mcp-server-docker MCP Server

What does “mcp-server-docker” MCP Server do?

The mcp-server-docker MCP Server is a specialized Model Context Protocol (MCP) server designed to provide AI assistants with the ability to manage Docker containers seamlessly via natural language. By connecting AI agents to Docker, it enables automated container orchestration, introspection, debugging, and persistent data management, all through standardized MCP interfaces. This server empowers developers, system administrators, and AI enthusiasts to interact with Docker environments—locally or remotely—simplifying workflows such as spinning up new services, managing running containers, and handling Docker volumes. The integration of MCP with Docker enhances productivity, reduces manual intervention, and opens up new avenues for AI-driven development and operations.

List of Prompts

• docker_compose
  Use natural language to compose and manage containers. This prompt guides the LLM through a plan/apply workflow: you describe desired containers and configurations, and the LLM generates a plan which you can review, approve, or modify before application.

List of Resources

• Containers
  Exposes information about running and available Docker containers, allowing AI clients to inspect or interact with them.
• Volumes
  Provides access to Docker volumes for managing persistent data, enabling clients to list, create, or remove volumes.
• Networks
  Surfaces available Docker networks, which can be used by clients for connecting containers or managing network settings.

List of Tools

• docker_compose
  Enables the creation and orchestration of multi-container Docker applications using natural language instructions.
• container_introspection
  Allows introspection and debugging of running containers, providing details on status, configuration, and logs.
• volume_management
  Facilitates management of Docker volumes, including creation, listing, and deletion for persistent storage.

Use Cases of this MCP Server

• Natural Language Container Deployment
  Deploy and manage Docker containers by simply describing the desired setup in plain language, streamlining development and testing workflows.
• Remote Server Administration
  Connect to remote Docker engines to manage web servers or cloud workloads, simplifying ops for administrators.
• Container Debugging and Introspection
  Use AI to inspect, debug, and manage running containers, reducing the time needed for troubleshooting.
• Persistent Data Management
  Handle Docker volumes directly from AI tools, making it easier to manage, back up, and clean up persistent data.
• Experimentation with Open-source Apps
  Quickly spin up and test open-source applications that use Docker, helping tinkerers and developers evaluate new tools efficiently.

How to set it up

Windsurf

1. Ensure you have Node.js and the Windsurf application installed.
2. Open your Windsurf configuration file.
3. Add the following entry to your mcpServers object:

   "mcpServers": {
     "mcp-server-docker": {
       "command": "uvx",
       "args": [
         "mcp-server-docker"
       ]
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the server is running and accessible.

Claude

1. Install uv if not already present.
2. On MacOS, locate ~/Library/Application Support/Claude/claude_desktop_config.json.
   On Windows, locate %APPDATA%/Claude/claude_desktop_config.json.
3. Add the following to your mcpServers section:

   "mcpServers": {
     "mcp-server-docker": {
       "command": "uvx",
       "args": [
         "mcp-server-docker"
       ]
     }
   }
   

4. Save the file and restart Claude.
5. Confirm the MCP server is listed and functional.

Cursor

1. Install uv.
2. Open Cursor’s configuration file.
3. Insert the following JSON into the mcpServers object:

   "mcpServers": {
     "mcp-server-docker": {
       "command": "uvx",
       "args": [
         "mcp-server-docker"
       ]
     }
   }
   

4. Save and restart Cursor.
5. Check that the Docker MCP server appears in the tool list.

Cline

1. Make sure Node.js and uv are installed.
2. Edit Cline’s configuration file.
3. Add the MCP server entry:

   "mcpServers": {
     "mcp-server-docker": {
       "command": "uvx",
       "args": [
         "mcp-server-docker"
       ]
     }
   }
   

4. Save changes and restart Cline.
5. Verify the MCP server is operating by running a test command.

Securing API Keys

To secure API keys, use environment variables in your configuration. Example:

"mcpServers": {
  "mcp-server-docker": {
    "command": "uvx",
    "args": [
      "mcp-server-docker"
    ],
    "env": {
      "DOCKER_HOST": "${DOCKER_HOST_ENV_VAR}"
    },
    "inputs": {
      "api_key": "${DOCKER_API_KEY_ENV_VAR}"
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "mcp-server-docker": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “mcp-server-docker” to your actual MCP server name and replace the URL with your own MCP server endpoint.

Overview

Our opinion

The mcp-server-docker MCP provides clear documentation, practical prompt workflows, and robust Docker integration. Its focus on natural language orchestration and introspection makes it especially valuable for developers and AI-powered operations. However, details on advanced MCP features such as Roots and Sampling are not present. Overall, it’s a mature, highly usable MCP server for Docker automation.

MCP Score