MCP Python Toolbox

🚀 MCP Python Toolbox

A server implementing the Model Context Protocol (MCP), offering a comprehensive toolset to empower Python development. It enables AI assistants like Claude to efficiently handle Python code and projects through a standardized interface.

🚀 Quick Start

The MCP Python Toolbox implements the Model Context Protocol server, enabling Claude to perform Python development tasks. It allows Claude to:

• Securely read, write, and manage files within the workspace directory.
• Analyze, format, and check Python code using AST.
• Manage virtual environments and dependencies.
• Execute Python code in a controlled environment.

✨ Features

File Operations (FileOperations)

Handle all file-related tasks, including reading, writing, copying, moving, and deleting files or directories. Support simultaneous operations on multiple files and provide detailed operation feedback.

# Example usage:
files = FileOperations()
files.move("source.txt", "destination.txt")  # Move a file
files.copy("template.html", "new_page.html")  # Copy a file
print(files.read("data.csv"))                # Read file content

Code Analysis (CodeAnalysis)

Conduct structured analysis of Python code, including detecting syntax errors, identifying variable and function names, and generating an Abstract Syntax Tree (AST) of the code. Support multiple programming languages.

# Example usage:
analyzer = CodeAnalysis()
structure = analyzer.parse("app.py")
print(structure.errors)      # Display errors encountered during parsing
print(structure.variables)   # List all variable names

Project Management (ProjectManagement)

Assist in managing the development environment, including creating and deleting virtual environments, installing dependencies, and viewing information about installed packages and their versions in the current environment.

# Example usage:
pm = ProjectManagement()
pm.create_venv("my_project")       # Create a new virtual environment
pm.install(["numpy", "pandas"])    # Install multiple dependencies
print(pm.installed_packages)       # Display installed packages and their versions

Code Execution (CodeExecution)

Safely execute user-provided code in a controlled environment, support multiple programming languages, and capture output and errors. Configure to limit running time, memory usage, and file access permissions.

# Example usage:
executor = CodeExecution()
result = executor.run("print('Hello, World!')")
print(result.stdout)    # Output result
print(result.stderr)    # Display error information

📦 Installation

Install from Source

1. Clone the repository: git clone https://github.com/your-repository.git
2. Navigate to the directory: cd mcp-python-toolbox
3. Install dependencies: pip install -r requirements.txt
4. Install the toolbox: python setup.py install

Install from PyPI

pip install mcp-python-toolbox

💻 Usage Examples

Basic Usage

mcp [command] [options]

Available Commands

• init: Initialize the MCP Toolbox environment.
• run: Execute code snippets.
• analyze: Analyze code structure.
• manage: Manage the development environment.

📚 Documentation

Run Tests

pytest tests/

Type Checking

mypy src/mcp_python_toolbox/

Code Formatting

black src/mcp_python_toolbox/

🤝 Contribution Guidelines

1. Fork the repository.
2. Create a feature branch: git checkout -b feature/new-feature
3. Commit your changes: git commit -m 'Add new feature'
4. Push to the branch: git push origin feature/new-feature
5. Submit a Pull Request.

📄 License

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

🙏 Acknowledgments

• Implements the Model Context Protocol (MCP) specification.
• Built using modern Python development tools and best practices.
• Adopts industry-standard formatting (Black) and code inspection (Pylint) tools.