Python MCP Sandbox

Python MCP Sandbox is an interactive Python code execution environment that allows users and llms to safely execute Python code and install packages in isolated Docker containers.

Features

• 🐳 Docker Isolation: Securely run Python code in isolated Docker containers
• 📦 Package Management: Easily install and manage Python packages
• 📊 File Generation: Support for generating files and accessing them via web links
• 🔄 Automatic Cleanup: Containers and generated files are automatically cleaned up after a period of inactivity

Installation

# Clone the repository
git clone https://github.com/JohanLi233/python-mcp-sandbox.git
cd python-mcp-sandbox

uv venv

# Start the server
uv run mcp_sandbox.py

The default SSE endpoint is http://localhost:8000/sse, and you can interact with it via the MCP Inspector through SSE or any other client that supports SSE connections.

Available Tools

1. Create Python Environment: Creates a new Docker container for Python execution and returns its ID
2. Execute Python Code: Executes Python code in a specified Docker container
3. Install Python Package: Installs Python packages in a specified Docker container

Project Structure

python-mcp-sandbox/
├── mcp_sandbox.py     # Main application file
├── Dockerfile         # Docker configuration for Python containers
├── results/           # Directory for generated files
└── README.md          # Project documentation

Example Prompt

I've configured a Python code execution environment for you. You can run Python code using the following steps:

1. First, use the "Create Python virtual environment" tool to create a virtual environment
   - This will return an environment ID which you'll need for subsequent operations

2. If you need to install packages, use the "Install Python package" tool
   - Parameters: env_id (environment ID) and package_name (e.g., numpy, pandas)
   - Example: Install numpy and matplotlib

3. Use the "Execute Python code" tool to run your code
   - Parameters: env_id (environment ID) and code (Python code)
   - You can write any Python code including data processing, visualization, file operations, etc.

Example workflow:
- Create environment → Get environment ID
- Install necessary packages (like pandas, matplotlib)
- Execute code (such as data analysis, chart generation)
- View execution results and generated file links

Code execution happens in a secure sandbox environment. Generated files (images, CSVs, etc.) will be automatically provided with download links.

Remeber not to show the image directly, do not use plt.plot() etc.