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.
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Highlight AISearch through stored documentation using natural language queries. Parameters: query (string), limit (optional number)
List all documentation sources currently stored in the system, including source URLs, titles, and last update times.
Extract and analyze all URLs from a given web page. Parameters: url (string), add_to_queue (optional boolean)
Remove specific documentation sources from the system by their URLs. Parameters: urls (string[])
List all URLs currently waiting in the documentation processing queue.
Process and index all URLs currently in the documentation queue.
Remove all pending URLs from the documentation processing queue.
