"How to run a Python script in a sandboxed environment" matching MCP servers:

Enables execution of arbitrary CLI tools and shell scripts by defining them in YAML configuration files as MCP Tools. Supports custom shells (bash, Python, Node.js, Deno), input parameters passed as environment variables, and flexible timeout settings.

A server that provides a persistent Python REPL environment through the MCP protocol, allowing execution of Python code, variable management, and package installation.

Provides tools for executing shell commands both synchronously and asynchronously with real-time output streaming and process management capabilities. It enables users to start background tasks, monitor progress, and manage long-running processes via Stdio or HTTP transports.

A Python server that enables interaction with macOS native applications (Contacts, Notes, Mail, Messages, Reminders, Calendar, and Maps) through AppleScript, featuring asynchronous operations and type-safe interfaces.

Enterprise-grade MCP server integrating Claude AI capabilities, enabling AI-powered conversations, file operations, and system tools via the Model Context Protocol.

Enables AI agents to control and interact with a local Windows computer through authenticated MCP tools, including file operations, shell commands, process management, HTTP requests, and system/media inspection.

Provides secure filesystem access for AI models through the Model Context Protocol with strict path validation, file operations, directory management, and system command execution within predefined directories.

Enables AI agents to securely connect to and manage remote servers via SSH, with tools for running commands, file operations, log inspection, service management, and system monitoring under configurable access controls and audit logging.

Enables interaction with Google Cloud Platform services through gcloud CLI commands via a Cloud Run deployed MCP server. Supports executing gcloud commands and managing GCP resources through natural language.

A local MCP runtime for macOS and Windows that enables ChatGPT to read, edit, search, run tests, and capture screenshots in your local project folder while keeping your source code on your machine.

A self-hosted MCP server that enables AI coding agents to read, edit, search, and run code in local projects with human review loops and policy controls.

A Python implementation of the Model Context Protocol that allows applications to provide standardized context for LLMs, enabling creation of servers that expose data and functionality to LLM applications through resources, tools, and prompts.

A testing environment for MCP server setup and client-server interactions, allowing users to verify basic MCP server functionality and test client-server communications including shell command execution.

A simple MCP server that exposes a terminal tool, allowing AI agents to execute shell commands.

Enables AI assistants to execute Python functions and system commands via Streamable HTTP, including bash, Python code execution, file operations, and text searching.

PRIMS is a lightweight, open-source Model Context Protocol (MCP) server that lets LLM agents safely execute arbitrary Python code in a secure, throw-away sandbox.

Exposes the agent's built-in tools, such as file operations, shell commands, web search, memory, skills, and code search, as MCP tools over stdio or HTTP, allowing external MCP clients to invoke them directly.

A demo MCP server that enables users to interact with various tools through natural language, including note management, weather queries, calculations, file operations, and shell commands. Uses Ollama for intent detection and structured command parsing from free text input.

Extends Claude Desktop with custom tools for file management, system operations, and command execution. Supports creating/reading/deleting files, directory listing, system information retrieval, and file searching capabilities.

A FastMCP server that enables AI agents to safely execute shell commands, run code in Python/Node.js/Go, and install packages, all packaged in Docker for secure deployment.