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MCP Python Interpreter

MCP Python Interpreter

Local
github.com/yzflyDeveloper Tools104Updated 17w ago

About MCP Python Interpreter

MCP Python Interpreter is an MCP server that lets LLMs work with Python environments, read and write files, and execute Python code. It is aimed at developers who want an AI assistant to run code within a development workflow.

Key features

  1. 01Lists system and conda Python environments
  2. 02Runs Python code or script files
  3. 03Lists and installs Python packages
  4. 04Reads and writes text and binary files
  5. 05Provides prompt templates for common Python tasks

Use cases

  1. 01Running a Python snippet in a chosen conda environment
  2. 02Having an AI assistant create or modify Python files
  3. 03Checking and installing dependencies in an environment
  4. 04Using prompt templates to help write or debug functions

MCP support

The project is an MCP server that exposes tools, resources and prompts to MCP clients, such as run_python_code, read_file and the python://environments resource.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MseeP.ai Security Assessment Badge

MCP Python Interpreter

A Model Context Protocol (MCP) server that allows LLMs to interact with Python environments, read and write files, execute Python code, and manage development workflows.

Features

  • Environment Management: List and use different Python environments (system and conda)
  • Code Execution: Run Python code or scripts in any available environment
  • Package Management: List installed packages and install new ones
  • File Operations:
    • Read files of any type (text, source code, binary)
    • Write text and binary files
  • Python Prompts: Templates for common Python tasks like function creation and debugging

Installation

You can install the MCP Python Interpreter using pip:

pip install mcp-python-interpreter

Or with uv:

uv install mcp-python-interpreter

Usage with Claude Desktop

  1. Install Claude Desktop
  2. Open Claude Desktop, click on menu, then Settings
  3. Go to Developer tab and click "Edit Config"
  4. Add the following to your claude_desktop_config.json:
{
  "mcpServers": {
    "mcp-python-interpreter": {
        "command": "uvx",
        "args": [
            "mcp-python-interpreter",
            "--dir",
            "/path/to/your/work/dir",
            "--python-path",
            "/path/to/your/python"
        ],
        "env": {
            "MCP_ALLOW_SYSTEM_ACCESS": 0
        },
    }
  }
}

For Windows:

{
  "mcpServers": {
    "python-interpreter": {
      "command": "uvx",
      "args": [
        "mcp-python-interpreter",
        "--dir",
        "C:\\path\\to\\your\\working\\directory",
        "--python-path",
        "/path/to/your/python"
      ],
        "env": {
            "MCP_ALLOW_SYSTEM_ACCESS": "0"
        },
    }
  }
}
  1. Restart Claude Desktop
  2. You should now see the MCP tools icon in the chat interface

The --dir parameter is required and specifies where all files will be saved and executed. This helps maintain security by isolating the MCP server to a specific directory.

Prerequisites
  • Make sure you have uv installed. If not, install it using:
    curl -LsSf https://astral.sh/uv/install.sh | sh
    
  • For Windows:
    powershell -ExecutionPolicy Bypass -Command "iwr -useb https://astral.sh/uv/install.ps1 | iex"
    

Available Tools

The Python Interpreter provides the following tools:

Environment and Package Management
  • list_python_environments: List all available Python environments (system and conda)
  • list_installed_packages: List packages installed in a specific environment
  • install_package: Install a Python package in a specific environment
Code Execution
  • run_python_code: Execute Python code in a specific environment
  • run_python_file: Execute a Python file in a specific environment
File Operations
  • read_file: Read contents of any file type, with size and safety limits
    • Supports text files with syntax highlighting
    • Displays hex representation for binary files
  • write_file: Create or overwrite files with text or binary content
  • write_python_file: Create or overwrite a Python file specifically
  • list_directory: List Python files in a directory

Available Resources

  • python://environments: List all available Python environments
  • python://packages/{env_name}: List installed packages for a specific environment
  • python://file/{file_path}: Get the content of a Python file
  • python://directory/{directory_path}: List all Python files in a directory

Prompts

  • python_function_template: Generate a template for a Python function
  • refactor_python_code: Help refactor Python code
  • debug_python_error: Help debug a Python error

Example Usage

Here are some examples of what you can ask Claude to do with this MCP server:

  • "Show me all available Python environments on my system"
  • "Run this Python code in my conda-base environment: print('Hello, world!')"
  • "Create a new Python file called 'hello.py' with a function that says hello"
  • "Read the contents of my 'data.json' file"
  • "Write a new configuration file with these settings..."
  • "List all packages installed in my system Python environment"
  • "Install the requests package in my system Python environment"
  • "Run data_analysis.py with these arguments: --input=data.csv --output=results.csv"

File Handling Capabilities

The MCP Python Interpreter now supports comprehensive file operations:

  • Read text and binary files up to 1MB
  • Write text and binary files
  • Syntax highlighting for source code files
  • Hex representation for binary files
  • Strict file path security (only within the working directory)

Security Considerations

This MCP server has access to your Python environments and file system. Key security features include:

  • Isolated working directory
  • File size limits
  • Prevented writes outside the working directory
  • Explicit overwrite protection

Always be cautious about running code or file operations that you don't fully understand.

License

MIT

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.