RAGDocs (Vector Documentation Search)

MCP-RAGDocs is a server implementation that provides semantic documentation search and retrieval using vector databases to augment LLM capabilities. Developed by hannesrudolph and forked by jumasheff, it enables AI assistants to search through stored documentation, extract URLs from web pages, manage documentation sources, and process queues of URLs for indexing. The server uses Qdrant for vector storage and supports multiple embedding providers including Ollama and OpenAI, making it particularly valuable for enhancing AI responses with relevant documentation context without requiring users to switch between interfaces.