RAG Documentation Search

26

Provides semantic document search and retrieval through vector embeddings, enabling context-aware responses backed by specific documentation sources

GitHub npm
Category MCP Servers
Added Mar 28, 2026
Views 101

About

The MCP-server-ragdocs provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. Built with TypeScript, it supports both local (Ollama) and cloud-based (OpenAI) embeddings generation, integrates with Qdrant for vector storage, and includes tools for semantic document search, URL extraction, and queue management. This implementation is particularly valuable for workflows requiring context-aware AI responses backed by specific documentation sources.

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