Python Exec Sandbox

Name: Python Exec Sandbox
Author: lu-zhengda

by lu-zhengdaversion 0.1.5i★ 1iRuns on your computer

Registry nameio.github.lu-zhengda/mcp-python-exec-sandbox

Install

Overview

Description from the maker (original English)

Sandboxed Python execution for AI agents. PEP 723 inline deps, multi-version Python, zero pollution.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 2 passedChecked from public information; code not scanned yet
UsageiVery new↓ 33/wk · ★ 1

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 2 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

claude mcp add python-exec-sandbox -- uvx mcp-python-exec-sandbox

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "python-exec-sandbox": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "mcp-python-exec-sandbox"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

Which apps can use it

Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.

Run on your computer in

Claude Goose LM Studio Msty Studio Cherry Studio Cursor VS Code + GitHub Copilot Visual Studio Windsurf (Devin Desktop)Zed Kiro Cline Continue JetBrains AI Assistant Claude Code Codex CLI Gemini CLI OpenCode nodecode Warp LibreChat Raycast

All apps, with pros and cons →

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 10, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account lu-zhengda, which owns the repo
! Caution
Still maintained
Code last changed 9 months ago, more than 6 months ago
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

More stars than 65% of the other 5729 servers in this category
Published to the registry more recently than 3% of the other 5729 servers in this category
Basic checks equal to or better than 13% of the other 5729 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Python Exec Sandbox safe?

It passed 1 of 2 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed 9 months ago, and the latest version (0.1.5) reached the registry 9 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: REA, Netdata, Front-End Checklist

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

REA

Reverse engineer anything from your terminal or agent with one CLI and MCP server.

by morlutoWidely used↓ 388.7K/wk★ 448.2KUpdated 16 days ago

Runs on your computer

Install

Netdata

Real-time infrastructure monitoring with metrics, logs, alerts, and ML-based anomaly detection.

by netdataWidely used★ 81.3KUpdated 6 days ago

Runs in the cloud

Install

Front-End Checklist

Review frontend code and live pages against 386 quality-gated web development rules.

by thedaviddiasWidely used★ 74.7KUpdated 37 days ago

Runs in the cloud

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

mcp-python-exec-sandbox

Sandboxed Python execution for AI agents. Scripts run in ephemeral, isolated environments with inline dependencies (PEP 723) -- zero host pollution, zero leftover venvs, zero package conflicts.

Why?

Every coding agent can already run Python on your host. The problem is what happens next: packages accumulate, venvs sprawl, and a rogue pip install breaks your system. mcp-python-exec-sandbox eliminates this:

Scripts execute in a sandbox (bubblewrap on Linux, Docker on macOS/other platforms)
Dependencies are declared inline and resolved ephemerally via uv
Nothing touches your host's Python, site-packages, or virtualenvs
Each execution is isolated and disposable

Features

Sandboxed execution -- platform-specific isolation prevents host filesystem access
PEP 723 inline metadata -- declare dependencies directly in scripts with # /// script blocks
Multi-version Python -- run scripts on Python 3.13, 3.14, or 3.15 (uv downloads the right version automatically)
Ephemeral environments -- dependencies are resolved per-execution, never persisted
Package caching -- uv's global cache makes repeat installs near-instant
Timeout enforcement -- configurable per-execution timeouts
Output truncation -- prevents runaway output from overwhelming the agent

Prerequisites

All setups require:

Python 3.13+ -- to run the MCP server process
[uv](https://docs.astral.sh/uv/getting-started/installation/) -- manages script execution, dependency resolution, and Python version downloads. Also provides uvx for running the server without installing it globally.

Additional requirements depend on your chosen sandbox backend:

Setup	Additional requirements	Install
Native sandbox (Linux)	bubblewrap	`sudo apt install bubblewrap`
Docker sandbox (macOS, any)	Docker Engine	See Docker docs
No sandbox	None	--

Host Python vs. execution Python: These are independent. Python 3.13+ is needed to run the server process itself. The --python-version flag controls which Python version your *scripts* execute on -- uv downloads the target version automatically. You do not need to install Python 3.14 or 3.15 on your host to run scripts on those versions.

