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.
Enables AI agents to interact with Windows operating systems through native UI automation, file navigation, application control, and system commands. Provides seamless integration between LLMs and Windows environments for tasks like clicking, typing, launching apps, and capturing desktop state.
Enables Windows UI Automation through MCP, allowing agents to inspect, search, navigate, and interact with desktop UI elements via the UIAutomation framework. Supports actions such as focus, invoke, set-value, toggle, and event waiting, with CLI and VS Code extension variants.
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.
Enables AI agents to interact with the Windows operating system, performing tasks such as file navigation, application control, UI interaction, and QA testing.
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.
