Python MCP Sandbox

# Clone the repository git clone https://github.com/JohanLi233/python-mcp-sandbox.git cd python-mcp-sandbox uv venv uv sync # Start the server uv run main.py

python-mcp-sandbox/ ├── main.py # Application entry point ├── requirements.txt # Project dependencies ├── Dockerfile # Docker configuration for Python containers ├── results/ # Directory for generated files ├── mcp_sandbox/ # Main package directory │ ├── __init__.py │ ├── models.py # Pydantic models │ ├── api/ # API related components │ │ ├── __init__.py │ │ └── routes.py # API route definitions │ ├── core/ # Core functionality │ │ ├── __init__.py │ │ ├── docker_manager.py # Docker container management │ │ └── mcp_tools.py # MCP tools │ └── utils/ # Utilities │ ├── __init__.py │ ├── config.py # Configuration constants │ ├── file_manager.py # File management │ └── task_manager.py # Periodic task management └── README.md # Project documentation

I've configured a Python code execution sandbox for you. You can run Python code using the following steps: 1. First, use the "list_sandboxes" tool to view all existing sandboxes (Docker containers). - You can reuse an existing sandbox_id if a sandbox exists, do not create a new one. - If you need a new sandbox, use the "create_sandbox" tool. - Each sandbox is an isolated Python environment, and the sandbox_id is required for all subsequent operations. 2. If you need to install packages, use the "install_package_in_sandbox" tool - Parameters: sandbox_id and package_name (e.g., numpy, pandas) - This starts asynchronous installation and returns immediately with status 3. After installing packages, you can check their installation status using the "check_package_installation_status" tool - Parameters: sandbox_id and package_name (name of the package to check) - If the package is still installing, you need to check again using this tool 4. Use the "execute_python_code" tool to run your code - Parameters: sandbox_id and code (Python code) - Returns output, errors and links to any generated files - All generated files are stored inside the sandbox, and file_links are direct HTTP links for inline viewing Example workflow: - Use list_sandboxes to check for available sandboxes, if no available sandboxes, use create_sandbox to create a new one → Get sandbox_id - Use install_package_in_sandbox to install necessary packages (like pandas, matplotlib), with the sandbox_id parameter - Use check_package_installation_status to verify package installation, with the same sandbox_id parameter - Use execute_python_code to run your code, with the sandbox_id parameter Code execution happens in a secure sandbox. Generated files (images, CSVs, etc.) will be provided as direct HTTP links, which can viewed inline in the browser. Remember not to use plt.show() in your Python code. For visualizations: - Save figures to files using plt.savefig() instead of plt.show() - For data, use methods like df.to_csv() or df.to_excel() to save as files - All saved files will automatically appear as HTTP links in the results, which you can open or embed directly.

{ "mcpServers": { "mcpSandbox": { "command": "npx", "args": ["-y", "supergateway", "--sse", "http://localhost:8000/sse"] } } }

{ "mcpServers": { "mcpSandbox": { "command": "npx", "args": ["-y", "supergateway", "--sse", "http://115.190.87.78/sse?api_key=<API_KEY>"] } } }