A Model Context Protocol (MCP) server that enables semantic search and retrieval of documentation using a vector database (Qdrant). This server allows you to add documentation from URLs or local files and then search through them using natural language queries.
A Model Context Protocol server that enables intelligent document search and retrieval from PDF collections, providing semantic search capabilities powered by OpenAI embeddings and ChromaDB vector storage.
A local-first semantic search server for documents, supporting PDFs, Office files, and text/markdown, enabling natural language search via the Model Context Protocol (MCP).
A Model Context Protocol (MCP) server for Retrieval-Augmented Generation (RAG) operations. It provides tools for building and querying vector-based knowledge bases from document collections, enabling semantic search and document retrieval capabilities.
A Model Context Protocol server for ingesting, chunking and semantically searching documentation files, with support for markdown, Python, OpenAPI, HTML files and URLs.
