About Python Interpreter Mcp
Python Interpreter Mcp is an experimental MCP server for running Python scripts supplied by clients. It uses uv to execute each script in an isolated subprocess and returns its stdout.
Key features
- 01Run supplied Python code as a script
- 02Execute scripts with uv
- 03Capture and return stdout
Use cases
- 01Run a Python snippet during an LLM workflow
- 02Collect script output for a later step
MCP support
Exposes the `run_script` tool to MCP clients, accepting code and returning the script’s stdout. It can be launched over stdio.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEpython-interpreter-mcp: A MCP server to run scripts
Overview
A lightweight, experimental MCP server designed to execute arbitrary Python scripts in a structured and reproducible environment. It leverages uv to run isolated code snippets through subprocesses.
Tools
run_script- Runs the given script with
uv run script.py. - Input:
code(str): Script to be run
- Return: The stdout of the given script.
- Runs the given script with
Configuration
Usage with OpenAI Agents SDK
async with MCPServerStdio(
params={
"command": "uvx",
"args": ["python-interpreter-mcp"],
}
) as server
...
Usage with Claude Desktop
Add this to your claude_desktop_config.json:
"mcpServers": {
"interpreter": {
"command": "uvx",
"args": ["python-interpreter-mcp"]
}
}
How it works
- A script string is received by the MCP tool
run_script. - A hidden folder is created in cwd, and the script is saved as a
.pyfile inside it. - The script is then executed using
uv run, which ensures dependency isolation. - The stdout of the script is captured and returned as the response.
Usage Warnings
This project is in a very early stage of development.
⚠️ Important notes
- It executes arbitrary Python code, which means it can run anything — including malicious or destructive commands.
- Use only in trusted, sandboxed environments.
- You should always validate, guardrail, or restrict inputs when wiring this into an LLM.
Comments
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