Sandbox MCP Server

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

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
  1. Create and activate a virtual environment with uv:
uv venv source .venv/bin/activate # On Unix/MacOS # Or on Windows: # .venv\Scripts\activate
  1. Install dependencies:
uv pip install .

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
  1. 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.

  1. Restart Claude Desktop

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?
  1. Run code in different languages:
Could you create a C program that calculates the fibonacci sequence and run it?
  1. 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

For reproducible development environments:

  1. Create a persistent container:
Create a persistent Python container for data science work
  1. Install needed packages and set up the environment:
Install numpy, pandas, and scikit-learn in the container
  1. Test your setup:
Create and run a test script to verify the environment
  1. Save the state:
Save this container as 'ds-workspace:v1'
  1. 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

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
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security - not tested
F
license - not found
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quality - not tested

Provides isolated Docker environments for code execution, enabling users to create and manage containers, execute multi-language code, save and reproduce development environments, ensuring security and isolation.

  1. Prerequisites
    1. Installation
      1. Integration with Claude Desktop
        1. Usage Examples
          1. Basic Usage
            1. Saving and Reproducing Environments
              1. Creating Persistent Containers
                1. Saving Container State
                  1. Generating Dockerfiles
                    1. Recommended Workflow
                  2. Security Notes
                    1. Project Structure
                      1. Available Tools