Quick start

Claude Code (Linux -- native sandbox)

claude mcp add python-sandbox -- uvx mcp-python-exec-sandbox

Claude Code (macOS -- Docker sandbox, recommended)

claude mcp add python-sandbox -- uvx mcp-python-exec-sandbox

The Docker sandbox image is pulled automatically from GHCR on first use. No manual build required.

Claude Code (no sandbox)

claude mcp add python-sandbox -- uvx mcp-python-exec-sandbox --sandbox-backend none

Cursor

Add to .cursor/mcp.json (project-level) or ~/.cursor/mcp.json (global):

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

OpenAI Codex CLI

codex mcp add python-sandbox -- uvx mcp-python-exec-sandbox

Or add to .codex/config.toml:

[mcp_servers.python-sandbox]

command = "uvx" args = ["mcp-python-exec-sandbox"]

Other MCP clients

Any client that supports the MCP stdio transport can use this server:

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

Multi-version Python

Use --python-version to target a specific Python version. uv downloads it automatically -- no manual install needed.

# Python 3.13 (default)

uvx mcp-python-exec-sandbox --python-version 3.13

Python 3.14

uvx mcp-python-exec-sandbox --python-version 3.14

Python 3.15

uvx mcp-python-exec-sandbox --python-version 3.15

This works across all sandbox backends. The Docker sandbox uses uv inside the container to manage Python versions, so the same --python-version flag applies.

Tools

`execute_python`

Execute a Python script with automatic dependency management.

Parameter	Type	Default	Description
`script`	str	required	Python source code, may include PEP 723 inline metadata
`dependencies`	list[str]	`[]`	Extra PEP 508 dependency specifiers to merge
`timeout_seconds`	int	30	Maximum execution time (1--300)

# Simple script

execute_python(script="print('hello world')")

Script with dependencies

execute_python( script="import requests; print(requests.get('https://httpbin.org/get').status_code)", dependencies=["requests"] )

Script with inline PEP 723 metadata

execute_python(script="""

/// script

dependencies = ["pandas", "matplotlib"]

///

import pandas as pd print(pd.DataFrame({'a': [1,2,3]}).describe()) """)

`check_environment`

Returns information about the execution environment: Python version, uv version, platform, sandbox status, and configuration.

`validate_script`

Validates a script's PEP 723 metadata and dependencies without executing it.

Parameter	Type	Default	Description
`script`	str	required	Python source code to validate
`dependencies`	list[str]	`[]`	Extra dependency specifiers to validate

Sandbox backends

Backend	Platform	Tool	Notes
`native`	Linux	bubblewrap	Namespace isolation, network allowed
`docker`	Any	Docker	Container isolation, resource limits
`none`	Any	--	No sandboxing (not recommended)

The default backend is native (bubblewrap) on Linux and docker on macOS/other platforms. Specifying --sandbox-backend native on macOS automatically redirects to Docker. If the sandbox tool is unavailable, the server falls back to none with a warning.

Docker sandbox setup

The Docker sandbox image is published to GHCR and pulled automatically when the server starts. No manual setup is needed.

To build locally for development:

docker build -t ghcr.io/lu-zhengda/mcp-python-exec-sandbox profiles/

CLI options

mcp-python-exec-sandbox [OPTIONS]

Options: --python-version TEXT Python version for execution (default: 3.13) --sandbox-backend TEXT native | docker | none (default: native on Linux, docker on macOS) --max-timeout INT Maximum allowed timeout in seconds (default: 300) --default-timeout INT Default timeout in seconds (default: 30) --max-output-bytes INT Maximum output size in bytes (default: 102400) --no-warm-cache Skip cache warming on startup --uv-path TEXT Path to uv binary (default: uv)

Development

Setup

git clone https://github.com/lu-zhengda/mcp-python-exec-sandbox.git

cd mcp-python-exec-sandbox uv sync --dev

Project structure

src/mcp_python_exec_sandbox/   # Package source

server.py # FastMCP server + tool definitions executor.py # uv subprocess orchestration script.py # PEP 723 metadata parsing/merging sandbox.py # Sandbox ABC + factory sandbox_linux.py # bubblewrap sandbox (Linux) sandbox_docker.py # Docker sandbox (macOS/any) config.py, cache.py, output.py, errors.py tests/ # Unit + integration tests (mocked or local uv) e2e_tests/ # End-to-end tests (require uv + network) profiles/ # Dockerfile, warmup packages .devcontainer/ # Devcontainer for Linux sandbox testing from macOS

