Docs MCP Server
@gpreddy172
Docs MCP Server について
A simple MCP server to search for documentation
基本情報
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-for-Documentation": {
"command": "uv",
"args": [
"init",
"mcp-server"
]
}
}
}ツール
ツールは検出されませんでした
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 Docs MCP Server?
Docs MCP Server is a Model Context Protocol (MCP) server that searches the latest documentation for a given query and library. It supports searching docs for langchain, openai, and llama-index.
How to use Docs MCP Server?
Install Python 3.10+, MCP SDK 1.2.0+, and the uv package manager. Run uv run main.py to start the server, then configure Claude Desktop via its claude_desktop_config.json to connect.
Key features of Docs MCP Server
- Searches latest docs for a given query and library
- Supports langchain, openai, and llama-index
- Lightweight MCP server exposing tools via standardized protocol
- Uses
uvfor project management and dependency handling - Requires Python 3.10 or higher
Use cases of Docs MCP Server
- Retrieving up-to-date API documentation for langchain inside an LLM tool
- Searching OpenAI documentation for specific function signatures
- Looking up llama-index code examples through a natural-language query
FAQ from Docs MCP Server
What does the tool function take as arguments?
It takes a query (e.g. "Chroma DB") and a library (e.g. "langchain"). It returns text from the docs.
What are the system requirements?
Python 3.10 or higher, MCP SDK 1.2.0 or higher, and the uv package manager.
How do I connect the server to Claude Desktop?
Edit ~/Library/Application Support/Claude/claude_desktop_config.json to add the server command with the absolute path to your project, then restart Claude Desktop.
Does the server support authentication or remote transport?
The README only describes a local Unix-based setup with Claude Desktop; no authentication or remote transport details are provided.
What libraries can I search docs for?
The server supports langchain, openai, and llama-index.
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