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    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables 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
    • A
      license
      Not graded
      quality
      A
      maintenance
      Gives AI agents full control of a Windows computer over MCP through 63 native tools — screenshots and UI trees for vision, mouse/keyboard input, Chrome control via CDP, plus files, terminal, processes, and SSH/SFTP. The agent can look at the screen, click, type, launch apps, run commands, and verify each step with a fresh screenshot.
      47
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables 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
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables 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.
      1
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables an MCP host to observe and control Windows desktop applications deterministically: reading UIA control trees, OCR text and pixel diffs, clicking, typing, scrolling and window management, plus verbatim text reading/appending via ValuePattern/TextPattern. Visual judgment is delegated back to the host model's own multimodal context through annotated screenshots, numbered element cards, grid maps and crosshair zoom-ins, so no separate vision model is needed.
      1
      AGPL 3.0