"How to run a Windows command" matching MCP servers:

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.

Self-hosted mem0 MCP server for Claude Code. Run a complete memory server against self-hosted Qdrant + Neo4j + Ollama while using Claude as the main LLM.

Indexes codebases using semantic embeddings for natural language search, enabling developers to find code with queries like 'how does authentication work'.

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.

Model Context Protocol server for RosalindDB, enabling AI clients to create datasets, ingest vectors, run similarity queries, and check usage on a cost-optimized vector search database.

Connects AI assistants to a persistent memory engine with Neo4j knowledge graph and ProMem extraction, enabling long-term context and associative memory across chats and workspaces.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Provides a local-first secure memory store with encrypted payloads, entity extraction, and vector persistence, exposing read, write, and delete tools with granular capability controls.

Enables AI assistants to interact with Meilisearch through a standardized interface, supporting index and document management, search capabilities, settings configuration, task monitoring, and experimental vector search.

A 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.

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.

Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.

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.

An 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.

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

A 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.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

A local-first MCP server for indexing and searching codebases using PostgreSQL and Ollama embeddings, ensuring complete privacy and zero API costs.

Exposes document ingestion, retrieval (vector, vectorless, hybrid), and multi-turn chat tools for a LangGraph-powered RAG pipeline with streaming answers.