mcp-docker-server 0.0.7

A Docker MCP Server

🐋 mcp-docker-server

Effortlessly orchestrate and oversee Docker environments from anywhere, using natural, conversational commands.

mcp-docker-server is an open-source MCP (Model Context Protocol) server that transforms Docker container management by bridging the gap between human intuition and automation. Built for DevOps professionals who demand both power and simplicity.

🎯 Core Capabilities

• 🗣️ Natural Language Orchestration: Compose multi-container environments using conversational commands
• 🔗 Remote SSH Management: Securely manage Docker environments across local and remote infrastructure
• 🔍 Intelligent Debugging: Introspect running containers with AI-assisted troubleshooting
• 📊 Unified Observability: Monitor container stats, logs, and resource usage from a single interface
• ⚡ Automation-Ready: Integrate with CI/CD pipelines and infrastructure-as-code workflows

👥 Built for DevOps Professionals

DevOps Engineers & SREs: Reduce cognitive load when managing complex container environments. Replace fragmented toolchains with unified, conversational control.

Cloud Architects & Consultants: Demonstrate rapid prototyping and environment setup to clients. Manage multiple client infrastructures with consistent, intuitive commands.

Open Source Contributors: Contribute to a tool that democratizes infrastructure automation. Help build the future of developer-friendly container management.

Scale-up Teams: Accelerate development velocity without sacrificing operational reliability. Perfect for teams transitioning from manual processes to automated infrastructure.

🚀 Quick Start

Prerequisites

• Docker: Ensure Docker is running locally or accessible via SSH
• Python 3.11+: Required for the MCP server
• Claude Desktop or Claude Code: For natural language interaction

Installation

Option 1: Direct Installation with uv (Recommended)

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh
# or: brew install uv

# Install mcp-docker-server
uvx mcp-docker-server

Option 2: Install from PyPI

pip install mcp-docker-server

Option 3: Install from Test PyPI (Testing/Preview versions)

# Install from Test PyPI with PyPI fallback for dependencies
pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ mcp-docker-server

Option 4: Development Installation

git clone https://github.com/pnmice/mcp-docker-server
cd mcp-docker-server
uv tool install .

# Force reinstall after updates
uv tool install --force --reinstall .

Configuration

Claude code

MacOS (with Docker Desktop):

Claude Code configuration:

claude mcp add mcp-docker-server --env DOCKER_HOST=unix:///Users/youruser/.docker/run/docker.sock -- uvx mcp-docker-server

Codex configuration:

cat ~/.codex/config.toml
[mcp_servers.mcp-docker-server]
command = "uvx"
args = ["mcp-docker-server"]

Claude Code configuration:

claude mcp add mcp-docker-server -- uvx mcp-docker-server

Codex configuration:

cat ~/.codex/config.toml
[mcp_servers.mcp-docker-server]
command = "uvx"
args = ["mcp-docker-server"]

Support ~/.ssh/config aliases:

Claude Code configuration:

claude mcp add mcp-docker-server-alias --env DOCKER_HOST=ssh://your-ssh-config-alias -- uvx mcp-docker-server

Codex configuration:

cat ~/.codex/config.toml
[mcp_servers.mcp-docker-server-alias]
command = "uvx"
args = ["mcp-docker-server"]
env = { DOCKER_HOST = "ssh://your-ssh-config-alias" }

Claude Desktop

Claude Desktop Configuration

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json

On Windows: %APPDATA%/Claude/claude_desktop_config.json

Then add the following to your MCP servers file:

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

Install with Docker

Purely for convenience, the server can run in a Docker container.

After cloning this repository, build the Docker image:

docker build -t mcp-docker-server .

And then add the following to your MCP servers file:

{
  "mcpServers": {
    "mcp-docker-server": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v",
        "/var/run/docker.sock:/var/run/docker.sock",
        "mcp-docker-server:latest"
      ]
    }
  }
}

Note that we mount the Docker socket as a volume; this ensures the MCP server can connect to and control the local Docker daemon.

📝 Prompts

🎻 docker_compose

Use natural language to compose containers.

Provide a Project Name, and a description of desired containers, and let the LLM do the rest.

This prompt instructs the LLM to enter a plan+apply loop. Your interaction with the LLM will involve the following steps:

1. You give the LLM instructions for which containers to bring up
2. The LLM calculates a concise natural language plan and presents it to you
3. You either:
   • Apply the plan
   • Provide the LLM feedback, and the LLM recalculates the plan

Examples

• name: nginx, containers: "deploy an nginx container exposing it on port 9000"
• name: wordpress, containers: "deploy a WordPress container and a supporting MySQL container, exposing Wordpress on port 9000"

Resuming a Project

When starting a new chat with this prompt, the LLM will receive the status of any containers, volumes, and networks created with the given project name.

This is mainly useful for cleaning up, in-case you lose a chat that was responsible for many containers.

