hannesrudolph/mcp-ragdocs

Overview

An MCP server that uses vector search to retrieve and process documentation. It allows AI assistants to augment their responses with relevant documentation context.

Best for

Best for
Developers building AI assistants that need up-to-date documentation context

Use cases

• Integrate documentation retrieval into AI assistants
• Build a RAG pipeline for developer docs
• Enhance AI responses with context from vector search

How to use

Install

npx -y @hannesrudolph/mcp-ragdocs

Tools exposed

• search_documentation
• list_sources
• extract_urls
• remove_documentation
• list_queue
• run_queue
• clear_queue

Tested with

Claude Desktop

Example client config

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

Notes

An MCP server that uses vector search to retrieve and process documentation. It allows AI assistants to augment their responses with relevant documentation context.

265 stars on GitHub. Last updated 2025-07-18. Licensed MIT.

Use cases

• Integrate documentation retrieval into AI assistants
• Build a RAG pipeline for developer docs
• Enhance AI responses with context from vector search

Pros

• Open-source and written in TypeScript for easy integration
• Leverages vector search for relevant documentation retrieval
• Compatible with the MCP ecosystem for AI assistants

Cons

• Requires setup and configuration of vector search infrastructure
• Documentation quality and coverage directly affect retrieval accuracy
• Relatively early stage with 265 stars, limited community support

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.