"A Java-based MCP server" matching MCP servers:

A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.

MCP server for Vectros, a typed multi-tenant record store with hybrid search and citation-grounded RAG, enabling agents to query, search, and ask questions over their own indexed data.

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

An independent memory service for AI agents that stores and retrieves facts with keyword and semantic search, context management, and a plugin-based architecture.

A Model Context Protocol (MCP) server for Retrieval-Augmented Generation (RAG) operations. It provides tools for building and querying vector-based knowledge bases from document collections, enabling semantic search and document retrieval capabilities.

The MCP Server for Weaviate facilitates integration with Weaviate using a customizable Python-based server, enabling interaction with Weaviate databases and OpenAI APIs via configurable URL and API keys.

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 modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.

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.

An MCP server aimed to be portable, local, easy and convenient to support semantic/graph based retrieval of txtai "all in one" embeddings database. Any txtai embeddings db in tar.gz form can be loaded

A knowledge-graph-based persistent memory server for the Model Context Protocol, storing entities, observations, and relations in SQLite with semantic vector search and temporal versioning.

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 applications to perform homomorphic encryption-based vector search via enVector, allowing secure data retrieval in private networks.

Enables Claude Desktop to search private documents using Azure AI Search and perform web searches with Bing, providing AI-enhanced results with source citations through Azure AI Agent Service or direct Azure AI Search integration.

Indexes PDF documents into Qdrant and exposes semantic search as MCP tools, enabling RAG-based interactions with your documents.

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.

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