MCP server for controlling VirtualBox VMs — screenshots, keyboard input, PowerShell, vagrant, WinRM, podman, and CI build pipelines
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"mcp-vm-blackbox","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# vm-flightsimulator [](https://badge.fury.io/py/mcp-vm-blackbox) [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) [](https://github.com/bitflight-devops/vm-flightsimulator/actions/workflows/test.yml) A Claude Code plugin that gives AI agents the ability to **see and act on real virtual machines**, turning them into a unified machine control substrate for validating software across complete workflows. The plugin provides a **collaborative testing partner**: describe what you want to test, and AI-driven pilots autonomously work through it with full observability. A real-time dashboard lets you watch progress and steer agents mid-flight. --- ## What This Is **Not** a specific-scenario tool. **A toolkit** for long-running, AI-driven validation work on any virtual machine. You ask: "Install this app and verify it works end-to-end." The plugin provides the control surface (screenshot, keyboard, mouse, PowerShell, WinRM). Pilots fly the VM, inspectors analyze what happened, and a distributed coordinator keeps everything moving without blocking. Currently targets **VirtualBox + Vagrant**. Designed for expansion to AWS, OpenTofu, Cloudflare Workers, bare metal, and beyond. --- ## The Actor Model Understanding who does what: ### Pilots AI agents that act on VMs. They see (screenshots), type (keyboard/mouse), run commands (PowerShell/SSH), and report outcomes. Follow a tight **declare-execute-reflect** loop: log intent → perform action → log outcome. Autonomous; do not return to the orchestrator after every action. ### Inspectors AI agents that analyze without touching. Dispatched on-demand to read pilot transcripts, cross-refe…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.