"A sandbox for executing Python code" matching MCP servers:

Creates short-lived, offline JavaScript sandbox containers for safe code execution via MCP, with no network access and strict resource limits.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

This server gives AI clients a persistent, isolated Linux container in which the model can run any bash command as root with internet access, install packages and start long-running processes without command filtering. Files produced inside it can be handed back to the user as signed, expiring public download links suitable for PDFs, images, audio and video.

Provides a local, isolated Linux VM sandbox for AI agents using Apple's Virtualization.framework, enabling fast command execution (~60ms) and package management without cloud costs.

Execute scientific scripts (R, Python, GMT, LaTeX, Octave, Julia, etc.) in isolated, disposable containers with no host filesystem access.

A sandbox MCP server for testing and debugging MCP tools and interactions via the MCP Inspector.

Enables code execution in isolated Docker containers with persistent IPython, Node.js, or R kernels, supporting file import/export and cross-session transfers via MCP tools.

Execute Python code via subprocess with timeout and error handling, returning stdout, stderr, and exit code.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

The Problem Modern distributed systems and AI agents require robust operational scaffolding. Simple CRUD apps or mock loops fail when subjected to real-world edge cases, asynchronous boundaries, and security constraints. The Solution An isolated execution platform for AI agents. An agent is given a broken service and a bounded toolset inside a disposable sandbox. Every command passes throug

Enables MCP-compatible AI clients to run curated Kali Linux security tools such as nmap, gobuster, nikto, sqlmap and netcat inside an isolated microVM sandbox. Commands execute as asynchronous jobs with readable output, cancellation and persistent per-workspace storage, with configurable network, CPU and memory limits.

Enables AI clients to manage and execute development commands within isolated Railway sandboxes, providing a remote shell, file read/write, and sandbox lifecycle management for running tests, builds, and repository operations.

This is a secure Python code execution server that enables LLMs to run Python code safely in isolated Docker containers, supporting stdout capture and Matplotlib PNG generation.

A production-grade MCP server providing a persistent Python REPL with multi-session support, sandboxing, and timeout protection, enabling LLM agents to execute Python code across multiple turns with variables that persist between calls.

Provides a sandboxed Docker environment for executing Python code against API endpoints, exposing tools for discovery, inspection, execution, and reuse.

A comprehensive MCP implementation designed for fullstack development that enables code manipulation, terminal command execution, and system notifications. It provides an intelligent agent interface powered by Gemini to automate Python and Next.js development workflows.

Enables AI coding agents to work inside a disposable Windows guest desktop, where they can run shell commands, launch applications, inspect and operate native GUI controls, perform browser DOM actions, and transfer files. Fresh observations, PNG screenshots, and hashed artifacts let agents verify results without touching the host desktop.

Enables chat clients to execute real shell commands (apt install, curl, ffmpeg, Python) inside a disposable, per-conversation Debian container and hand resulting files back to the user via signed download links. Installed packages and processes are discarded when the container recycles, while files under /workspace persist.

Exposes tools over stdio that let an AI agent create disposable Docker sandboxes to run commands and stream output, read and write files, and take snapshots, roll them back, fork them, list, and destroy them. It tells the model to snapshot before risky changes so failed attempts can be undone or explored in parallel on self-hosted infrastructure.

Executes Python code with safety constraints and manages Python packages through the Model Context Protocol.