Qdrant

Use Qdrant as a semantic memory layer for your AI assistant, letting it store and retrieve information by meaning rather than exact keywords. - **Store semantic memories**— Ask your assistant to save information with optional metadata to Qdrant via`qdrant-store`for later retrieval. - **Retrieve relevant memories**— Use`qdrant-find`to search stored information with a natural language query and get matching results. - **Build a code snippet library**— Store code snippets with descriptions and retrieve them semantically, as shown in the Cursor and Claude Code setups. - **Target specific collections**— Store or search in a named collection via`collection_name`, or rely on the default set by`COLLECTION_NAME`. 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](https://modelcontextprotocol.io/introduction)[Qdrant, a vector search engine. 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. - `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. - 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. Configuration is done via environment variables. The only command-line argument is`--transport`, used to select the](https://qdrant.tech/)[transport protocol. ](#transport-protocols)[!NOTE] You cannot provide both`QDRANT_URL`and`QDRANT_LOCAL_PATH`at the same time. Since`mcp-server-qdrant`is based on FastMCP, it also supports all the FastMCP environment variables. The most important ones are listed below: [!NOTE] Server-specific settings use the`FASTMCP_SERVER_`prefix. This may change in future versions. 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` ``` - `stdio`(default): Standard input/output transport, might only be used by local MCP clients - `sse`: Server-Sent Events transport, perfect for remote clients - `streamable-http`: Streamable HTTP transport, perfect for remote clients, more recent than SSE The default transport is`stdio`if not specified. When SSE transport is used, the server will listen on the specified port and wait for incoming connections. The default port is 8000, however it can be changed using the`FASTMCP_SERVER_PORT`environment variable. ``` `QDRANT_URL="http://localhost:6333" \ COLLECTION_NAME="my-collection" \ FASTMCP_SERVER_PORT=1234 \ uvx mcp-server-qdrant --transport sse` ``` 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 FASTMCP_SERVER_HOST="0.0.0.0" \ -e QDRANT_URL="http://your-qdrant-server:6333" \ -e QDRANT_API_KEY="your-api-key" \ -e COLLECTION_NAME="your-collection" \ mcp-server-qdrant` ``` ](https://docs.astral.sh/uv/guides/tools/#running-tools)[!TIP] Please note that we set`FASTMCP_SERVER_HOST="0.0.0.0"`to make the server listen on all network interfaces. This is necessary when running the server in a Docker container. 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": ](https://smithery.ai/protocol/mcp-server-qdrant)["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" } } }` ``` ``` `{ "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[FastEmbedmodels are supported. This MCP server can be used with any MCP-compatible client. For example, you can use it with](https://qdrant.github.io/fastembed/)[Cursorand](https://docs.cursor.com/context/model-context-protocol)[VS Code, which provide 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](https://code.visualstudio.com/docs)[Cursor documentation. If you are running Cursor/Windsurf locally, you can use the following URL: ](https://docs.cursor.com/context/model-context-protocol#adding-an-mcp-server-to-cursor)[!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 rulesso 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. You can enhance Claude Code's capabilities by connecting it to this MCP server, enabling semantic search over your existing codebase. ``` `# 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` ``` #### 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. 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` ``` For one-click installation, click one of the install buttons below: 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": ](https://docs.cursor.com/context/rules-for-ai)[ { "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}" } } } }` ``` 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. The[MCP inspectoris 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](https://github.com/modelcontextprotocol/inspector)[http://localhost:5173to 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. Semantic search using the Qdrant vector database. Search, Query and interact with data in your Milvus Vector Database. Implement semantic memory layer on top of the Qdrant vector search engine An MCP server for vector storage and retrieval using ChromaDB. Interact with on-disk documents using agentic RAG and hybrid search via LanceDB. Connect AI tools with Pinecone projects to search, configure indexes, generate code, and manage data. A containerized MCP server for LanceDB vector search, featuring hybrid processing with Gemini and Ollama. Semantic code search using the Qdrant vector database and OpenAI embeddings. Embeddings, vector search, document storage, and full-text search with the open-source AI application database Semantic search over 4.6M+ text chunks from 20,000+ classical philosophy and humanities works — Plato, Aristotle, Kant, Nietzsche, and thousands more. Multilingual (Greek, Latin, German, French, English). No install needed.](http://localhost:5173)