RAG Documentation
Mcp Ragdocs provides vector-based semantic search and retrieval tools for documentation, enabling AI assistants to augment responses with relevant context from indexed sources. The server offers tools to search documentation through natural language queries, manage documentation sources, extract URLs from webpages, and process documentation queues for indexing. It solves the problem of efficiently retrieving contextually relevant documentation at scale by leveraging vector embeddings and semantic search capabilities.
- GitHub stars
- 261
- tools
- 7
- last updated
- Feb 15, 2026
Tools
7 toolsPublic tool metadata for what this MCP can expose to an agent.
search_documentationSearch through stored documentation using natural language queries. Returns matching excerpts with context, ranked by relevance.
Search through stored documentation using natural language queries. Returns matching excerpts with context, ranked by relevance.
No parameter schema in public metadata yet.
list_sourcesList all documentation sources currently stored in the system. Returns a comprehensive list of all indexed documentation including source URLs, titles, and last update times.
List all documentation sources currently stored in the system. Returns a comprehensive list of all indexed documentation including source URLs, titles, and last update times.
No parameter schema in public metadata yet.
extract_urlsExtract and analyze all URLs from a given web page. This tool crawls the specified webpage, identifies all hyperlinks, and optionally adds them to the processing queue.
Extract and analyze all URLs from a given web page. This tool crawls the specified webpage, identifies all hyperlinks, and optionally adds them to the processing queue.
No parameter schema in public metadata yet.
remove_documentationRemove specific documentation sources from the system by their URLs. The removal is permanent and will affect future search results.
Remove specific documentation sources from the system by their URLs. The removal is permanent and will affect future search results.
No parameter schema in public metadata yet.
list_queueList all URLs currently waiting in the documentation processing queue. Shows pending documentation sources that will be processed when run_queue is called.
List all URLs currently waiting in the documentation processing queue. Shows pending documentation sources that will be processed when run_queue is called.
No parameter schema in public metadata yet.
run_queueProcess and index all URLs currently in the documentation queue. Each URL is processed sequentially, with proper error handling and retry logic.
Process and index all URLs currently in the documentation queue. Each URL is processed sequentially, with proper error handling and retry logic.
No parameter schema in public metadata yet.
clear_queueRemove all pending URLs from the documentation processing queue. Use this to reset the queue when you want to start fresh, remove unwanted URLs, or cancel pending processing.
Remove all pending URLs from the documentation processing queue. Use this to reset the queue when you want to start fresh, remove unwanted URLs, or cancel pending processing.
No parameter schema in public metadata yet.
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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.
Features
- Vector-based documentation search and retrieval
- Support for multiple documentation sources
- Semantic search capabilities
- Automated documentation processing
- Real-time context augmentation for LLMs
Tools
search_documentation
Search through stored documentation using natural language queries. Returns matching excerpts with context, ranked by relevance.
Inputs:
query(string): The text to search for in the documentation. Can be a natural language query, specific terms, or code snippets.limit(number, optional): Maximum number of results to return (1-20, default: 5). Higher limits provide more comprehensive results but may take longer to process.
Details
- Repository
- github.com/hannesrudolph/mcp-ragdocs
- Categories
- AI & LLM Tools, Search & Web Crawling
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