A
license-
qualityD
maintenanceEnables semantic search across multiple Qdrant vector database collections, supporting multi-query capability and providing semantically relevant document retrieval with configurable result counts.
46
MIT
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"Reversing MD5 hash of a phone number to retrieve the original number" matching MCP servers:
- Flicense-qualityCmaintenanceMCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.
- Flicense-qualityCmaintenanceAn enterprise-ready MCP server that exposes a RAG tool for retrieving relevant context and metadata from a Qdrant vector database using natural language queries.2
- AlicenseDqualityCmaintenanceA 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.12MIT
- Alicense-qualityAmaintenanceA self-hosted MCP memory and state system for long-running AI relationships, storing source text locally with explicit memory tools and a web manager for humans.11MIT
- Flicense-qualityCmaintenanceEnables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.
- AlicenseAqualityCmaintenanceEnables semantic search and analysis of customer support tickets. Provides tools to search tickets, analyze the dataset, and retrieve individual tickets using natural language.3MIT
- AlicenseAqualityDmaintenanceA Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.356Apache 2.0
- AlicenseAqualityCmaintenanceA Model Context Protocol server for Chroma, enabling AI models to create collections and retrieve data using vector search, full text search, and metadata filtering.13Apache 2.0
- FlicenseAqualityBmaintenanceSemantic search over project-specific decision rules using Chroma, enabling LLMs to retrieve relevant context.1
- AlicenseAqualityCmaintenanceAn MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.2Apache 2.0
- AlicenseAqualityDmaintenanceEnables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).396MIT
- FlicenseBqualityDmaintenanceA Model Context Protocol server that enables semantic vector search over Obsidian notes, allowing AI assistants to intelligently search and retrieve relevant note content.12
- FlicenseBqualityDmaintenanceA Node.js-based MCP server that enables AI agents to generate embeddings, index documents, and perform semantic vector searches using OpenAI and Chroma. It facilitates the creation of retrieval-augmented generation (RAG) pipelines for internal knowledge assistants and document-based workflows.3
LanceDB MCP Serverofficial
FlicenseBqualityDmaintenanceA basic serverless MCP server using LanceDB to store and retrieve documents via three tools: ingest, retrieve, and get table details.325- AlicenseCqualityDmaintenanceEnables vector similarity search and serving of Svelte documentation via the MCP protocol, with support for local caching and multiple llms.txt documentation formats.11,065123MIT
Memgraph MCP Serverofficial
-licenseCquality-maintenanceA lightweight server implementation of the Model Context Protocol that connects Memgraph database with LLMs, allowing users to interact with graph databases through natural language.125
@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.15MIT- Alicense-qualityCmaintenanceEnables 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.MIT
- Alicense-quality-maintenanceProvides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.