docker-mcp MCP Server: Seamless Container and Docker Compose Stack Management

Overview: What is docker-mcp MCP Server?

docker-mcp is a powerful Model Context Protocol (MCP) server designed to provide seamless container management capabilities, including creation and instantiation of standalone containers, deployment of Docker Compose stacks, retrieval of container logs, and monitoring of container status. By leveraging the power of MCP protocol, it integrates directly with AI applications such as Claude Desktop, Continue, Cursor, and more, ensuring a standardized approach for managing various data sources and tools within these applications.

🔧 Core Features & MCP Capabilities

The docker-mcp server offers several key features that enhance AI application development and deployment:

1. Container Creation and Instantiation: Users can easily create standalone Docker containers with predefined configurations, including images, names, ports, environments, and more.
2. Docker Compose Stack Deployment: The ability to deploy entire Docker Compose stacks through a single command, simplifying the setup process for complex multi-container applications.
3. Logs Retrieval: Efficiently access logs from specific containers without needing to manually interact with each container individually.
4. Container Listing and Status Monitoring: Real-time monitoring of all running containers and their current status.

These features are crucial for AI developers who need a robust, standardized method to manage Docker resources within their AI applications through the MCP protocol.

⚙️ MCP Architecture & Protocol Implementation

The docker-mcp server operates on a client-server model, where it receives commands from MCP clients (such as Claude Desktop) and processes them according to the MCP protocol. The flow of interaction can be visualized with the following Mermaid diagram:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This architecture ensures that all interactions are standardized, making it easier to integrate different AI tools and resources.

🚀 Getting Started with Installation

To get started using the docker-mcp server, you need to follow several steps:

1. Install Prerequisites: Ensure Python 3.12+ is installed, along with Docker Desktop or Docker Engine.
2. Set Up Your Development Environment:
   • Clone the repository: git clone https://github.com/QuantGeekDev/docker-mcp.git
   • Set up a virtual environment and install dependencies using UV.

git clone https://github.com/QuantGeekDev/docker-mcp.git
cd docker-mcp
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
uv sync

3. Configure Claude Desktop: Add the following configuration to your claude_desktop_config.json file.

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

💡 Key Use Cases in AI Workflows

Real-Time Container Management

Consider an AI developer working on a project with multiple microservices deployed using Docker Compose. By integrating docker-mcp, the developer can easily manage and monitor these services through MCP protocol.

Automated CI/CD Pipelines

In complex CI/CD pipelines, developers often need to run various tests in custom environments managed by Docker containers. docker-mcp simplifies this process by allowing seamless container creation and deployment within such workflows.

🔌 Integration with MCP Clients

The docker-mcp server is designed to be fully compatible with various MCP clients. As of now, it supports full integration with Claude Desktop, Continue, and Cursor but does not yet support other clients like GPT-4. The compatibility matrix provides details on current support:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

📊 Performance & Compatibility Matrix

The performance of the docker-mcp server has been tested under various conditions, ensuring that it can handle both simple and complex workflows. The performance matrix provides key metrics on CPU usage, memory consumption, and response times.

🛠️ Advanced Configuration & Security

Customizing the Configurations

While the default configurations work well for most use cases, advanced users may need to customize their setup further. Docker-mcp supports environment variables and additional command-line options, allowing for fine-tuning of server behavior.

Example Configuration:

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

Security Considerations

To ensure the security of data and operations, users should follow best practices such as:

• Using strong encryption for communications.
• Regularly updating dependencies to patch vulnerabilities.

❓ Frequently Asked Questions (FAQ)

1. Does docker-mcp support all MCP clients?

   • Currently, docker-mcp supports full integration with Claude Desktop, Continue, and Cursor. Support for other clients like GPT-4 is pending development.

2. Is there a performance overhead when using docker-mcp?

   • The server has been optimized to minimize performance overhead, but larger container deployments may experience increased CPU and memory usage.

3. How does docker-mcp handle environment variables in containers?

   • Environment variables can be specified directly within the configuration files for both create-container and deploy-compose.

4. Is there a fallback mechanism if something goes wrong during deployment?

   • Yes, docker-mcp provides comprehensive error handling and logging mechanisms to help diagnose issues when deployments fail.

5. Can I use docker-mcp with non-Docker environments?

   • Yes, the protocol is agnostic and can be adapted for other containerization platforms with minor modifications.

👨‍💻 Development & Contribution Guidelines

Forking and Pull Requests

1. Fork the Repository: Start by forking this repository on GitHub.
2. Create a Feature Branch: Develop your features in a new branch.
3. Commit and Push Changes: Commit your changes and push them back to your forked repository.
4. Open a Pull Request: Submit a pull request to get your contributions reviewed and merged.

🌐 MCP Ecosystem & Resources

For more information on the Model Context Protocol (MCP) and its application, visit:

• Model Context Protocol Documentation
• MCP GitHub Repository

The docker-mcp server is part of a broader ecosystem designed to enhance AI application development through standardized tool integration.

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This comprehensive documentation highlights the capabilities and usage of the docker-mcp server, focusing on its integration with various MCP clients and real-world use cases. By adhering to these guidelines, developers can effectively utilize this server in their AI workflows.