RAG Documentation Server

Example Use Cases

• 1

  Access and search indexed project documentation and return ranked, context-rich excerpts to augment responses.

• 2

  Crawl web pages, extract URLs, and queue them for processing and indexing into a vector database for later retrieval.

• 3

  Remove, list, or manage documentation sources and monitor processing queue status while integrating vector search into workflows.

Description

This MCP server processes and indexes documentation sources, generates embeddings (via OpenAI by default), and performs vector-based semantic search to return relevant excerpts and context. It supports multiple documentation sources, a URL processing queue (extract, list, run, clear), and tools to add or remove indexed documentation. The server is useful for real-time context augmentation of LLMs and building documentation-aware assistants.