MCP Server with Qdrant

What is MCP Server with Qdrant?

MCP Server with Qdrant integrates the Qdrant vector database with a Model Context Protocol server to store, manage, and semantically search code snippets. It is designed for developers and AI tools that need to retrieve relevant code using natural language queries.

How to use MCP Server with Qdrant?

Start the services with docker-compose up -d. Use the provided MCP tools qdrant-store to store information and qdrant-find to search for related snippets. Configure AI IDEs (such as Cursor) by adding a JSON entry with "url": "http://localhost:8000/sse".

Key features of MCP Server with Qdrant

• Store and manage code snippets in Qdrant.
• Natural language code search with semantic retrieval.
• Supports SSE (Server-Sent Events) transport.
• Uses default embedding model all-MiniLM-L6-v2.
• Provides dedicated qdrant-store and qdrant-find tools.

Use cases of MCP Server with Qdrant

• Build a searchable code snippet library for quick reference.
• Enable LLM tools to retrieve relevant code context on demand.
• Integrate with AI IDEs for contextual code retrieval during development.

FAQ from MCP Server with Qdrant

What are the prerequisites to run MCP Server with Qdrant?

Docker and Docker Compose must be installed on the system.

How is data persisted and where is it stored?

Data is stored permanently in the ./qdrant_storage directory. Regular backups of this directory are recommended.

How can I change the embedding model used for search?

Set the EMBEDDING_MODEL environment variable to your desired model name (default is sentence-transformers/all-MiniLM-L6-v2).

How do I access the Qdrant dashboard?

The Qdrant UI is available at http://localhost:6333/dashboard.

What transport protocol does the MCP server use?

The MCP server uses SSE (Server-Sent Events) and listens on port 8000 at the /sse endpoint.