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

A local-only codebase indexer that extracts syntax-aware chunks, creates embeddings locally, and enables semantic code search and symbol lookup via MCP.

Enables semantic code search over a local codebase using Qdrant vector embeddings and OpenAI embeddings, allowing natural language queries from MCP-compatible clients like Claude Desktop.

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

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

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 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.

An MCP server for semantic code search using Qdrant vector database, enabling natural language queries to find relevant code snippets across indexed codebases.

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.

A Model Context Protocol server that enables semantic search capabilities by providing tools to manage Qdrant vector database collections, process and embed documents using various embedding services, and perform semantic searches across vector embeddings.

Enables indexing and semantic search of software project files via MCP, with automatic file watching for updates.

Provides a natural language interface for querying and managing PostgreSQL, MySQL, MariaDB, MSSQL, and SQLite databases using the Model Context Protocol. Users can explore database schemas and visualize query results through an integrated web dashboard.

Enables semantic and full-text search over markdown notes, with tools to create, edit, move, and delete notes, all through MCP. Runs entirely locally using Ollama for embeddings and SQLite for storage, working with Obsidian or standalone.

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 semantic search across multiple Qdrant vector database collections, supporting multi-query capability and providing semantically relevant document retrieval with configurable result counts.

Combines Neo4j graph database with vector search using OpenAI embeddings for intelligent semantic search across knowledge graphs.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

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.

Turns your Readwise library into a blazing-fast semantic search engine with a streaming MCP server for LLM clients, supporting vector search, nightly syncs, and deployment on serverless platforms like Vercel.

Enables AI assistants to interact with PostgreSQL and Supabase databases through natural language. Supports secure database operations including queries, migrations, and schema management with user-provided credentials.