Dockerised Python execution environment for AI agents
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# python-docker-mcp Dockerized Python execution environment for AI agents. ## Overview This MCP server provides a safe, sandboxed Python execution environment for LLM-powered agents. It allows agents to: - Execute Python code in isolated Docker containers - Choose between transient or persistent execution environments - Install packages as needed for specific tasks - Maintain state between execution steps ## Installation ### Requirements - Docker must be installed and running on the host system - Python 3.11 or later - `uv` for package management (recommended) ### Install from PyPI ```bash # Using uv (recommended) uv pip install python-docker-mcp # Using pip pip install python-docker-mcp ``` ### Install from Source ```bash # Clone the repository git clone https://github.com/artivus/python-docker-mcp.git cd python-docker-mcp # Install with uv uv pip install -e . # Or with pip pip install -e . ``` ## Quick Start ### Running the Server The python-docker-mcp server can be started directly using the module: ```bash python -m python_docker_mcp ``` This will start the MCP server and listen for JSONRPC requests on stdin/stdout. ## Components ### Docker Execution Environment The server implements two types of execution environments: 1. **Transient Environment** - Each execution is isolated in a fresh container - State must be explicitly passed and returned between calls - Safer for one-off code execution 2. **Persistent Environment** - Maintains state between executions - Variables defined in one execution are available in subsequent executions - Suitable for interactive, stateful REPL-like sessions ### Preinstalled Packages The Docker containers come with several common scientific and data analysis libraries preinstalled: - **NumPy**: For numerical computing - **Pandas**: For data manipulation and analysis - **SciPy**: For scientific computing and technical computing - **Matplotlib**: For creating visualizations - **scikit-learn…
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