"How to run a Windows command" matching MCP servers:

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

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.

Model Context Protocol server to run Python code in a sandbox.

Enables compatible MCP AI tools to read, write, design windows, and run programs in the E-Language IDE.

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 secure MCP server example that demonstrates how to deploy to Google Cloud Run with authentication and identity token protection. Serves as a tutorial template for building production-ready MCP servers in the cloud.

Ephemeral MicroVM-isolated code execution for AI agents. Run Python, Node, or bash — fresh hardware-isolated VM per call, hard-purged after. No state persists between calls.

MCP server that gives AI assistants a secure gVisor-isolated sandbox to run Python or JavaScript code, returning stdout, stderr, and exit codes without local access.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

Run DeepSeek as a real sub-agent inside Claude Code / Codex CLI — not just a single LLM call. DeepSeek gets its own 7-tool agent loop (Read/Write/Edit/Bash/Glob/Grep/NotebookEdit) inside a sandboxed workspace.

Enables agents to verify completion by cold-running an acceptance command and reporting whether it exits with code 0, returning a pass/fail receipt with output and refusal reasons.

Provides MCP tools to create, run commands in, checkpoint, and delete durable cloud environments, giving coding agents persistent state between calls.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

A server that allows LLMs to run Claude Code with all permissions bypassed automatically, enabling code execution and file editing without permission interruptions.

Enables an LLM or agent to drive a VUnit HDL unit-testing project end to end: list tests, compile, run, and inspect reports and per-test logs.

Enables delegating bounded development tasks to a locally installed DSH harness and returns a structured handoff covering file writes, command exit codes, missing checks, blockers, and evidence.

Enables AI assistants to create and modify Google Apps Script projects, manage versions and deployments, run functions, and inspect execution history through natural language.

Enables agents to accomplish multi-step work by writing a single sandboxed JavaScript program that drives shell, filesystem, HTTP, and every tool of any configured MCP server, returning only a small summary instead of raw JSON. This collapses dozens of advertised tool schemas into one tool, cutting per-request context by roughly 26× while keeping disabled upstream servers reachable.

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.