Comprehensive MCP Python Toolbox enables AI assistants to manage, analyze, and execute Python code efficiently within projects
The MCP Python Toolbox serves as an advanced Model Context Protocol (MCP) server tailored for Python development, providing a robust set of tools and functionalities to enhance the capabilities of Artificial Intelligence (AI) applications like Claude Desktop. By leveraging the Model Context Protocol, this MCP server enables AI applications to interact seamlessly with complex coding tasks, project management, and file operations through a standardized interface.
requirements.txt
, pyproject.toml
, or specific version dependencies, ensuring a clean, consistent development environment.requirements.txt
based on the current project's setup.The Model Context Protocol Server is built to conform strictly to the protocol specifications defined by the MCP community. By adhering to these standards, this server ensures compatibility across various AI applications such as Claude Desktop and continues to evolve with advancements in communication protocols and interoperability needs.
MCP's architecture can be encapsulated using a Mermaid flowchart:
graph TD
A[AI Application] -->|MCP Client| B[MCP Protocol]
B --> C[MCP Server]
C --> D[Data Source/Tool]
style A fill:#e1f5fe
style C fill:#f3e5f5
style D fill:#e8f5e8
This figure illustrates the bidirectional flow of commands and responses between the AI application, MCP Client, Protocol layer, and ultimately, the Data Source (or tool being managed) via the server.
To set up the MCP Python Toolbox MCP Server for use in various settings, follow these steps:
Clone the Repository:
git clone https://github.com/gianlucamazza/mcp_python_toolbox.git
cd mcp_python_toolbox
Create and Activate a Virtual Environment:
python -m venv .venv
source .venv/bin/activate # Linux/Mac
.venv\Scripts\activate # Windows
Install in Development Mode:
pip install -e ".[dev]"
Imagine an AI-assisted development scenario where the MCP Python Toolbox processes large codebases to perform complex analyses, recommend optimizations or code improvements. The process could involve automatically reading and analyzing a Python project's architecture with detailed diagnostics provided by tools such as Pylint and Black.
In another context, an AI-driven project management system can dynamically manage virtual environments for each development task. By using the ProjectManager module, it can install dependencies based on .toml
or .txt
files to ensure that all necessary packages are available in a consistent and controlled manner.
The MCP Python Toolbox is designed to integrate seamlessly with various MCPC clients such as Claude Desktop. Compatibility details are outlined below:
MCP Client | Resources | Tools | Prompts |
---|---|---|---|
Claude Desktop | ✅ | ✅ | ✅ |
For a hands-on setup, follow these steps to configure the server with Claude Desktop:
{
"mcpServers": {
"[server-name]": {
"command": "/path/to/.venv/bin/python",
"args": ["-m", "mcp_python_toolbox", "--workspace", "/path/to/workspace"],
"env": {
"PYTHONPATH": "/path/to/mcp_python_toolbox/src",
"PYTHON_HOME": "",
"VIRTUAL_ENV": "/path/to/.venv",
"PATH": "/opt/homebrew/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin"
}
}
}
}
The MCP Python Toolbox is tailored to ensure compatibility and performance across various scenarios. Here’s a summary of its current status:
Feature | Status |
---|---|
File Operations | Fully Implemented |
Code Analysis | Enhanced with Pylint and Black |
Project Management | Extensive Support |
Code Execution | Controlled Environment |
This matrix underscores the thorough feature coverage and performance stability of the MCP Python Toolbox server.
Fine-tuning the mcp_python_toolbox
configuration is essential for specific AI use cases. Ensure that paths, environment variables, and command arguments align with your project’s needs to achieve optimal operation.
{
"command": "/path/to/.venv/bin/python",
"args": ["-m", "mcp_python_toolbox", "--workspace", "/path/to/workspace"],
"env": {
"PYTHONPATH": "/path/to/mcp_python_toolbox/src",
"VIRTUAL_ENV": "/path/to/.venv"
}
}
Follow these best practices to secure your MCP Python Toolbox installation:
A1: The MCP Python Toolbox is fully compatible with Claude Desktop. Ensure that you configure the server according to the provided instructions.
A2: The CodeExecutor ensures that all dependencies required by running scripts are available in an isolated virtual environment, providing a reliable and controlled execution context.
A3: Yes, while it is primarily compatible with Claude Desktop, the MCP Python Toolbox can be configured for integration with any MCPC client that supports Model Context Protocol.
A4: Store API keys as environment variables (e.g., API_KEY
) and exclude them from version control systems using .env
or similar mechanisms.
A5: Common challenges include ensuring secure path configurations to prevent data leakage. Follow strict security guidelines and regularly update dependencies to mitigate risks.
{
"mcpServers": {
"[server-name]": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-[name]"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
This documentation is crafted to ensure:
By emphasizing the capabilities and benefits of the MCP Python Toolbox for AI applications, this documentation aims to position it as a crucial tool in modern software development ecosystems.
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