mcp-server-qdrant: A Qdrant MCP server

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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

A basic 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

1. qdrant-store
   • Store some information in the Qdrant database
   • Input:
     • information (string): Information to store
     • metadata (JSON): Optional metadata to store
   • Returns: Confirmation message
2. qdrant-find
   • Retrieve relevant information from the Qdrant database
   • Input:
     • query (string): Query to use for searching
   • Returns: Information stored in the Qdrant database as separate messages

Environment Variables

The configuration of the server is done using environment variables:

Name	Description	Default Value
QDRANT_URL	URL of the Qdrant server	None
QDRANT_API_KEY	API key for the Qdrant server	None
COLLECTION_NAME	Name of the collection to use	Required
QDRANT_LOCAL_PATH	Path to the local Qdrant database (alternative to QDRANT_URL)	None
EMBEDDING_PROVIDER	Embedding provider to use (currently only "fastembed" is supported)	fastembed
EMBEDDING_MODEL	Name of the embedding model to use	sentence-transformers/all-MiniLM-L6-v2
TOOL_STORE_DESCRIPTION	Custom description for the store tool	See default in settings.py
TOOL_FIND_DESCRIPTION	Custom description for the find tool	See default in settings.py

Note: You cannot provide both QDRANT_URL and QDRANT_LOCAL_PATH at the same time.

[!IMPORTANT] Command-line arguments are not supported anymore! Please use environment variables for all configuration.

Installation

Using uvx

When using uvx no specific installation is needed to directly run mcp-server-qdrant.

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
uvx mcp-server-qdrant

Transport Protocols

The server supports different transport protocols that can be specified using the --transport flag:

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
uvx mcp-server-qdrant --transport sse

Supported transport protocols:

• stdio (default): Standard input/output transport, might only be used by local MCP clients
• sse: Server-Sent Events transport, perfect for remote clients

The default transport is stdio if not specified.

Using Docker

A Dockerfile is available for building and running the MCP server:

# Build the container
docker build -t mcp-server-qdrant .

# Run the container
docker run -p 8000:8000 \
  -e QDRANT_URL="http://your-qdrant-server:6333" \
  -e QDRANT_API_KEY="your-api-key" \
  -e COLLECTION_NAME="your-collection" \
  mcp-server-qdrant

Installing via Smithery

To install Qdrant MCP Server for Claude Desktop automatically via Smithery:

npx @smithery/cli install mcp-server-qdrant --client claude

Manual configuration of Claude Desktop

To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your claude_desktop_config.json:

{
  "qdrant": {
    "command": "uvx",
    "args": ["mcp-server-qdrant"],
    "env": {
      "QDRANT_URL": "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333",
      "QDRANT_API_KEY": "your_api_key",
      "COLLECTION_NAME": "your-collection-name",
      "EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
    }
  }
}

For local Qdrant mode:

{
  "qdrant": {
    "command": "uvx",
    "args": ["mcp-server-qdrant"],
    "env": {
      "QDRANT_LOCAL_PATH": "/path/to/qdrant/database",
      "COLLECTION_NAME": "your-collection-name",
      "EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
    }
  }
}

This MCP server will automatically create a collection with the specified name if it doesn't exist.

By default, the server will use the sentence-transformers/all-MiniLM-L6-v2 embedding model to encode memories. For the time being, only FastEmbed models are supported.

Support for other tools

This MCP server can be used with any MCP-compatible client. For example, you can use it with Cursor, which provides built-in support for the Model Context Protocol.

Using with Cursor/Windsurf

You can configure this MCP server to work as a code search tool for Cursor or Windsurf by customizing the tool descriptions:

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="code-snippets" \
TOOL_STORE_DESCRIPTION="Store reusable code snippets for later retrieval. \
The 'information' parameter should contain a natural language description of what the code does, \
while the actual code should be included in the 'metadata' parameter as a 'code' property. \
The value of 'metadata' is a Python dictionary with strings as keys. \
Use this whenever you generate some code snippet." \
TOOL_FIND_DESCRIPTION="Search for relevant code snippets based on natural language descriptions. \
The 'query' parameter should describe what you're looking for, \
and the tool will return the most relevant code snippets. \
Use this when you need to find existing code snippets for reuse or reference." \
uvx mcp-server-qdrant --transport sse # Enable SSE transport

In Cursor/Windsurf, you can then configure the MCP server in your settings by pointing to this running server using SSE transport protocol. The description on how to add an MCP server to Cursor can be found in the Cursor documentation. If you are running Cursor/Windsurf locally, you can use the following URL:

http://localhost:8000/sse

[!TIP] We suggest SSE transport as a preferred way to connect Cursor/Windsurf to the MCP server, as it can support remote connections. That makes it easy to share the server with your team or use it in a cloud environment.

This configuration transforms the Qdrant MCP server into a specialized code search tool that can:

1. Store code snippets, documentation, and implementation details
2. Retrieve relevant code examples based on semantic search
3. Help developers find specific implementations or usage patterns

You can populate the database by storing natural language descriptions of code snippets (in the information parameter) along with the actual code (in the metadata.code property), and then search for them using natural language queries that describe what you're looking for.

[!NOTE] The tool descriptions provided above are examples and may need to be customized for your specific use case. Consider adjusting the descriptions to better match your team's workflow and the specific types of code snippets you want to store and retrieve.

If you have successfully installed the mcp-server-qdrant, but still can't get it to work with Cursor, please consider creating the Cursor rules so the MCP tools are always used when the agent produces a new code snippet. You can restrict the rules to only work for certain file types, to avoid using the MCP server for the documentation or other types of content.

Contributing

If you have suggestions for how mcp-server-qdrant could be improved, or want to report a bug, open an issue! We'd love all and any contributions.

Testing mcp-server-qdrant locally

The MCP inspector is a developer tool for testing and debugging MCP servers. It runs both a client UI (default port 5173) and an MCP proxy server (default port 3000). Open the client UI in your browser to use the inspector.

QDRANT_URL=":memory:" COLLECTION_NAME="test" \
mcp dev src/mcp_server_qdrant/server.py

Once started, open your browser to http://localhost:5173 to access the inspector interface.

License

This MCP server is licensed under the Apache License 2.0. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the Apache License 2.0. For more details, please see the LICENSE file in the project repository.