"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.

Enables searching documentation from GitHub repositories and web pages via MCP tools, with in-memory indexing and caching for fast retrieval.

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.

Gives AI agents a persistent vector memory by embedding text with Ollama and storing it in Qdrant, so they can save, batch-save, semantically search with payload filters, and delete memories across collections.

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.

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

Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.

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

Enables a unified Graph-RAG memory layer combining vector similarity search via Qdrant with entity-relationship traversal via Kuzu, allowing memories to be stored, retrieved, and expanded along an entity graph.

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

An MCP server for storing and retrieving memories using Qdrant vector search, acting as a semantic memory layer.

Enables AI assistants to perform semantic search and retrieval across codebases stored in Qdrant, using Ollama models for GPU-accelerated embeddings.

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.

attendance-engine-mcp is a Model Context Protocol server that gives AI agents deterministic, fixture-backed tools for workforce attendance and wage-and-hour compliance. Built on @attendance-engine/core — a pure-function, zero-deps, 100%-covered TypeScript engine — it lets Claude, Cursor, Windsurf, or any MCP host correctly answer the questions HR/payroll teams actually ask: did this person clock i

Enables SQL database interaction with true server-side result paging, runtime-managed connection profiles, and SSH-bridged access for PostgreSQL.

MCP server for querying and managing multiple databases (SQLite, PostgreSQL, MySQL) with read-only mode and schema inspection.

A database MCP server providing 33 tools across PostgreSQL, MySQL, and SQLite, with connection groups, rollback, dump/restore, and schema auto-discovery via natural language.

Enables building and querying vector-based knowledge graphs with node and edge management and semantic search.

Read-only security audit for Supabase. Surfaces RLS gaps, permissive policies, exposed functions and grants, and returns scored findings with fix SQL that is never executed.

Enables grounded, dated research from a curated AI-knowledge repository, offering free BM25 retrieval and a research loop that combines the knowledge base, curated sources, and the open web with tiered citations.