mcp-server-qdrant

# mcp-server-qdrant: A Qdrant MCP server [](https://smithery.ai/protocol/mcp-server-qdrant) > The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) 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](https://qdrant.tech/), a vector search engine. <a href="https://glama.ai/mcp/servers/9ejy5scw5i"><img width="380" height="200" src="https://glama.ai/mcp/servers/9ejy5scw5i/badge" alt="mcp-server-qdrant MCP server" /></a> ## 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 1. `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 2. `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 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 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`](src/mcp_server_qdrant/settings.py) | | `TOOL_FIND_DESCRIPTION` | Custom description for the find tool | See default in [`settings.py`](src/mcp_server_qdrant/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`](https://docs.astral.sh/uv/guides/tools/#running-tools) no specific installation is needed to directly run *mcp-server-qdrant*. ```shell 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: ```shell 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: ```bash # 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](https://smithery.ai/protocol/mcp-server-qdrant): ```bash 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`: ```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: ```json { "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](https://qdrant.github.io/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](https://docs.cursor.com/context/model-context-protocol) and [VS Code](https://code.visualstudio.com/docs), 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: ```bash 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](https://docs.cursor.com/context/model-context-protocol#adding-an-mcp-server-to-cursor). 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](https://docs.cursor.com/context/rules-for-ai) 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 1. Add the MCP server to Claude Code: ```shell # 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 ``` 2. Verify the server was added: ```shell 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: 1. Use the `qdrant-store` tool to store code snippets with descriptions. 2. 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. ```shell COLLECTION_NAME=mcp-dev mcp dev src/mcp_server_qdrant/server.py ``` ### Using with VS Code For one-click installation, click one of the install buttons below: [](https://insiders.vscode.dev/redirect/mcp/install?name=qdrant&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22mcp-server-qdrant%22%5D%2C%22env%22%3A%7B%22QDRANT_URL%22%3A%22%24%7Binput%3AqdrantUrl%7D%22%2C%22QDRANT_API_KEY%22%3A%22%24%7Binput%3AqdrantApiKey%7D%22%2C%22COLLECTION_NAME%22%3A%22%24%7Binput%3AcollectionName%7D%22%7D%7D&inputs=%5B%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantUrl%22%2C%22description%22%3A%22Qdrant+URL%22%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantApiKey%22%2C%22description%22%3A%22Qdrant+API+Key%22%2C%22password%22%3Atrue%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22collectionName%22%2C%22description%22%3A%22Collection+Name%22%7D%5D) [](https://insiders.vscode.dev/redirect/mcp/install?name=qdrant&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22mcp-server-qdrant%22%5D%2C%22env%22%3A%7B%22QDRANT_URL%22%3A%22%24%7Binput%3AqdrantUrl%7D%22%2C%22QDRANT_API_KEY%22%3A%22%24%7Binput%3AqdrantApiKey%7D%22%2C%22COLLECTION_NAME%22%3A%22%24%7Binput%3AcollectionName%7D%22%7D%7D&inputs=%5B%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantUrl%22%2C%22description%22%3A%22Qdrant+URL%22%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantApiKey%22%2C%22description%22%3A%22Qdrant+API+Key%22%2C%22password%22%3Atrue%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22collectionName%22%2C%22description%22%3A%22Collection+Name%22%7D%5D&quality=insiders) [](https://insiders.vscode.dev/redirect/mcp/install?name=qdrant&config=%7B%22command%22%3A%22docker%22%2C%22args%22%3A%5B%22run%22%2C%22-p%22%2C%228000%3A8000%22%2C%22-i%22%2C%22--rm%22%2C%22-e%22%2C%22QDRANT_URL%22%2C%22-e%22%2C%22QDRANT_API_KEY%22%2C%22-e%22%2C%22COLLECTION_NAME%22%2C%22mcp-server-qdrant%22%5D%2C%22env%22%3A%7B%22QDRANT_URL%22%3A%22%24%7Binput%3AqdrantUrl%7D%22%2C%22QDRANT_API_KEY%22%3A%22%24%7Binput%3AqdrantApiKey%7D%22%2C%22COLLECTION_NAME%22%3A%22%24%7Binput%3AcollectionName%7D%22%7D%7D&inputs=%5B%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantUrl%22%2C%22description%22%3A%22Qdrant+URL%22%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantApiKey%22%2C%22description%22%3A%22Qdrant+API+Key%22%2C%22password%22%3Atrue%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22collectionName%22%2C%22description%22%3A%22Collection+Name%22%7D%5D) [](https://insiders.vscode.dev/redirect/mcp/install?name=qdrant&config=%7B%22command%22%3A%22docker%22%2C%22args%22%3A%5B%22run%22%2C%22-p%22%2C%228000%3A8000%22%2C%22-i%22%2C%22--rm%22%2C%22-e%22%2C%22QDRANT_URL%22%2C%22-e%22%2C%22QDRANT_API_KEY%22%2C%22-e%22%2C%22COLLECTION_NAME%22%2C%22mcp-server-qdrant%22%5D%2C%22env%22%3A%7B%22QDRANT_URL%22%3A%22%24%7Binput%3AqdrantUrl%7D%22%2C%22QDRANT_API_KEY%22%3A%22%24%7Binput%3AqdrantApiKey%7D%22%2C%22COLLECTION_NAME%22%3A%22%24%7Binput%3AcollectionName%7D%22%7D%7D&inputs=%5B%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantUrl%22%2C%22description%22%3A%22Qdrant+URL%22%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22qdrantApiKey%22%2C%22description%22%3A%22Qdrant+API+Key%22%2C%22password%22%3Atrue%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22collectionName%22%2C%22description%22%3A%22Collection+Name%22%7D%5D&quality=insiders) #### 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)`. ```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": "${input:collectionName}" } } } } } ``` Or if you prefer using Docker, add this configuration instead: ```json { "mcp": { "inputs": [ { "type": "promptString", "id": "qdrantUrl", "description": "Qdrant URL" }, { "type": "promptString", "id": "qdrantApiKey", "description": "Qdrant API Key", "password": true }, { "type": "promptString", "id": "collectionName", "description": "Collection Name" } ], "servers": { "qdrant": { "command": "docker", "args": [ "run", "-p", "8000:8000", "-i", "--rm", "-e", "QDRANT_URL", "-e", "QDRANT_API_KEY", "-e", "COLLECTION_NAME", "mcp-server-qdrant" ], "env": { "QDRANT_URL": "${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: ```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": "uvx", "args": ["mcp-server-qdrant"], "env": { "QDRANT_URL": "${input:qdrantUrl}", "QDRANT_API_KEY": "${input:qdrantApiKey}", "COLLECTION_NAME": "${input:collectionName}" } } } } ``` For workspace configuration with Docker, use this in `.vscode/mcp.json`: ```json { "inputs": [ { "type": "promptString", "id": "qdrantUrl", "description": "Qdrant URL" }, { "type": "promptString", "id": "qdrantApiKey", "description": "Qdrant API Key", "password": true }, { "type": "promptString", "id": "collectionName", "description": "Collection Name" } ], "servers": { "qdrant": { "command": "docker", "args": [ "run", "-p", "8000:8000", "-i", "--rm", "-e", "QDRANT_URL", "-e", "QDRANT_API_KEY", "-e", "COLLECTION_NAME", "mcp-server-qdrant" ], "env": { "QDRANT_URL": "${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](https://github.com/modelcontextprotocol/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. ```shell 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.