mcp-server-qdrant-embeddings: Qdrant MCP server with extended embedding providers

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 independently maintained distribution is based on the official Qdrant MCP server and follows its upstream development while adding OpenAI, Vertex AI, and Cohere embedding providers. It is maintained as an independent distribution.

Overview

An 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

Configuration is done via environment variables. The only command-line argument is --transport, used to select the transport protocol.

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	Provider: fastembed, openai, vertexai, or cohere	fastembed
EMBEDDING_MODEL	Name of the embedding model to use	sentence-transformers/all-MiniLM-L6-v2
EMBEDDING_API_KEY	API key used by OpenAI or Cohere providers	None
GOOGLE_CLOUD_PROJECT	Google Cloud project ID for Vertex AI	Detected by Google credentials
GOOGLE_CLOUD_LOCATION	Vertex AI region	us-central1
EMBEDDING_VECTOR_SIZE	Vector dimension for OpenAI, Vertex AI, or Cohere	None
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-embeddings 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	[]

Installation

Using uvx

When using uvx no specific installation is needed to directly run mcp-server-qdrant-embeddings.

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
uvx mcp-server-qdrant-embeddings

Transport Protocols

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-embeddings --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
• 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_PORT=1234 \
uvx mcp-server-qdrant-embeddings --transport sse

Using Docker

A Dockerfile is available for building and running the MCP server:

# Build the container
docker build -t mcp-server-qdrant-embeddings .

# 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-embeddings

Installing via Smithery

The Smithery listing below installs the official upstream Qdrant server, not this distribution:

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:

{
  "qdrant": {
    "command": "uvx",
    "args": ["mcp-server-qdrant-embeddings"],
    "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-embeddings"],
    "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 uses sentence-transformers/all-MiniLM-L6-v2 with FastEmbed. The supported provider values are fastembed, openai, vertexai, and cohere. Set EMBEDDING_VECTOR_SIZE to the chosen model's output dimension when using a hosted provider; it is used when creating a Qdrant collection.

OpenAI example:

EMBEDDING_PROVIDER=openai \
EMBEDDING_MODEL=text-embedding-3-small \
EMBEDDING_API_KEY=your-openai-api-key \
EMBEDDING_VECTOR_SIZE=1536 \
uvx mcp-server-qdrant-embeddings

Vertex AI example (using Application Default Credentials):

EMBEDDING_PROVIDER=vertexai \
EMBEDDING_MODEL=gemini-embedding-001 \
GOOGLE_CLOUD_PROJECT=your-project-id \
GOOGLE_CLOUD_LOCATION=us-central1 \
EMBEDDING_VECTOR_SIZE=3072 \
uvx mcp-server-qdrant-embeddings

Cohere example:

EMBEDDING_PROVIDER=cohere \
EMBEDDING_MODEL=embed-v4.0 \
EMBEDDING_API_KEY=your-cohere-api-key \
EMBEDDING_VECTOR_SIZE=1536 \
uvx mcp-server-qdrant-embeddings

Cohere uses search_document for stored text and search_query for searches. Documents are sent in batches of at most 96, as required by the Cohere Embed API.

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-embeddings --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

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.

If you have successfully installed mcp-server-qdrant-embeddings 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-embeddings

1. Add the MCP server to Claude Code:

   # Add mcp-server-qdrant-embeddings 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-embeddings
   

2. 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:

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.

COLLECTION_NAME=mcp-dev fastmcp dev src/mcp_server_qdrant/server.py

Using with VS Code

The one-click buttons below are maintained by upstream and install the official Qdrant server. To use this distribution, use the manual configurations below with mcp-server-qdrant-embeddings.

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-embeddings"],
        "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-embeddings"
        ],
        "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-embeddings"],
      "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-embeddings"
      ],
      "env": {
        "QDRANT_URL": "${input:qdrantUrl}",
        "QDRANT_API_KEY": "${input:qdrantApiKey}",
        "COLLECTION_NAME": "${input:collectionName}"
      }
    }
  }
}

Contributing

If you have suggestions for improving this project or want to report a bug, open an issue in this repository.

Testing mcp-server-qdrant-embeddings 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.

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