"A server for MongoDB database information and resources" matching MCP servers:

Provides persistent memory for AI assistants using MongoDB Vector Search, enabling content storage, semantic search, and knowledge retrieval through MCP tools.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.

A server implementation that allows secure communication between MCP clients and privateGPT, enabling users to chat with privateGPT using knowledge bases and manage sources, groups, and users through a standardized Model Context Protocol.

Munin is a high-performance, pragmatic memory layer for AI agents (Cursor, Claude Code, OpenClaw, Gemini CLI,...). Unlike other solutions, Munin focuses on developer productivity with: * Multi-Project Support: Isolate memories into separate "brains" (Context Cores). * GraphRAG: Automatically builds a knowledge graph from your context. * Sub-200ms Search: Blazing fast Hybrid & Semantic

A local Retrieval-Augmented Generation system that enables users to ingest markdown files into a FAISS-powered vector knowledge base for semantic search. It provides tools for document indexing and context retrieval to support informed LLM queries without external dependencies.

将 pgvector 知识库检索能力封装为标准 MCP 服务器，支持 Tools、Resources 和 Prompts，使 Claude Desktop 等客户端可通过自然语言查询知识库。

Enables LLMs to interact with Zvec vector database through tools for collection management, document operations, vector search, and AI-powered embeddings.

Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.

An MCP server that integrates with LangChain and ChromaDB to provide documentation search for AI libraries and vector database management.

Provides semantic search capabilities by connecting Claude Desktop to a Cloudflare Workers backend powered by Vectorize. It enables natural language querying of knowledge bases using vector similarity and edge-based embedding generation.

A server that enables vector and keyword search capabilities in Typesense databases through the Model Context Protocol, providing tools for collection management, document operations, and search functionality.

mcp-server-test

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.