A
license-
qualityD
maintenanceA Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.
Apache 2.0
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"A platform for finding vacation rentals and homes" matching MCP servers:
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- Flicense-quality-maintenanceA 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.
- Alicense-qualityDmaintenanceA 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.MIT
- Alicense-qualityDmaintenanceA 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.6MIT
- AlicenseBqualityBmaintenanceA high-performance MCP server utilizing libSQL for persistent memory and vector search capabilities, enabling efficient entity management and semantic knowledge storage.615887MIT
@manasdb/mcp-serverofficial
Alicense-qualityDmaintenanceProvides a plug-and-play persistent memory layer for MCP-compatible AI assistants, enabling them to store, retrieve, and delete memories across multiple databases simultaneously using semantic vector search.5MIT
sovseal MCP Serverofficial
Alicense-qualityBmaintenanceProvides zero-knowledge, local-first persistent memory for AI assistants with on-device embeddings, cross-platform capture, and zero-RTT semantic recall.7Apache 2.0
tigergraph-mcpofficial
Alicense-qualityBmaintenanceModel Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.3Apache 2.0
MCP MariaDB Serverofficial
Alicense-qualityFmaintenanceAn interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.188MIT- Alicense-qualityDmaintenanceA high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities.8MIT
- Alicense-quality-maintenanceA Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.2Apache 2.0
- Alicense-qualityCmaintenanceA server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.12MIT
- Alicense-qualityCmaintenanceMCP server for Qdrant vector database with local BERT embeddings. Enables semantic search and vector storage operations through natural language.MIT
- Alicense-qualityFmaintenanceA local-first MCP server for indexing and searching codebases using PostgreSQL and Ollama embeddings, ensuring complete privacy and zero API costs.16MIT
- Alicense-qualityCmaintenanceA fully local, self-hosted memory server for MCP clients (Claude Code, Cursor, etc.) that provides persistent memory storage with semantic search, using local embeddings and a local Qdrant vector store.MIT
- Alicense-qualityDmaintenanceEnables AI assistants to interact with MariaDB databases through standard SQL operations and advanced vector/embedding-based search. Supports database management, schema inspection, and semantic document storage and retrieval with multiple embedding providers.MIT
- Alicense-qualityBmaintenanceA minimal RAG service that exposes a vector index for document retrieval via REST and MCP, allowing querying for relevant document chunks and returning a suggested LLM prompt.MIT