Docker MCP Server

What is this?

The Docker MCP Server is a specialized implementation of the Model Context Protocol (MCP) that empowers AI agents and developer tools to interact programmatically with Docker. Packaged as @modelcontextprotocol/server-docker, this server bridges the gap between container management and AI-driven automation.

It exposes Docker container lifecycle operations, image management, log retrieval, networking, and volume orchestration through a unified MCP interface, allowing agents to build, run, inspect, and troubleshoot containers without manual CLI intervention.

As containerization cements its role in modern development and DevOps workflows, automation of container tasks is increasingly important. By standardizing these operations under the MCP umbrella, Docker MCP Server enables AI-driven pipelines to spin up environments, generate Dockerfiles, diagnose services, and orchestrate multi-container setups through simple protocol calls.

With support for resources like DockerImage, DockerContainer, DockerNetwork, and tools such as buildImage, runContainer, and inspectContainer, this server facilitates automated container lifecycles, reproducible image generation, embedded debugging via log streams and exec commands, and orchestration of networks and volumes. It seamlessly integrates with MCP-enabled platforms like Cursor and Claude Desktop.

Quick Start

Install the server using npm:

npm install @modelcontextprotocol/server-docker

Then add it to your MCP client configuration:

{
  "mcpServers": {
    "server-docker": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-docker"],
      "env": {
        "API_KEY": "your-api-key-here"
      }
    }
  }
}

Key Features

Feature 1: Automated container lifecycle management, allowing AI agents to build, start, inspect, and stop containers on demand.

Feature 2: AI-assisted Dockerfile generation for reproducible and optimized image builds based on contextual prompts.

Feature 3: Comprehensive orchestration support with network, volume, and multi-container deployment via a unified protocol interface.

Example Usage

Suppose you have an AI agent that needs to build an application image. It can call the buildImage tool directly, passing in Dockerfile content and a tag to produce a ready-to-run Docker image.

// Example code
const result = await client.callTool({
  name: "buildImage",
  arguments: {
    dockerfile: "FROM node:18-alpine\nWORKDIR /app\nCOPY . .\nRUN npm install\nCMD [\"node\",\"server.js\"]",
    tag: "myapp:latest"
  }
});

This code uses the buildImage tool to create an image tagged myapp:latest and returns the image ID along with build logs.

Configuration

The server accepts the following environment variables:

API_KEY – Your MCP API key for authenticating requests.

LOG_LEVEL (optional) – Set to debug for verbose server logs.

Available Tools/Resources

buildImage: Builds a Docker image from Dockerfile content or context path and returns the image ID and build logs.

runContainer: Launches a container with environment variables, port mappings, and resource limits.

inspectContainer: Retrieves detailed metadata, status, and runtime statistics for a running container.

Who Should Use This?

This server is perfect for:

Use case 1: DevOps teams that need to automate container lifecycles without manual scripting.

Use case 2: AI developers integrating container operations into intelligent workflows.

Use case 3: Platform engineering groups building self-service container management interfaces.

Conclusion

Docker MCP Server brings container management into the AI-driven era, letting you automate builds, deployments, and debugging through simple protocol calls. Install, configure your API key, and start building smarter container workflows today!

Check out the GitHub repository for more information and to contribute.