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
qdrant-store
- Store some information in the Qdrant database
- Input:
information (string): Information to storemetadata (JSON): Optional metadata to storecollection_name (string): Name of the collection to store the information in. This field is required if there are no default collection name.
If there is a default collection name, this field is not enabled.
- Returns: Confirmation message
qdrant-find
- Retrieve relevant information from the Qdrant database
- Input:
query (string): Query to use for searchingcollection_name (string): Name of the collection to store the information in. This field is required if there are no default collection name.
If there is a default collection name, this field is not enabled.
- Returns: Information stored in the Qdrant database as separate messages
Environment Variables
Configuration is done via environment variables. The only command-line argument is --transport, used to select the transport protocol.
[!NOTE]
You cannot provide both QDRANT_URL and QDRANT_LOCAL_PATH at the same time.
| 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 default collection to use. | None |
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 |
QDRANT_SEARCH_LIMIT | Maximum number of results to return from search | 10 |
QDRANT_READ_ONLY | Enable read-only mode (disables qdrant-store tool) | false |
FastMCP Environment Variables
Since mcp-server-qdrant is based on FastMCP, it also supports all the FastMCP environment variables. The most
important ones are listed below:
| Environment Variable | Description | Default Value |
|---|
FASTMCP_LOG_LEVEL | Set logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) | INFO |
FASTMCP_SERVER_DEBUG | Enable debug mode | false |
FASTMCP_SERVER_HOST | Host address to bind the server to | 127.0.0.1 |
FASTMCP_SERVER_PORT | Port to run the server on | 8000 |
FASTMCP_SERVER_ON_DUPLICATE_RESOURCES | Behavior for duplicate resources (warn, error, replace, ignore) | warn |
FASTMCP_SERVER_ON_DUPLICATE_TOOLS | Behavior for duplicate tools (warn, error, replace, ignore) | warn |
FASTMCP_SERVER_ON_DUPLICATE_PROMPTS | Behavior for duplicate prompts (warn, error, replace, ignore) | warn |
FASTMCP_SERVER_DEPENDENCIES | List of dependencies to install in the server environment | [] |
[!NOTE]
Server-specific settings use the FASTMCP_SERVER_ prefix. This may change in future versions.
Support for other tools
This MCP server can be used with any MCP-compatible client. For example, you can use it with
Cursor and VS Code, which provide 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:
- Store code snippets, documentation, and implementation details
- Retrieve relevant code examples based on semantic search
- 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.
Using with Claude Code
You can enhance Claude Code's capabilities by connecting it to this MCP server, enabling semantic search over your
existing codebase.
Setting up mcp-server-qdrant
-
Add the MCP server to Claude Code:
# Add mcp-server-qdrant configured for code search
claude mcp add code-search \
-e QDRANT_URL="http://localhost:6333" \
-e COLLECTION_NAME="code-repository" \
-e EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
-e TOOL_STORE_DESCRIPTION="Store code snippets with descriptions. 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." \
-e TOOL_FIND_DESCRIPTION="Search for relevant code snippets using natural language. The 'query' parameter should describe the functionality you're looking for." \
-- uvx mcp-server-qdrant
-
Verify the server was added:
claude mcp list
Using Semantic Code Search in Claude Code
Tool descriptions, specified in TOOL_STORE_DESCRIPTION and TOOL_FIND_DESCRIPTION, guide Claude Code on how to use
the MCP server. The ones provided above are examples and may need to be customized for your specific use case. However,
Claude Code should be already able to:
- Use the
qdrant-store tool to store code snippets with descriptions.
- Use the
qdrant-find tool to search for relevant code snippets using natural language.
Run MCP server in Development Mode
The MCP server can be run in development mode using the mcp dev command. This will start the server and open the MCP
inspector in your browser.
COLLECTION_NAME=mcp-dev fastmcp dev src/mcp_server_qdrant/server.py
Using with VS Code
For one-click installation, click one of the install buttons below:
Manual Installation
Add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).
{
"mcp": {
"inputs": [
{
"type": "promptString",
"id": "qdrantUrl",
"description": "Qdrant URL"
},
{
"type": "promptString",
"id": "qdrantApiKey",
"description": "Qdrant API Key",
"password": true
},
{
"type": "promptString",
"id": "collectionName",
"description": "Collection Name"
}
],
"servers": {
"qdrant": {
"command": "uvx",
"args": ["mcp-server-qdrant"],
"env": {
"QDRANT_URL": "${input:qdrantUrl}",
"QDRANT_API_KEY": "${input:qdrantApiKey}",
"COLLECTION_NAME": "${input:collectionName}"
}
}
}
}
}
Or if you prefer using Docker, add this configuration instead:
{
"mcp": {
"inputs": [
{
"type": "promptString",
"id": "qdrantUrl",
"description": "Qdrant URL"
},
{
"type": "promptString",
"id": "qdrantApiKey",
"description": "Qdrant API Key",
"password": true
},
{
"type": "promptString",
"id": "collectionName",
"description": "Collection Name"
}
],
"servers": {
"qdrant": {
"command": "docker",
"args": [
"run",
"-p", "8000:8000",
"-i",
"--rm",
"-e", "QDRANT_URL",
"-e", "QDRANT_API_KEY",
"-e", "COLLECTION_NAME",
"mcp-server-qdrant"
],
"env": {
"QDRANT_URL": "${input:qdrantUrl}",
"QDRANT_API_KEY": "${input:qdrantApiKey}",
"COLLECTION_NAME": "${input:collectionName}"
}
}
}
}
}
Alternatively, you can create a .vscode/mcp.json file in your workspace with the following content:
{
"inputs": [
{
"type": "promptString",
"id": "qdrantUrl",
"description": "Qdrant URL"
},
{
"type": "promptString",
"id": "qdrantApiKey",
"description": "Qdrant API Key",
"password": true
},
{
"type": "promptString",
"id": "collectionName",
"description": "Collection Name"
}
],
"servers": {
"qdrant": {
"command": "uvx",
"args": ["mcp-server-qdrant"],
"env": {
"QDRANT_URL": "${input:qdrantUrl}",
"QDRANT_API_KEY": "${input:qdrantApiKey}",
"COLLECTION_NAME": "${input:collectionName}"
}
}
}
}
For workspace configuration with Docker, use this in .vscode/mcp.json:
{
"inputs": [
{
"type": "promptString",
"id": "qdrantUrl",
"description": "Qdrant URL"
},
{
"type": "promptString",
"id": "qdrantApiKey",
"description": "Qdrant API Key",
"password": true
},
{
"type": "promptString",
"id": "collectionName",
"description": "Collection Name"
}
],
"servers": {
"qdrant": {
"command": "docker",
"args": [
"run",
"-p", "8000:8000",
"-i",
"--rm",
"-e", "QDRANT_URL",
"-e", "QDRANT_API_KEY",
"-e", "COLLECTION_NAME",
"mcp-server-qdrant"
],
"env": {
"QDRANT_URL": "${input:qdrantUrl}",
"QDRANT_API_KEY": "${input:qdrantApiKey}",
"COLLECTION_NAME": "${input:collectionName}"
}
}
}
}
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" \
fastmcp 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.
Privacy and support