Documentation Retrieval MCP Server (DOCRET)

@Sreedeep-SS

代码仓库

基本信息

分类

记忆与知识

许可证

MIT

传输方式

stdio

作者

Sreedeep-SS

关于 Documentation Retrieval MCP Server

A Model Context Protocol (MCP) server

#docret#mcp-server#documentation-retrieval

Documentation Retrieval MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "docret-mcp-server": {
      "command": "python",
      "args": [
        "main.py"
      ]
    }
  }
}

Documentation Retrieval MCP Server 工具

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。

概览

What is Documentation Retrieval MCP Server (DOCRET)?

Documentation Retrieval MCP Server (DOCRET) is a Model Context Protocol (MCP) server that enables AI assistants to access up-to-date documentation for Python libraries such as LangChain, LlamaIndex, and OpenAI. It dynamically fetches and provides relevant information from official documentation sources, ensuring AI applications always have access to the latest content.

How to use Documentation Retrieval MCP Server (DOCRET)?

Clone the repository, create and activate a virtual environment, install dependencies with pip install -r requirements.txt (or uv sync), set the SERPER_API_KEY environment variable in a .env file, and run python main.py. The server exposes a get_docs function that takes a query and library parameter. Integrate with AI assistants by adding a JSON configuration to the assistant’s MCP server list.

Key features of Documentation Retrieval MCP Server (DOCRET)

Dynamic retrieval of documentation content for Python libraries.
Asynchronous web searches using the SERPER API.
HTML parsing with BeautifulSoup to extract readable text.
Extensible design: easily add new libraries by updating the docs_urls dictionary.
Currently supports LangChain, LlamaIndex, and OpenAI.

Use cases of Documentation Retrieval MCP Server (DOCRET)

AI assistants answering user questions about Python library APIs and usage.
Keeping AI applications updated with the latest official documentation.
Providing context for code generation or debugging sessions.
Integrating with Claude Desktop or Claude Code for real-time documentation lookup.

常见问题

What is the Model Context Protocol?

The Model Context Protocol is an open standard that enables secure, two-way connections between data sources and AI-powered tools. Developers can expose data through MCP servers or build AI applications (MCP clients) that connect to these servers.

What are the prerequisites for using this server?

You need Python 3.8 or higher, a package manager (UV or pip), a Serper API key, and Claude Desktop or Claude Code for testing.

Which Python libraries are currently supported?

The server currently supports LangChain, LlamaIndex, and OpenAI.

How can I add support for an additional library?

Add the library name and its documentation URL to the `docs_urls` dictionary in `main.py`. The server will then be able to search and retrieve documentation from that URL.

How do I run the MCP server after installation?

After setting up the environment and API key, run `python main.py` from the project root directory to launch the server.

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