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.

• 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

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

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

# 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

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

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 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 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})

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 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

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.

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.

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

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

// ~/.config/Claude/claude_desktop_config.json
{
    "mcpServers": {
        "code-sandbox-mcp": {
            "command": "/path/to/code-sandbox-mcp",
            "args": [],
            "env": {}
        }
    }
}

// ~/Library/Application Support/Claude/claude_desktop_config.json
{
    "mcpServers": {
        "code-sandbox-mcp": {
            "command": "/path/to/code-sandbox-mcp",
            "args": [],
            "env": {}
        }
    }
}

// %APPDATA%\Claude\claude_desktop_config.json
{
    "mcpServers": {
        "code-sandbox-mcp": {
            "command": "C:\\path\\to\\code-sandbox-mcp.exe",
            "args": [],
            "env": {}
        }
    }
}

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

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

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