Running tests

Unit and integration tests -- fast, run everywhere:

uv run pytest tests/ -v

E2E tests -- require uv and network access. These exercise real script execution, package installation, MCP protocol flow, and sandbox enforcement:

uv run pytest e2e_tests/ -v

Docker sandbox tests

The Docker E2E tests (e2e_tests/test_docker_sandbox.py) verify execution, dependency installation, read-only filesystem enforcement, host isolation, and timeout handling through the Docker backend.

Prerequisites:

Docker must be installed and running
Build the sandbox image:

docker build -t ghcr.io/lu-zhengda/mcp-python-exec-sandbox profiles/

Then run:

uv run pytest e2e_tests/test_docker_sandbox.py -v

These tests are automatically skipped if Docker is unavailable or the image hasn't been built.

Linux sandbox tests (devcontainer)

The Linux sandbox tests (e2e_tests/test_sandbox_enforcement.py::test_linux_sandbox_blocks_etc_shadow) use bubblewrap (bwrap) for namespace isolation. They are skipped on macOS because bwrap is Linux-only.

To run them from macOS, use the included devcontainer which provides Ubuntu 24.04 with bwrap pre-installed:

VS Code:

Install the Dev Containers extension
Open the project and select Reopen in Container
In the integrated terminal:

uv run pytest e2e_tests/test_sandbox_enforcement.py -v

CLI:

# Install the devcontainer CLI (once)

npm install -g @devcontainers/cli

Build and start the container

devcontainer up --workspace-folder .

Run the Linux sandbox tests inside the container

devcontainer exec --workspace-folder . uv run pytest e2e_tests/test_sandbox_enforcement.py -v

Test matrix

Test suite	Command	Requirements
Unit tests	`uv run pytest tests/ -v`	`uv`
Integration tests	`uv run pytest tests/test_integration.py -v`	`uv`
E2E (general)	`uv run pytest e2e_tests/ -v`	`uv`, network
E2E (Docker sandbox)	`uv run pytest e2e_tests/test_docker_sandbox.py -v`	`uv`, Docker, sandbox image
E2E (Linux/bwrap sandbox)	`uv run pytest e2e_tests/test_sandbox_enforcement.py -v`	`uv`, Linux with `bwrap` (or devcontainer)

Contributing

One logical change per commit. Descriptive commit message (imperative mood).
Run uv run pytest tests/ -v before committing -- all tests must pass.
Add tests for new functionality: unit tests in tests/, E2E in e2e_tests/ if it needs real execution.
Keep dependencies minimal. Do not add runtime deps without strong justification.
Tool docstrings in server.py are user-facing MCP tool descriptions. Write them for an LLM audience.
Sandbox backends must degrade gracefully: if the required tool (bwrap, docker) is missing, fall back to NoopSandbox with a warning.

License

MIT

Python Exec Sandbox

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Which apps can use it

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Developer toolsi

Common questions

Alternatives

REA

Netdata

Front-End Checklist

README from the maker

mcp-python-exec-sandbox

Why?

Features

Prerequisites

Quick start

Claude Code (Linux -- native sandbox)

Claude Code (macOS -- Docker sandbox, recommended)

Claude Code (no sandbox)

Cursor

OpenAI Codex CLI

Other MCP clients

Multi-version Python

Python 3.14

Python 3.15

Tools

execute_python

Script with dependencies

Script with inline PEP 723 metadata

/// script

dependencies = ["pandas", "matplotlib"]

///

check_environment

validate_script

Sandbox backends

Docker sandbox setup

CLI options

Development

Setup

Project structure

Running tests

Docker sandbox tests

Linux sandbox tests (devcontainer)

Build and start the container

Run the Linux sandbox tests inside the container

Test matrix

Contributing

License

`execute_python`

`check_environment`

`validate_script`