MCP Sandbox Computer VM for AI MCP server

MCP Sandbox Computer VM for AI Named, manageable Linux computers for AI agents — via MCP

MCP Sandbox Computer VM for AI is a lifecycle-focused fork of Kilntainers. It gives agents isolated Linux computers, stable IDs, temporary or persistent lifecycles, an interactive MCP App dashboard, and first-class Docker and Fly Machines backends.

<!-- mcp-name: io.github.flujo-app/mcp-sandbox-computer-vm-for-ai -->

• 🖥️ MCP App dashboard: List computers, run commands, restart, factory reset, and delete from FLUJO or another stable MCP Apps host.
• 🏷️ Named computers: Reconnect with a stable computer_id, or omit it to receive a readable random slug.
• 💾 Explicit lifecycle: Temporary computers are removed with their MCP session; permanent computers survive and can be reattached later.
• 🧰 Multiple backends: Docker/Podman, native Fly Machines, Modal, E2B, and WebAssembly.
• 🏝️ Isolated per agent: Every agent gets its own dedicated sandbox — no shared state, no cross-contamination.
• 🔒 Secure by design: The agent communicates with the sandbox over MCP — it doesn’t run inside it. No agent API keys, code, or prompts are exposed to the sandbox.
• 🔌 Tool and UI access: terminal_execute stays simple, while optional provider-neutral lifecycle tools power both models and the dashboard.
• 📈 Scalable: Scale from a few agents on your laptop to thousands running in parallel in the cloud.

Why sandbox computers?

Agents are already excellent at using terminals and can save thousands of tokens with common Linux utilities like grep, find, jq, and awk. Giving an agent access to the host OS is dangerous, while provisioning large numbers of isolated environments is operationally painful. MCP Sandbox Computer VM for AI gives every agent a dedicated sandbox with an explicit lifecycle.

Quick Start

Run the released package directly from PyPI. Docker and stdio are the defaults:

uvx mcp-sandbox-computer-vm-for-ai

Add it to Claude Code:

claude mcp add --scope user sandbox-computer -- uvx mcp-sandbox-computer-vm-for-ai

Or add it to a JSON-based MCP client such as Claude Desktop:

{
  "mcpServers": {
    "sandbox-computer": {
      "command": "uvx",
      "args": ["mcp-sandbox-computer-vm-for-ai"]
    }
  }
}

By default, the server exposes only terminalexecute. Set ENABLELIFECYCLETOOLS=true before starting the server to expose the computer tools and MCP App dashboard. For a JSON-based stdio client, add it to the server configuration:

{
  "env": {
    "ENABLE_LIFECYCLE_TOOLS": "true"
  }
}

Then call computer_dashboard to open the App. The dashboard has no external browser dependencies. Its internal resource URI remains ui://kilntainers/computers for compatibility with the upstream implementation.

Named computer lifecycle

terminal_execute accepts two additional optional inputs:

• computer_id: a 1–63 character lowercase slug. The first call without one creates a readable random ID and reuses it as that MCP session's default.
• temporary: defaults to true. Temporary computers are removed when the owning MCP session closes. Set it to false for a computer that survives server/session shutdown and can be reattached later by ID.

Shortened. The full README is on GitHub.