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MCP Documentation Search Server

@PicardRaphael

リポジトリ

基本情報

カテゴリ

メモリとナレッジ

トランスポート

stdio

作者

PicardRaphael

MCP Documentation Search Server について

🔍 FastMCP-powered documentation search engine that provides unified access to multiple framework docs (Next.js, Tailwind, Framer Motion, etc.) with intelligent name resolution and async processing.

#duckduckgo#developer-tools#documentation-tools#documentation-search#python-async#fastmcp

MCP Documentation Search Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-server-documentation": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

MCP Documentation Search Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is MCP Documentation Search Server?

This server enables AI systems to intelligently search across multiple popular framework and library documentations using a unified interface. Built with FastMCP, it provides a single tool to query documentation from sources like LangChain, Next.js, Tailwind CSS, Framer Motion, and more.

How to use MCP Documentation Search Server?

Clone the repository, set up a Python virtual environment, install dependencies with pip install -r requirements.txt, and run python main.py. Then use the get_docs function, passing a query and a library name (e.g., get_docs(query="animations", library="framer-motion")). The server intelligently handles multiple library name variations.

Key features of MCP Documentation Search Server

  • Multi-library support across six popular frameworks/libraries
  • Intelligent search with smart name resolution for library variations
  • DuckDuckGo-powered search for accurate results
  • Asynchronous processing and parallel content fetching
  • Robust error handling and network timeout management
  • Comprehensive test suite for reliability

Use cases of MCP Documentation Search Server

  • AI assistants retrieving documentation for user queries without manual browsing
  • Developers integrating doc search into automated workflows or coding tools
  • Rapid prototyping by searching across multiple frameworks from a single interface
  • Serving as a backend for documentation bots or chat interfaces

よくある質問

1

What are the runtime requirements?

Python 3.8+ and either pip or uv package manager. A virtual environment is recommended.

2

How do I add a new documentation source?

Add the URL to `DOCS_URLS` in `config.py` and add common aliases to `LIBRARY_ALIASES`. Then update documentation and submit a pull request.

3

What should I do if I get a TimeoutError or no results?

Increase `HTTP_TIMEOUT` in `config.py` (default 30 seconds) for timeouts. For no results, try different search terms or verify the library name. Check your internet connection and the documentation URL for HTTP errors.

4

How does library name resolution work?

The system accepts multiple variations of library names (e.g., "framer", "framermotion", "framer-motion", "motion") and maps them to the correct documentation URL using aliases defined in `config.py`.

5

Are there any tests?

Yes, the project includes unit tests (for utilities and services), integration tests (for the main API), and uses pytest. Run all tests with `python -m pytest`.

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