized discovery, access control, and observability.
Instead of wiring every MCP server into every AI client, register your servers once in MCPJungle and let Claude, Cursor, Codex, or your own Agents connect to a single MCP endpoint.
Why MCPJungle?
MCP is powerful, but managing many MCP servers gets messy fast.
Without a gateway:
- 🔌 Every client needs its own MCP server configuration
- 🧩 Tools, prompts, and resources are scattered across different servers
- 🔐 Access control is duplicated or missing
- 👥 Teams have no shared view of available MCP tools
- 🛠️ Local setups become hard to reproduce
MCPJungle gives you a single control point:
- 🌐 One MCP endpoint for Claude, Cursor, Copilot, and custom agents
- 🗂️ One place to register and manage MCP servers
- 🔎 Unified discovery for tools, prompts, and resources
- 🎛️ Optional tool groups to expose only the tools a client should see
- 📊 Simple access-control and observability hooks for shared deployments
Start with a local setup. Scale to a shared team gateway when you need it.
Documentation
Mcpjungle documentation has a new home: https://docs.mcpjungle.com.
Please prefer the docs site over this README for the latest guides, reference, and operational details.
Your AI Clients can also access the docs using its MCP server https://docs.mcpjungle.com/mcp!
Quickstart
This quickstart guide will show you how to:
1. Start the mcpjungle server locally using docker compose
2. Add an MCP server in mcpjungle
3. Connect your Claude Desktop to mcpjungle to access your MCP tools
Start the server
Fetch the docker-compose.yaml and start the mcpjungle server:
This exposes mcpjungle's streamable http mcp server at http://localhost:8080/mcp by default.
Add an MCP server
1. Download the mcpjungle CLI on your local machine either using brew or directly from the Releases Page.
2. Add the context7 MCP server to mcpjungle using the CLI:
You should see output similar to this:
Connect to mcpjungle
In your Claude Desktop, add the configuration for mcpjungle MCP server:
Once you have added the configuration, try asking claude something simple:
Claude will then attempt to call the context7__get-library-docs tool via MCPJungle, which will return the documentation for the Lodash library.
You now have a working MCP setup with a single unified endpoint!
Next, explore the complete documentation at docs.mcpjungle.com and the public roadmap.
---
Legacy README reference
📋 Table of Contents
- Installation
- Usage
- Server
- Running mcpjungle server inside Docker
- Running mcpjungle server directly on the host machine
- Shutting down the server
- Client
- Adding Streamable HTTP-based MCP servers
- Adding STDIO-based MCP servers
- Removing MCP servers
- Custom URL for server
- Cold-start problem & Stateful Connections
- Connect to mcpjungle from Claude
- Connect to mcpjungle from Cursor
- Connect to mcpjungle from Copilot
- Enabling/Disabling Tools globally
- Prompts
- Tool Groups
- Authentication
- Enterprise features
- Access Control
- OpenTelemetry
- Limitations
- Contributing
Installation
MCPJungle is shipped as a stand-alone binary.
You can either download it from the Releases Page or use Homebrew to install it:
Verify your installation by running
> [!IMPORTANT]
> On MacOS, you will have to use homebrew because the compiled binary is not Notarized yet.
MCPJungle provides a Docker image which is useful for running the registry server (more about it later).
Usage
MCPJungle has a Client-Server architecture and the binary lets you run both the Server and the Client.
Server
The MCPJungle server is responsible for managing all the MCP servers registered in it and providing a unified MCP gateway for AI Agents to discover and call tools provided by these registered servers.
The gateway itself runs over streamable http transport and is accessible at the /mcp endpoint.
Running inside Docker
For running the MCPJungle server locally, docker compose is the recommended way:
> [!NOTE]
> The enterprise mode used to be called production mode.
> The mode has now been renamed for clarity. Everything else remains the same.
This will start the MCPJungle server along with a persistent Postgres database container.
来源
awesome · https://github.com/mcpjungle/MCPJungle
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