
Qdrant
The Qdrant MCP server provides semantic memory capabilities by integrating the Qdrant vector search engine with language models through the Model Context Protocol. It offers two main tools: `qdrant-store` for persisting information with optional metadata into Qdrant collections, and `qdrant-find` for retrieving semantically relevant information through vector search queries. The server solves the problem of enabling LLMs to maintain and access persistent, semantically-indexed memory across conversations by acting as a memory layer on top of a vector database.
- GitHub stars
- 1.4K
- tools
- 2
- last updated
- Mar 10, 2026
Tools
2 toolsPublic tool metadata for what this MCP can expose to an agent.
qdrant-storeStore some information in the Qdrant database
Store some information in the Qdrant database
No parameter schema in public metadata yet.
qdrant-findRetrieve relevant information from the Qdrant database
Retrieve relevant information from the Qdrant database
No parameter schema in public metadata yet.
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mcp-server-qdrant: A Qdrant MCP server
The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Overview
An official Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine. It acts as a semantic memory layer on top of the Qdrant database.
Components
Tools
Details
- Repository
- github.com/qdrant/mcp-server-qdrant
- Category
- AI & LLM Tools