RAG Documentation MCP Server

@hannesrudolph

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

hannesrudolph

关于 RAG Documentation MCP Server

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

#mcp #rag #vector-database #llm #mcp-servers

RAG Documentation 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": {
    "rag-docs": {
      "command": "npx",
      "args": [
        "-y",
        "@hannesrudolph/mcp-ragdocs"
      ],
      "env": {
        "OPENAI_API_KEY": "",
        "QDRANT_URL": "",
        "QDRANT_API_KEY": ""
      }
    }
  }
}

RAG Documentation MCP Server 工具

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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。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is RAG Documentation MCP Server?

An MCP server that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. It supports multiple documentation sources and uses semantic search to find relevant excerpts.

How to use RAG Documentation MCP Server?

Install the server via npx and add it to your claude_desktop_config.json with the required environment variables (OPENAI_API_KEY, QDRANT_URL, QDRANT_API_KEY). Once configured, use the provided tools such as search_documentation to query indexed documentation or extract_urls to add new sources.

Key features of RAG Documentation MCP Server

Vector-based documentation search and retrieval
Support for multiple documentation sources
Semantic search capabilities
Automated documentation processing
Real-time context augmentation for LLMs

Use cases of RAG Documentation MCP Server

Enhancing AI responses with relevant documentation
Building documentation-aware AI assistants
Creating context-aware tooling for developers
Implementing semantic documentation search
Augmenting existing knowledge bases

常见问题

What environment variables are required?

You need `OPENAI_API_KEY` for embeddings generation, `QDRANT_URL` for your Qdrant vector database instance, and `QDRANT_API_KEY` for authenticating with Qdrant.

How do I search documentation?

Use the `search_documentation` tool with a natural language query and an optional `limit` (1–20, default 5) to receive ranked excerpts with context.

How can I add new documentation sources?

Use the `extract_urls` tool on a public webpage to find hyperlinks, optionally adding them to the processing queue. Then run the `run_queue` tool to index them.

How do I remove documentation?

Use the `remove_documentation` tool with an array of exact URLs. Removal is permanent and affects future search results.

Can I monitor the processing queue?

Yes, use `list_queue` to see pending URLs and `clear_queue` to remove all pending items immediately.

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