What is Python Mcp

python_mcp is an MCP Server designed to run Python code locally, providing an interactive REPL (Read-Eval-Print Loop) environment for executing Python scripts and commands.

Use cases

Use cases for python_mcp include educational purposes for learning Python, testing and debugging Python code snippets, and providing a local environment for developers to experiment with Python code interactively.

How to use

To use python_mcp, install it according to the configuration instructions for your operating system. Once installed, you can run the server and access the REPL environment, where you can execute Python code by providing the required arguments like ‘code’ and ‘session_id’.

Key features

Key features of python_mcp include: access to REPL session history via a custom ‘repl://’ URI scheme, the ability to maintain separate states for each session, support for both expressions and statements, and capturing stdout/stderr output for each execution.

Where to use

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python_local MCP Server

An MCP Server that provides an interactive Python REPL (Read-Eval-Print Loop) environment.

Components

Resources

The server provides access to REPL session history:

• Custom repl:// URI scheme for accessing session history
• Each session’s history can be viewed as a text/plain resource
• History shows input code and corresponding output for each execution

Tools

The server implements one tool:

• python_repl: Executes Python code in a persistent session
  • Takes code (Python code to execute) and session_id as required arguments
  • Maintains separate state for each session
  • Supports both expressions and statements
  • Captures and returns stdout/stderr output

Configuration

Install

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json

Development

Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:

uv sync

2. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

3. Publish to PyPI:

uv publish

Note: You’ll need to set PyPI credentials via environment variables or command flags:

• Token: --token or UV_PUBLISH_TOKEN
• Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory /path/to/python_local run python-local

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.