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stabgan/openrouter-mcp-multimodal

stabgan/openrouter-mcp-multimodal

audio, and video AI tools" OpenRouter MCP Multimodal The MCP server for multimodal AI agents. One install · 21 tools · 300+ OpenRouter models · text, vision, audio & video — analysis and generation.
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Config template (adjust for your client)
claude mcp add openrouter-mcp-multimodal -- npx -y openrouter-mcp-multimodal
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
Star count
97 ★ · stabgan/openrouter-mcp-multimodal · read from the repository page · (checked 2026-10-16 20:00:20)
Source
collected from the punkpeye/awesome-mcp-servers list · type: MCP server
Harvest history
first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
Score
score 703 · 97 ★ · 7 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository stabgan/openrouter-mcp-multimodal;first catalogued 2026-10-09;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
Third-party quality signals

OpenRouter MCP Multimodal

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

The MCP server for multimodal AI agents.
One install · 21 tools · 300+ OpenRouter models · text, vision, audio & video — analysis and generation.
Quick start ·
Tools ·
Examples ·
Security ·
Troubleshooting ·
Development ·
Releasing ·
FAQ
---
What is this?
**OpenRouter MCP Multimodal** is a production-grade Model Context Protocol (MCP) server — listed on the official MCP Registry as io.github.stabgan/openrouter-multimodal. It connects AI coding agents (Cursor, Claude Desktop, VS Code, Windsurf, Cline, and others) to OpenRouter's unified LLM API over stdio.
Unlike text-only MCP servers, one install covers the **full multimodal surface**:
| Capability | Tools | Highlights |
| :------------ | :-------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Chat** | chat_completion, start_chat_completion, get_chat_completion_status | 300+ models, :nitro / :floor / :free / :online / :exacto suffixes, provider routing, web search, response caching, reasoning tokens, reasoning effort control, structured output (JSON Schema), async jobs, multi-model fusion, subagent delegation, response healing |
| **Vision** | analyze_image, generate_image, generate_image_dedicated | OCR, captioning, VQA, image generation with reference inputs, dedicated Image API with resolution/quality/format control |
| **Audio** | analyze_audio, generate_audio, text_to_speech, speech_to_text | Transcription, speech/music generation, dedicated TTS (Deepgram, ElevenLabs, OpenAI; free Deepgram default, voices, mp3/pcm), dedicated STT (Whisper, GPT-4o Transcribe, Voxtral, ElevenLabs Scribe) |
| **Video** | analyze_video, generate_video, generate_video_from_image, get_video_status | Clip understanding, Veo 3.1 / Seedance 2.5 / Kling v3.0 / Wan 2.7 / Grok Imagine Video generation with progress notifications |
| **Embedding** | embed_text | 37+ embedding models (text-embedding-3-small/large, Nomic, Jina, Voyage, etc.) |
| **Decisions** | decide | Structured yes/no, choice, or score decisions with calibrated probabilities (System One models) |
| **Catalog** | search_models, get_model_info, validate_model, rerank_documents, health_check | Model discovery, validation, reranking, ops health |
**Production hardening:** input/output path sandboxes (including analyze\_\* local files as of v4.5.2), SSRF guards, structured errors with _meta.code, MCP 2025-11-25 structured outputs + tool icons, async video progress notifications, and **3929** automated tests (3905 unit/mock + 7 regression + 17 live integration).
Quick start
**1. Get an API key** (free tier works) → openrouter.ai/keys
**2. Run the server**
**3. Add to your MCP client** — copy one JSON block from Install into your client config:
| Client | Config location |
| :----------------- | :-------------------------------------------------------------------------------------------------------------------------------- |
| **Cursor** | Project: .cursor/mcp.json · User: Cursor Settings → MCP |
| **Claude Desktop** | macOS: ~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json |
| **VS Code** | .vscode/mcp.json (workspace) or User Settings → MCP |
| **Windsurf** | Windsurf Settings → MCP (same mcpServers JSON shape as Cursor) |
Use the mcpServers object from Manual config below.
> **No credits required to start.** Free models such as google/gemma-4-26b-a4b-it:free work for chat and vision. Video/audio generation typically needs credits.
Install
MCP servers are distributed through several packaging models. **This server is implemented in Node.js/TypeScript**; the table below maps each ecosystem method to how you run it here.
| Method | Runtime | Best for | This server |
| :-------------------------------------- | :------------------------------- | :------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------- |
| **npx** | Node.js 22+ | Most MCP clients (default) | ✅ @stabgan/openrouter-mcp-multimodal |
| **uvx / pipx** | Python 3.10+ **and** Node.js 22+ | Python-first workflows, same pattern as PyPI MCP servers | ✅ mcp-server-openrouter-multimodal |
Source
awesome · https://github.com/stabgan/openrouter-mcp-multimodal
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