mcp-server-qdrant
The Qdrant Model Context Protocol (MCP) server implementation for storing and retrieving information in a vector database.
- Type
- MCP
- Transport
- http
- Open source
- Yes
- GitHub Stars
- ★ 1.4k
- Source
- mcp-github
- Repository
- github.com/qdrant/mcp-server-qdrant
Overview
mcp-server-qdrant is the official MCP server implementation for the Qdrant vector search engine. It acts as a semantic memory layer on top of the Qdrant database, enabling AI applications to seamlessly integrate and access external data sources. With this capability, AI can store and retrieve relevant information, making it suitable for use cases requiring efficient vector search. Configuration is done via environment variables such as Qdrant server URL and API Key, and the server runs using a specified transport protocol. Ideal for developers needing efficient vector search and semantic memory functionality.
Capabilities
- ▪Store information in Qdrant
- ▪Retrieve relevant information from Qdrant
- ▪Support custom collection names
- ▪Support multiple embedding models
Use cases
Setup
Configure the Qdrant server URL and API Key via environment variables, then run mcp-server-qdrant using `uvx`.
This information was compiled by AI from public sources and may contain inaccuracies — please refer to the source.
FAQ
How do I configure the Qdrant server?
By setting the environment variables `QDRANT_URL` and `QDRANT_API_KEY`.
Which embedding models are supported?
Currently supports `fastembed` and `sentence-transformers/all-MiniLM-L6-v2`.
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