Sandbox MCP Server

An MCP server that provides isolated Docker environments for code execution. This server allows you to:

• Create containers with any Docker image
• Write and execute code in multiple programming languages
• Install packages and set up development environments
• Run commands in isolated containers

Integration with Claude Desktop

1. Open Claude Desktop's configuration file:

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

2. Add the sandbox server configuration:

{
    "mcpServers": {
        "sandbox": {
            "command": "uv",
            "args": [
                "--directory",
                "/absolute/path/to/sandbox_server",
                "run",
                "sandbox_server.py"
            ],
            "env": {
                "PYTHONPATH": "/absolute/path/to/sandbox_server"
            }
        }
    }
}

Replace /absolute/path/to/sandbox_server with the actual path to your project directory.

3. Restart Claude Desktop

Security Notes

• All code executes in isolated Docker containers
• Containers are automatically removed after use
• File systems are isolated between containers
• Host system access is restricted

Project Structure

sandbox_server/
├── sandbox_server.py     # Main server implementation
├── pyproject.toml        # Project configuration
└── README.md            # This file

Available Tools

The server provides three main tools:

1. create_container_environment: Creates a new Docker container with specified image
2. create_file_in_container: Creates a file in a container
3. execute_command_in_container: Runs commands in a container
4. save_container_state: Saves the container state to a persistent container
5. export_dockerfile: exports a docker file to create a persistant environment
6. exit_container: closes a container to cleanup environment when finished

Prerequisites

• Python 3.9 or higher
• Docker installed and running
• uv package manager (recommended)
• Docker MCP server (recommended)

Installation

1. Clone this repository:

git clone <your-repo-url>
cd sandbox_server

2. Create and activate a virtual environment with uv:

uv venv
source .venv/bin/activate  # On Unix/MacOS
# Or on Windows:
# .venv\Scripts\activate

3. Install dependencies:

uv pip install .

Usage Examples

Basic Usage

Once connected to Claude Desktop, you can:

1. Create a Python container:

Could you create a Python container and write a simple hello world program?

2. Run code in different languages:

Could you create a C program that calculates the fibonacci sequence and run it?

3. Install packages and use them:

Could you create a Python script that uses numpy to generate and plot some random data?

Saving and Reproducing Environments

The server provides several ways to save and reproduce your development environments:

Creating Persistent Containers

When creating a container, you can make it persistent:

Could you create a persistent Python container with numpy and pandas installed?

This will create a container that:

• Stays running after Claude Desktop closes
• Can be accessed directly through Docker
• Preserves all installed packages and files

The server will provide instructions for:

• Accessing the container directly (docker exec)
• Stopping and starting the container
• Removing it when no longer needed

Saving Container State

After setting up your environment, you can save it as a Docker image:

Could you save the current container state as an image named 'my-ds-env:v1'?

This will:

1. Create a new Docker image with all your:
   • Installed packages
   • Created files
   • Configuration changes
2. Provide instructions for reusing the environment

You can then share this image or use it as a starting point for new containers:

Could you create a new container using the my-ds-env:v1 image?

Generating Dockerfiles

To make your environment fully reproducible, you can generate a Dockerfile:

Could you export a Dockerfile that recreates this environment?

The generated Dockerfile will include:

• Base image specification
• Created files
• Template for additional setup steps

You can use this Dockerfile to:

1. Share your environment setup with others
2. Version control your development environment
3. Modify and customize the build process
4. Deploy to different systems

Recommended Workflow

For reproducible development environments:

1. Create a persistent container:

Create a persistent Python container for data science work

2. Install needed packages and set up the environment:

Install numpy, pandas, and scikit-learn in the container

3. Test your setup:

Create and run a test script to verify the environment

4. Save the state:

Save this container as 'ds-workspace:v1'

5. Export a Dockerfile:

Generate a Dockerfile for this environment

This gives you multiple options for recreating your environment:

• Use the saved Docker image directly
• Build from the Dockerfile with modifications
• Access the original container if needed