Python Exec Sandbox

@lu-zhengda

About Python Exec Sandbox

Sandboxed Python execution for AI agents. Scripts run in ephemeral, isolated environments (bubblewrap on Linux, Docker on macOS) with PEP 723 inline dependencies — zero host pollution, zero leftover venvs, zero package conflicts. Supports multi-version Python (3.13–3.15), configu

#python #sandbox #execution #security #pep723 #docker #bubblewrap #uv

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "python-sandbox": {
      "command": "uvx",
      "args": [
        "mcp-python-exec-sandbox"
      ]
    }
  }
}

Tools

No tools detected

Fetch the live tool list by running this server in a temporary sandbox using the button above.

Overview

What is Python Exec Sandbox?

Python Exec Sandbox is an MCP server that provides sandboxed execution of Python scripts with automatic dependency management. It uses platform-specific isolation (bubblewrap on Linux, Docker on macOS) and the uv package manager to resolve PEP 723 inline metadata, making it ideal for safely running untrusted or third-party code in agent workflows.

How to use Python Exec Sandbox?

Install the server via uvx and add it to Claude: claude mcp add python-sandbox -- uvx mcp-python-exec-sandbox. Then use the three provided tools: execute_python to run a script, check_environment to inspect the runtime, and validate_script to verify dependencies without execution.

Key features of Python Exec Sandbox

Sandboxed execution with platform-specific isolation (bubblewrap, Docker)
PEP 723 inline metadata for declaring dependencies in scripts
Multi-version Python support (3.13, 3.14, 3.15) via automatic uv downloads
Ephemeral environments with per-execution dependency resolution
Configurable timeouts (1–300s) and output truncation

Use cases of Python Exec Sandbox

Running untrusted Python scripts safely in an AI agent pipeline
Testing scripts with complex dependencies without permanent installation
Executing code in CI/CD workflows with guaranteed isolation
Providing a sandboxed Python environment for educational or exploration tools

FAQ from Python Exec Sandbox

What sandbox isolation methods are used?

Bubblewrap is used on Linux and Docker on macOS to prevent host filesystem access.

Can I configure the execution timeout?

Yes, the server supports configurable per-execution timeouts ranging from 1 to 300 seconds.

How are dependencies managed?

Dependencies are declared inline using PEP 723 # /// script blocks and are automatically resolved by uv into ephemeral environments. Package caching via uv makes repeat installs near-instant.

What Python versions are supported?

Python Exec Sandbox supports scripts on Python 3.13, 3.14, and 3.15. The correct version is downloaded automatically by uv.

What tools does the server expose?

Three tools are available: execute_python (run a script with dependency management), check_environment (report runtime details), and validate_script (validate metadata without execution).

Frequently asked questions

What sandbox isolation methods are used?

Bubblewrap is used on Linux and Docker on macOS to prevent host filesystem access.

Can I configure the execution timeout?

Yes, the server supports configurable per-execution timeouts ranging from 1 to 300 seconds.

How are dependencies managed?

Dependencies are declared inline using PEP 723 `# /// script` blocks and are automatically resolved by uv into ephemeral environments. Package caching via uv makes repeat installs near-instant.

What Python versions are supported?

Python Exec Sandbox supports scripts on Python 3.13, 3.14, and 3.15. The correct version is downloaded automatically by uv.

What tools does the server expose?

Three tools are available: `execute_python` (run a script with dependency management), `check_environment` (report runtime details), and `validate_script` (validate metadata without execution).

Comments

Basic information

More Reasoning MCP servers

Agentic MCP Client

peakmojo

A standalone agent runner that executes tasks using MCP (Model Context Protocol) tools via Anthropic Claude, AWS BedRock and OpenAI APIs. It enables AI agents to run autonomously in cloud environments and interact with various systems securely.

End-to-End Agentic AI Automation Lab

MDalamin5

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.

mcp-agentic-rag

rukshannet

MCP Server for Agentic RAG applications

NeoCoder: Neo4j-Guided AI Coding Workflow

angrysky56

An MCP server allowing AI assistants to use a Neo4j knowledge graph as their primary, dynamic instruction manual and long term project memory with adaptive templating and autonomous tool development tools.

MCP Sandbox

JohanLi233

Python sandboxes for llms