Universal Computer Control MCP
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- AlicenseNot gradedqualityCmaintenanceEnables AI agents to observe and operate real desktops, mobile devices, and browser pages through screenshots, native accessibility trees, and web automation, including clicking, typing, hotkeys, and UI lookup across macOS, Linux, Windows, Android, and iOS.7Apache 2.0
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to inspect and operate Windows applications through structured UI Automation maps, humanized mouse and keyboard input, and on-demand screenshots or OCR via MCP. It supports locating elements by role, name, or query; clicking, typing, scrolling, window management, event watching, and chained multi-step actions without relying on image analysis.MIT
- AlicenseNot gradedqualityCmaintenanceEnables MCP-capable AI agents to control Windows desktops through UI Automation element trees, native screenshots, app launching, batched action sequences, smart waits, and safety gates using normalized coordinates.MIT
- AlicenseAqualityCmaintenanceCross-platform desktop automation MCP server that lets AI agents capture screenshots, run OCR with UI-element classification, control mouse/keyboard, and launch programs on Linux, macOS, and Windows.201MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to automate desktop operating systems and applications through UI automation, screenshots, and input simulation.1MIT
- AlicenseNot gradedqualityCmaintenanceEnables MCP clients to observe and control the Windows desktop through the UI Automation accessibility tree — no vision model required — by exposing tools for reading windows and interactive elements, clicking, typing, scrolling, shortcuts, PowerShell, screenshots, virtual desktops, web scraping, file operations and persistent memory, including background-window control without stealing focus. An optional agent mode adds a single natural-language task tool that runs the full upstream agent loop when an LLM provider is configured.1MIT
TDQS
Scored across 29 tools
Most tools have clearly distinct purposes (click vs drag vs scroll, find_text vs find_visual, the many window operations are each specific). The only mild overlap is between computer.observe, computer.screenshot and computer.get_ui_tree, but observe's description explicitly positions it as the primary screen-understanding tool, keeping confusion low.
Every tool follows a strict namespace.verb_noun snake_case pattern (computer.click, computer.focus_window, computer.list_windows, computer.emergency_stop). No camelCase or mixed conventions anywhere, so the surface is highly predictable.
29 tools is on the high side, but the domain (full desktop automation spanning input, OCR/vision, accessibility, window management, clipboard, app launching and shell) legitimately needs this breadth and each tool maps to a distinct capability. Slightly heavy rather than gratuitous.
Coverage is comprehensive: observation/screenshot/UI-tree, the full input set (click variants, type, hotkey, scroll, drag), window lifecycle (list/focus/minimize/maximize/restore/close), clipboard read/write, app launch, shell execution, backend health, and safety controls (emergency stop/reset). No obvious dead ends for the stated purpose.