The Qdrant MCP server provides a Model Context Protocol implementation for Qdrant vector search engine, enabling seamless integration between LLM applications and vector-based data storage. It functions as a semantic memory layer allowing you to store and retrieve information using natural language queries.
The simplest way to run the server is with uvx:
QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
uvx mcp-server-qdrant
# 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
For Claude Desktop:
npx @smithery/cli install mcp-server-qdrant --client claude
The server is configured using environment variables:
| Variable | Description | Default |
|---|---|---|
| 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 local Qdrant database (alternative to URL) | None |
| EMBEDDING_PROVIDER | Embedding provider (currently only "fastembed") | fastembed |
| EMBEDDING_MODEL | Name of the embedding model | sentence-transformers/all-MiniLM-L6-v2 |
| TOOL_STORE_DESCRIPTION | Custom description for the store tool | See settings.py |
| TOOL_FIND_DESCRIPTION | Custom description for the find tool | See settings.py |
Note: You cannot provide both QDRANT_URL and QDRANT_LOCAL_PATH at the same time.
The server supports different transport protocols:
QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
uvx mcp-server-qdrant --transport sse
Supported protocols:
Add the following 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"
}
}
}
Configure the server as a code search tool:
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
In Cursor/Windsurf, add the MCP server using the SSE transport protocol at: http://localhost:8000/sse
Stores information in the Qdrant database.
Input:
Returns: Confirmation message
Retrieves relevant information from the Qdrant database.
Input:
Returns: Information stored in the Qdrant database as separate messages
To add this MCP server to Claude Code, run this command in your terminal:
claude mcp add-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"}}'See the official Claude Code MCP documentation for more details.
There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.
If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.
To add a global MCP server go to Cursor Settings > Tools & Integrations and click "New MCP Server".
When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:
{
"mcpServers": {
"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"
}
}
}
}To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.
Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.
The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.
You can also explicitly ask the agent to use the tool by mentioning the tool name and describing what the function does.
To add this MCP server to Claude Desktop:
1. Find your configuration file:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json~/.config/Claude/claude_desktop_config.json2. Add this to your configuration file:
{
"mcpServers": {
"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"
}
}
}
}3. Restart Claude Desktop for the changes to take effect