"Using Qdrant vector database for code indexing" matching MCP servers:

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

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

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.

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

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.

MCP server that allows Claude AI to interact directly with MySQL databases, enabling query execution and table information retrieval through natural language.

Allows agents to lookup code snippets in your project. Uses a remote MCP server with a collection of code snippets, with support for automatic project language detection.

Enables semantic and hybrid search over documents and codebases backed by a Qdrant vector database, with collection management, document CRUD, metadata filtering, and AST-aware code indexing using local or cloud embedding providers. Supports natural language queries, incremental re-indexing of changed files, and custom configurable prompts for guided workflows.

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.

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

Enables semantic code search across codebases using Qdrant vector database and OpenAI embeddings, allowing users to find code by meaning rather than just keywords through natural language queries.

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

Enables semantic search and retrieval-augmented generation (RAG) using Qdrant vector database. Supports indexing documents from URLs and local directories, with flexible embedding options using Ollama or OpenAI.

An MCP server that enables interacting with Google's Indexing API, allowing agents to submit URLs to Google for indexing or removal from search results through natural language commands.

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

Project-scoped semantic workspace memory for AI coding assistants. Watches your project files and auto-indexes code and docs into Qdrant using tree-sitter semantic chunking and hybrid search (dense + sparse + RRF)

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.