Enables AI-powered medical information retrieval through FHIR clinical document search and GraphRAG-based exploration of medical entities and relationships. Combines vector search with knowledge graph queries for comprehensive healthcare data analysis.
Enables AI to save, organize, search, and synthesize research materials using a local vector database with support for both OpenAI and Ollama backends.
MCP server that indexes your Obsidian notes into a Milvus vector database and enables querying them via a local or OpenAI LLM, with real-time synchronization.
Enables semantic search and conversational querying across a personal research library of PDFs, DOCX, and other documents using a vector database. It provides tools for document summarization, finding related papers, and high-accuracy retrieval for AI clients like Claude Desktop.
An enterprise-ready MCP server that exposes a RAG tool for retrieving relevant context and metadata from a Qdrant vector database using natural language queries.
Enables AI assistants to save, retrieve, and manage research content using ChromaDB vector storage with semantic search, topic organization, and automatic deduplication powered by OpenAI embeddings.
A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.
Enables AI-powered semantic search and question-answering for LiveKit documentation using Pinecone vector search and real-time web search with Tavily, providing detailed responses with source attribution.
Model Context Protocol (MCP) server implementation for semantic search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities. You can use Claude and Cline AI Also
Provides comprehensive management of OpenAI Vector Stores, allowing AI assistants to upload files, manage vector databases, and handle batch operations via the OpenAI API. It supports multiple deployment methods, including Cloudflare Workers and local NPM installation, for seamless integration with MCP-compatible clients.
Enables AI agents to interact with an embedded graph database (GrafeoDB) via the Model Context Protocol, providing tools for graph CRUD, GQL queries, full-text and vector search, and graph algorithms.
A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.
Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.
Enables scraping, crawling, structured data extraction, and vector DB synchronization from natural language in Claude Desktop using the scrapedatshi RAG pipeline API.
Enables interaction with Moorcheh AI services including namespace management, document embedding, vector search, and AI-powered answers through the Model Context Protocol.
The CockroachDB MCP Server is a natural language interface designed for agentic applications and LLMs to manage, monitor and query data in CockroachDB.