What is Code Sandbox

Code Sandbox MCP is a secure sandbox environment designed for executing code within Docker containers. It allows AI applications to run code in a safe and isolated manner, leveraging containerization for enhanced security.

Use cases

The Code Sandbox MCP can be used in various scenarios, including testing code snippets, running isolated environments for different programming languages, and executing scripts without affecting the host system. It’s particularly useful for AI applications requiring consistent and secure code execution environments.

How to use

Users can quickly install Code Sandbox MCP through provided scripts for different operating systems (Linux, macOS, Windows). After installation, users initialize a sandbox with a specific Docker image, copy files or projects, execute commands, and manage containers through a series of command-line tools.

Key features

Key features include flexible container management, support for custom environments, real-time logging, file operations for easy data transfer, automated updates, and compatibility across multiple platforms (Linux, macOS, Windows).

Where to use

Code Sandbox MCP is suitable for usage in AI development environments, educational settings for coding practice, development workflows requiring isolated execution, and automated testing setups that demand consistent and secure execution environments.

Code Sandbox MCP 🐳

A secure sandbox environment for executing code within Docker containers. This MCP server provides AI applications with a safe and isolated environment for running code while maintaining security through containerization.

🌟 Features

• Flexible Container Management: Create and manage isolated Docker containers for code execution
• Custom Environment Support: Use any Docker image as your execution environment
• File Operations: Easy file and directory transfer between host and containers
• Command Execution: Run any shell commands within the containerized environment
• Real-time Logging: Stream container logs and command output in real-time
• Auto-Updates: Built-in update checking and automatic binary updates
• Multi-Platform: Supports Linux, macOS, and Windows

🚀 Installation

Prerequisites

• Docker installed and running
  • Install Docker for Linux
  • Install Docker Desktop for macOS
  • Install Docker Desktop for Windows

Quick Install

Linux, MacOS

curl -fsSL https://raw.githubusercontent.com/Automata-Labs-team/code-sandbox-mcp/main/install.sh | bash

Windows

# Run in PowerShell
irm https://raw.githubusercontent.com/Automata-Labs-team/code-sandbox-mcp/main/install.ps1 | iex

The installer will:

1. Check for Docker installation
2. Download the appropriate binary for your system
3. Create necessary configuration files

Manual Installation

1. Download the latest release for your platform from the releases page
2. Place the binary in a directory in your PATH
3. Make it executable (Unix-like systems only):

   chmod +x code-sandbox-mcp
   

🛠️ Available Tools

sandbox_initialize

Initialize a new compute environment for code execution.
Creates a container based on the specified Docker image.

Parameters:

• image (string, optional): Docker image to use as the base environment
  • Default: ‘python:3.12-slim-bookworm’

Returns:

• container_id that can be used with other tools to interact with this environment

copy_project

Copy a directory to the sandboxed filesystem.

Parameters:

• container_id (string, required): ID of the container returned from the initialize call
• local_src_dir (string, required): Path to a directory in the local file system
• dest_dir (string, optional): Path to save the src directory in the sandbox environment

write_file

Write a file to the sandboxed filesystem.

Parameters:

• container_id (string, required): ID of the container returned from the initialize call
• file_name (string, required): Name of the file to create
• file_contents (string, required): Contents to write to the file
• dest_dir (string, optional): Directory to create the file in (Default: ${WORKDIR})

sandbox_exec

Execute commands in the sandboxed environment.

Parameters:

• container_id (string, required): ID of the container returned from the initialize call
• commands (array, required): List of command(s) to run in the sandboxed environment
  • Example: [“apt-get update”, “pip install numpy”, “python script.py”]

copy_file

Copy a single file to the sandboxed filesystem.

Parameters:

• container_id (string, required): ID of the container returned from the initialize call
• local_src_file (string, required): Path to a file in the local file system
• dest_path (string, optional): Path to save the file in the sandbox environment

sandbox_stop

Stop and remove a running container sandbox.

Parameters:

• container_id (string, required): ID of the container to stop and remove

Description:
Gracefully stops the specified container with a 10-second timeout and removes it along with its volumes.

Container Logs Resource

A dynamic resource that provides access to container logs.

Resource Path: containers://{id}/logs
MIME Type: text/plain
Description: Returns all container logs from the specified container as a single text resource.

🔐 Security Features

• Isolated execution environment using Docker containers
• Resource limitations through Docker container constraints
• Separate stdout and stderr streams

🔧 Configuration

Claude Desktop

The installer automatically creates the configuration file. If you need to manually configure it:

Linux

macOS

Windows

Other AI Applications

For other AI applications that support MCP servers, configure them to use the code-sandbox-mcp binary as their code execution backend.

🛠️ Development

If you want to build the project locally or contribute to its development, see DEVELOPMENT.md.

📝 License

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