Qdrant MCP Server

@davidwynter

Repository

Basic information

About Qdrant MCP Server

A simple MCP server to access Qdrant

#qdrant #mcpserver #fastapi

Qdrant MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "qdrant_mcpserver": {
      "command": "python",
      "args": [
        "-m",
        "qdrant_mcpserver.main",
        "--server-type",
        "fastmcp"
      ]
    }
  }
}

Qdrant MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Qdrant MCP Server?

A dual-protocol server that provides both FastAPI and FastMCP clients for accessing Qdrant knowledge graph operations. It is designed for developers who need to interact with Qdrant vector databases using either REST APIs or the Model Context Protocol.

How to use Qdrant MCP Server?

Install dependencies with Poetry after cloning the repository. Configure environment variables (QDRANT_URL, OPENAI_API_KEY, etc.). Run the server using python -m qdrant_mcpserver.main with an optional --server-type argument to choose between FastAPI (default: FastMCP). Both servers expose the same endpoints.

Key features of Qdrant MCP Server?

Flexible server selection via CLI argument
Shared configuration and Qdrant client
Consistent endpoints across both protocols
Poetry for dependency management
Environment variable configuration
Production-ready build and deployment instructions

Use cases of Qdrant MCP Server?

Upserting knowledge graph nodes into Qdrant
Performing semantic vector search across nodes
Deleting nodes by their IDs
Running health checks on the server

Frequently asked questions

What dependencies are required?

The server requires a running Qdrant instance (URL and optional API key), an OpenAI API key for embedding generation, and Python with Poetry for installation.