Qdrant

About

This tool operates as a Model Context Protocol (MCP) server, offering robust semantic search functionalities. It allows users to perform natural language searches across their document collections, powered by a local Qdrant vector database for complete data privacy and OpenAI's embedding models for automatic text-to-vector conversion. Integrated seamlessly with MCP clients like Claude Code, it simplifies collection and document management while ensuring intelligent request throttling for OpenAI API calls.

Key Features

• Semantic Search: Natural language search across document collections
• Local Vector Database: Runs Qdrant locally via Docker for data privacy
• Automatic Embeddings: Uses OpenAI's models to convert text to vectors
• MCP Integration: Works seamlessly with Claude Code and other MCP clients
• Collection Management: Create, list, delete, and manage vector collections
• 2 GitHub stars

Use Cases

• Managing document collections with advanced metadata filtering capabilities
• Integrating semantic search into AI assistants like Claude Code
• Building local, private knowledge bases for natural language querying