📔 Resources

The server implements a couple resources for every container:

• Stats: CPU, memory, etc. for a container
• Logs: tail some logs from a container

🔨 Tools

Containers

• list_containers
• create_container
• run_container
• recreate_container
• start_container
• fetch_container_logs
• stop_container
• remove_container

Images

• list_images
• pull_image
• push_image
• build_image
• remove_image

Networks

• list_networks
• create_network
• remove_network

Volumes

• list_volumes
• create_volume
• remove_volume

Monitoring

• get_docker_disk_usage
• get_container_stats

🚧 Security & Best Practices

Sensitive Data

DO NOT CONFIGURE CONTAINERS WITH SENSITIVE DATA. This includes API keys, database passwords, etc.

Any sensitive data exchanged with the LLM is inherently compromised, unless the LLM is running on your local machine.

If you are interested in securely passing secrets to containers, file an issue on this repository with your use-case.

🛠️ Configuration

This server uses the Python Docker SDK's from_env method. For configuration details, see the documentation.

Connect to Docker over SSH

This MCP server can connect to a remote Docker daemon over SSH.

Using full SSH URLs

Set a ssh:// host URL with username and hostname in the MCP server definition:

{
  "mcpServers": {
    "mcp-docker-server": {
      "command": "uvx",
      "args": [
        "mcp-docker-server"
      ],
      "env": {
        "DOCKER_HOST": "ssh://[e-mail]"
      }
    }
  }
}

Using SSH config aliases

You can also use SSH config aliases defined in your ~/.ssh/config file:

{
  "mcpServers": {
    "mcp-docker-server": {
      "command": "uvx",
      "args": [
        "mcp-docker-server"
      ],
      "env": {
        "DOCKER_HOST": "ssh://your-ssh-config-alias"
      }
    }
  }
}

The server will automatically resolve SSH config aliases to their full connection details (hostname, username, port) from your SSH configuration.

💻 Development

Build docker image for development:

docker build -t mcp-docker-server:dev -f Dockerfile.dev .

Run the development container:

mv env.example .env
# replace your ssh config alias in .env
# DOCKER_HOST=ssh://your-ssh-config-alias
docker-compose -f docker-compose.dev.yaml up -d --build

Local development with Docker:

Claude Code configuration:

claude mcp add mcp-docker-server-dev --env DOCKER_HOST=unix:///Users/user/.docker/run/docker.sock -- docker run -i --rm -v /var/run/docker.sock:/var/run/docker.sock mcp-docker-server:dev mcp-run

Codex configuration:

cat ~/.codex/config.toml  

[mcp_servers.mcp-docker-server-dev]
command = "docker"
args = ["run", "-i", "--rm", "-v", "/var/run/docker.sock:/var/run/docker.sock", "mcp-docker-server:dev", "mcp-run"]

Remote development with Docker over SSH:

MacOs (with Docker Desktop):

brew install socat

mkdir -p ~/.ssh

# Kill any old relay
[ -f ~/.ssh/agent-relay.pid ] && kill "$(cat ~/.ssh/agent-relay.pid)" 2>/dev/null || true
rm -f ~/.ssh/agent.sock

# Start the relay (background)
nohup socat UNIX-LISTEN:$HOME/.ssh/agent.sock,fork,mode=600 \
             UNIX-CONNECT:"$SSH_AUTH_SOCK" \
      >/tmp/ssh-agent-relay.log 2>&1 &
echo $! > ~/.ssh/agent-relay.pid

docker-compose -f docker-compose.dev.yaml up -d --build

Claude Code configuration:

claude mcp add mcp-docker-server-dev -- docker exec -i mcp-docker-server-dev mcp-run

Codex configuration:

cat ~/.codex/config.toml

[mcp_servers.mcp-docker-server-dev]
command = "docker"
args = ["exec", "-i", "mcp-docker-server-dev", "mcp-run"]

Testing

Run tests with:

pytest
# or
pytest -v

For coverage report:

pytest --cov=src/mcp_docker_server --cov-report=term-missing

Run tests by category:

pytest -m unit
# or
pytest -m unit --tb=short

pytest -m integration

Run specific test file:

pytest tests/test_handlers.py

pytest src/tests/test_handlers.py::TestContainerHandlers

pytest src/tests/test_handlers.py::TestContainerHandlers::test_list_containers

Testing with Coverage:

Run with coverage and generate HTML report

pytest --cov=src/mcp_docker_server --cov-report=html

Fail if coverage is below 85%

pytest --cov=src/mcp_docker_server --cov-fail-under=85

🌟 Contributing to the Future of DevOps

mcp-docker-server is more than a tool—it's a step toward democratizing infrastructure automation. Built with the belief that powerful DevOps capabilities should be accessible, intuitive, and secure for all practitioners.

🤝 Get Involved

• Report Issues: Share your real-world use cases and pain points
• Contribute Code: Help build features that matter to the DevOps community
• Share Knowledge: Write about your experiences and help others learn
• Spread the Word: Help fellow DevOps engineers discover better ways to work

💡 Vision

We're building toward a future where automation, AI, and resilient infrastructure empower every technologist to build, experiment, and scale with confidence—breaking down barriers between creativity and operational excellence.

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Built with ❤️ for the DevOps community