What are the runtime dependencies?
Python 3, Black, autopep8, Pylint, and a virtual environment. The package is installed in development mode with `.[dev]`.
MCP Python Toolbox
@gianlucamazza
Basic information
About MCP Python Toolbox
MCP server giving AI assistants a toolbox for Python development.
MCP Python Toolbox config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp_python_toolbox": {
"command": "python",
"args": [
"-m",
"venv",
".venv"
]
}
}
}MCP Python Toolbox tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is MCP Python Toolbox?
MCP Python Toolbox is a Model Context Protocol (MCP) server that provides a comprehensive set of tools for Python development. It enables AI assistants like Claude to read, write, analyze, format, lint, manage dependencies, and execute Python code within a workspace.
How to use MCP Python Toolbox?
Install by cloning the repository, creating a virtual environment, and running pip install -e ".[dev]". Start the server as a CLI tool with python -m mcp_python_toolbox --workspace /path/to/project. For Claude Desktop, add a JSON configuration entry specifying the Python binary and arguments. Programmatic usage is also supported via the PythonToolboxServer class.
Key features of MCP Python Toolbox
- Safe file operations with workspace path validation
- AST-based Python code analysis (imports, functions, classes)
- Code formatting via Black and PEP8 (autopep8)
- Code linting using Pylint with detailed reports
- Virtual environment creation and dependency management
- Sandboxed code execution using the project’s virtual environment
Use cases of MCP Python Toolbox
- An AI assistant reading and modifying Python project files.
- Analyzing code structure (functions, classes, dependencies) for refactoring.
- Automatically formatting and linting code before committing.
- Managing virtual environments and installing packages from requirements or pyproject.toml.
- Safely executing Python code within a project context.
Frequently asked questions
How does the server ensure safety for file operations?
File operations are restricted to the configured workspace directory. Path validation prevents access outside that directory. Code execution uses temporary files and captures stdout/stderr.
How do I configure MCP Python Toolbox with Claude Desktop?
Add a JSON entry under MCP tools specifying the command (Python binary), arguments (`-m mcp_python_toolbox --workspace /path`), and environment variables like `PYTHONPATH` and `VIRTUAL_ENV`.
Can it execute arbitrary Python code?
Yes. The `CodeExecutor` tool runs code in the project’s virtual environment, capturing output, errors, and exit code. It uses temporary files and supports custom working directories.
What license does MCP Python Toolbox use?
MIT License.
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