"Qdrant vector database and search engine" matching MCP servers:

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

MCP server that searches documents in Qdrant using embeddings from LMStudio. Takes a text query, converts it to a vector via LMStudio's OpenAI-compatible API, and performs semantic search in Qdrant.

Enables AI assistants to interact with a Qdrant vector database by exposing collection, point, vector, payload, snapshot, search, recommendation, discovery, and observability operations as MCP tools.

MCP server that gives AI coding agents persistent, semantic memory via Qdrant vector search, enabling workspace-aware codebase, documentation, and decision search.

MCP server for document ingestion and semantic search on Qdrant. Enables ingesting local documents, generating embeddings with OpenAI, and performing vector search with metadata filters.

Integrates RAG into AI agents via MCP Server, supporting multiple vector database technologies for collection management and search operations.

A unified Docker container that runs Qdrant vector database and provides REST API and MCP interfaces for vector storage and semantic search, compatible with Claude vector hooks.

Enables integrating RAG into AI agents via MCP server, supporting multiple vector database technologies.

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

A shared, persistent MCP memory server for coding agents that enables storing and retrieving project decisions and context across different tools like Claude Code, Codex, and Cursor using semantic vector search.

Provides semantic search capabilities using Qdrant vector database with multiple embedding providers, including hybrid search, code indexing, and git history search. Adds optional time-based recency scoring to search results.

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

Enables AI assistants to interact with a Qdrant vector database, including embedding generation and vector search operations.

A read-only MCP server providing curated Qdrant documentation for LLMs, enabling retrieval of the latest and most accurate documentation.

Enables semantic search and document management using a local Qdrant vector database with OpenAI embeddings. Supports natural language queries, metadata filtering, and collection management for AI-powered document retrieval.

Enables local vector database operations with a Pinecone-compatible API, supporting exact and approximate search, metadata filtering, and semantic text queries over MCP.

Enables agents to turn documents into vectors, perform approximate nearest neighbor search with HNSW, and filter by metadata via MCP tools for RAG workflows.

Enables interaction with Qdrant vector database for storing, searching, and managing vectors with automatic text embedding.

An MCP server that provides long-term memory and semantic search using Qdrant and OpenAI embeddings, with tools for storing, searching, and managing knowledge.