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Implement semantic memory layer on top of the Qdrant vector search engine

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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 store

  • metadata (JSON): Optional metadata to store

  • collection_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 searching

  • collection_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:

Copy & paste — that's it
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:

Copy & paste — that's it
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:

Copy & paste — that's it
# 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:

Copy & paste — that's it
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.

Copy & paste — that's it
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).

Copy & paste — that's it
{
 "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:

Copy & paste — that's it
{
 "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:

Copy & paste — that's it
{
 "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:

Copy & paste — that's it
{
 "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.

Copy & paste — that's it
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.