"Open Source MCP Server for Managing and Updating MCP Configurations in VSCode" matching MCP servers:

Open-source MCP server for mem0 that enables memory storage and retrieval using local LLMs, self-hosted, and Docker-free.

A self-hosted second brain MCP server that enables capturing thoughts with deduplication and semantic search using local embeddings and PostgreSQL with pgvector, all running on your own hardware.

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

Connects AI agents to Teradata databases for querying, analysis, administration, and AI pipelines with enterprise security.

Enables managing knowledge bases in Open WebUI and retrieving relevant fragments from Qdrant-stored knowledge through an MCP client.

A remote MCP server enabling semantic memory across multiple AI platforms like Perplexity Comet and Claude Desktop, with vector embeddings for intelligent retrieval.

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.

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.

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

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.

A local-first knowledge base server that enables AI clients to store, retrieve, and manage documents using semantic search. Provides privacy-focused, offline-capable memory for AI assistants with tools for ingesting, querying, updating, and deleting knowledge.

An MCP server for querying and managing LlamaIndex documents stored in Qdrant vector databases, with automatic embedding model detection and extensive tools for search, retrieval, and collection management.

Provides 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.

MCP server for Qdrant vector database with local BERT embeddings. Enables semantic search and vector storage operations through natural language.

MCP server that connects Cursor IDE to Inky backend, enabling users to list and filter personalized coding rules stored in Pinecone.

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.

MCP RAG Server is a Python MCP server that indexes documents in multiple formats (Markdown, text, PowerPoint, PDF) using multilingual-e5-large embeddings and enables vector search for retrieval-augmented generation.

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.

Enables users to ingest documents into a PostgreSQL/pgvector knowledge base, run semantic search over them, and get grounded answers through a retrieval-augmented generation pipeline backed by a free LLM. It also lets clients spin up specialized AI agents on demand and exposes knowledge-base stats and configuration as resources for tutoring workflows.