Which libraries are supported?
The server searches official documentation for Langchain (`python.langchain.com/docs`), LlamaIndex (`docs.llamaindex.ai/en/stable`), and OpenAI (`platform.openai.com/docs`).
@AmirUpSkill
基本信息
暂无概览
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"Mcp-Server": {
"command": "uv",
"args": [
"init",
"mcp-server"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
This project implements an MCP server that provides large language models (LLMs) with real-time access to the latest documentation for specified Python libraries (Langchain, LlamaIndex, OpenAI) before they generate code suggestions. It addresses the problem of LLMs using outdated training data for rapidly evolving libraries.
Install Python 3.11+ and the uv package manager. Obtain a Serper API key from serper.dev and store it in a .env file as SERPER_API_KEY. Run the server with uv run main.py. Then integrate with an MCP client like Claude Desktop (via JSON configuration) or Claude Code (via claude mcp add).
get_docs tool for searching official documentation.httpx and BeautifulSoup.asyncio, FastMCP, and managed via uv.The server searches official documentation for Langchain (`python.langchain.com/docs`), LlamaIndex (`docs.llamaindex.ai/en/stable`), and OpenAI (`platform.openai.com/docs`).
Python 3.11+, the `uv` package manager, and a Serper API key are required. Node.js/npx is needed only for the optional MCP Inspector debugging tool.
It constructs a site‑specific Google search via the Serper API, then uses `httpx` and `BeautifulSoup` to extract text content from the top result pages.
Create a `.env` file with `SERPER_API_KEY=your_key`. The server runs locally over stdio and connects to MCP clients via configuration (Claude Desktop or Claude Code).
Yes. Run the MCP Inspector with `npx @model-context-protocol/inspector "uv run main.py"`, open `http://localhost:5173`, and test the `get_docs` tool with sample queries.

Ask your Takibi Base collections anything; get answers with cited passages and a support score.
Local MCP server and CLI that keeps a Git-versioned, model-written index of a codebase and its database schema, so coding agents start each task with the whole system in view.
Project context your AI can query through MCP — with source evidence.
Shared, serverless, conflict-free memory for AI agents: a multi-machine drop-in for the MCP memory knowledge graph.

MCP connector for Snipara, the Project Brain for AI coding agents
评论