Qdrant Model Context Protocol (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.
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.
qdrant-store
information (string): Information to storemetadata (JSON): Optional metadata to storeqdrant-find
query (string): Query to use for searchingThe 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.
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
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 clientssse: Server-Sent Events transport, perfect for remote clientsThe default transport is stdio if not specified.
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
To install Qdrant MCP Server for Claude Desktop automatically via Smithery:
npx @smithery/cli install mcp-server-qdrant --client claude
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.
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.
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:
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.
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.
mcp-server-qdrant locallyThe 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.
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.