developer-tools

Docker

quantgeekdev

by quantgeekdev

Manage containers with Docker and Docker Compose using natural language. Simplify your stacks with easy Docker Compose i

Manage containers and compose stacks through natural language.

github stars

455

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Natural language Docker commandsFull compose stack deployment

best for

  • / Developers managing containerized applications
  • / DevOps engineers deploying multi-service stacks
  • / Anyone wanting natural language Docker control

capabilities

  • / Create standalone Docker containers
  • / Deploy Docker Compose stacks
  • / Retrieve container logs
  • / List all containers with status

what it does

Control Docker containers and compose stacks through natural language commands. Manage your Docker environment by creating containers, deploying stacks, and monitoring logs.

about

Docker is a community-built MCP server published by quantgeekdev that provides AI assistants with tools and capabilities via the Model Context Protocol. Manage containers with Docker and Docker Compose using natural language. Simplify your stacks with easy Docker Compose i It is categorized under developer tools. This server exposes 4 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install Docker in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Docker is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

🐳 docker-mcp

Python 3.12 License: MIT Code style: black smithery badge

A powerful Model Context Protocol (MCP) server for Docker operations, enabling seamless container and compose stack management through Claude AI.

✨ Features

  • 🚀 Container creation and instantiation
  • 📦 Docker Compose stack deployment
  • 🔍 Container logs retrieval
  • 📊 Container listing and status monitoring

🎬 Demos

Deploying a Docker Compose Stack

https://github.com/user-attachments/assets/b5f6e40a-542b-4a39-ba12-7fdf803ee278

Analyzing Container Logs

https://github.com/user-attachments/assets/da386eea-2fab-4835-82ae-896de955d934

🚀 Quickstart

To try this in Claude Desktop app, add this to your claude config files:

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

Installing via Smithery

To install Docker MCP for Claude Desktop automatically via Smithery:

npx @smithery/cli install docker-mcp --client claude

Prerequisites

  • UV (package manager)
  • Python 3.12+
  • Docker Desktop or Docker Engine
  • Claude Desktop

Installation

Claude Desktop Configuration

Add the server configuration to your Claude Desktop config file:

MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json

<details> <summary>💻 Development Configuration</summary>
{
  "mcpServers": {
    "docker-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "<path-to-docker-mcp>",
        "run",
        "docker-mcp"
      ]
    }
  }
}
</details> <details> <summary>🚀 Production Configuration</summary>
{
  "mcpServers": {
    "docker-mcp": {
      "command": "uvx",
      "args": [
        "docker-mcp"
      ]
    }
  }
}
</details>

🛠️ Development

Local Setup

  1. Clone the repository:
git clone https://github.com/QuantGeekDev/docker-mcp.git
cd docker-mcp
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies:
uv sync

🔍 Debugging

Launch the MCP Inspector for debugging:

npx @modelcontextprotocol/inspector uv --directory <path-to-docker-mcp> run docker-mcp

The Inspector will provide a URL to access the debugging interface.

📝 Available Tools

The server provides the following tools:

create-container

Creates a standalone Docker container

{
    "image": "image-name",
    "name": "container-name",
    "ports": {"80": "80"},
    "environment": {"ENV_VAR": "value"}
}

deploy-compose

Deploys a Docker Compose stack

{
    "project_name": "example-stack",
    "compose_yaml": "version: '3.8'
services:
  service1:
    image: image1:latest
    ports:
      - '8080:80'"
}

get-logs

Retrieves logs from a specific container

{
    "container_name": "my-container"
}

list-containers

Lists all Docker containers

{}

🚧 Current Limitations

  • No built-in environment variable support for containers
  • No volume management
  • No network management
  • No container health checks
  • No container restart policies
  • No container resource limits

🤝 Contributing

  1. Fork the repository from docker-mcp
  2. Create your feature branch
  3. Commit your changes
  4. Push to the branch
  5. Open a Pull Request

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

✨ Authors

  • Alex Andru - Initial work | Core contributor - @QuantGeekDev
  • Ali Sadykov - Initial work | Core contributor - @md-archive

Made with ❤️

FAQ

What is the Docker MCP server?
Docker is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Docker?
This profile displays 67 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.567 reviews
  • Layla Lopez· Dec 28, 2024

    Docker has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Ishan Desai· Dec 24, 2024

    According to our notes, Docker benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Lucas Iyer· Dec 20, 2024

    We wired Docker into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Fatima Garcia· Dec 20, 2024

    Docker reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Pratham Ware· Dec 12, 2024

    According to our notes, Docker benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Mateo Bhatia· Dec 12, 2024

    Docker has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Layla Flores· Nov 19, 2024

    Docker is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Ren Agarwal· Nov 15, 2024

    We wired Docker into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Evelyn Ramirez· Nov 11, 2024

    According to our notes, Docker benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Yusuf Martin· Nov 11, 2024

    Docker is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

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