A Model Context Protocol server for ingesting, chunking and semantically searching documentation files, with support for markdown, Python, OpenAPI, HTML files and URLs.
A Model Context Protocol server that enables semantic search capabilities by providing tools to manage Qdrant vector database collections, process and embed documents using various embedding services, and perform semantic searches across vector embeddings.
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 simple Model Context Protocol server that enables searching and retrieving relevant documentation snippets from Langchain, Llama Index, and OpenAI official documentation.
A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.
Self-hosted MCP server that indexes documentation from various sources and makes it searchable by AI assistants via the Model Context Protocol and REST API.