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"How to run code using Claude Desktop" matching MCP servers:

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    An MCP server that provides Claude with comprehensive desktop automation capabilities including browser control, window management, and native mouse/keyboard input on Windows. It enables users to capture screenshots, launch applications, and interact with the system clipboard through natural language.
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    MCP server that fixes Windows DPI coordinate scaling for AI desktop automation. Provides UI element finding via Windows Accessibility APIs and coordinate conversion between physical and logical coordinate systems.
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    MIT
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    MCP server for controlling a transparent sci-fi cockpit overlay on your desktop, allowing AI agents to spawn, move, configure, and close holographic widgets (clock, system stats, map, etc.) via MCP.
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    MIT
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    Provides tools for executing shell commands both synchronously and asynchronously with real-time output streaming and process management capabilities. It enables users to start background tasks, monitor progress, and manage long-running processes via Stdio or HTTP transports.
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    MIT
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    A Model Context Protocol server that provides desktop automation capabilities using RobotJS and screenshot capabilities, enabling LLMs to control mouse movements, keyboard inputs, and capture screenshots of the desktop environment.
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    MIT
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    MCP server for organizing files on your Mac Desktop by automatically categorizing them into folders. Supports listing, previewing, and moving files into category folders (Images, Documents, Videos, etc.).
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    macOS desktop automation enabling AI agents to screenshot, switch apps and tabs, click, type, and scroll. Offers two trust levels: read-only screenshot and full UI control.
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    GNOME desktop automation for AI agents. 30 tools via D-Bus: screenshots, window management, mouse/keyboard injection, clipboard, workspaces, and system notifications. Works on any GNOME 45–49 Linux desktop.
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    GPL 3.0
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    Enables high-speed native macOS automation for Claude by interacting directly with the Accessibility API, AppleScript, and UI trees instead of using screenshots. It allows users to read app states, click elements, and type text semantically across any macOS application.
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    MIT
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    Enables AI assistants to control Ubuntu desktops through screenshots, mouse clicks, and keyboard interactions using AT-SPI integration and computer vision. It features optimized element detection and workflow batching for fast and accurate visual interaction with desktop applications.
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    MIT
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    Provides native macOS desktop automation for AI agents, enabling screen capture, mouse/keyboard control, window management, and iOS/Android simulator control in both foreground and background modes without focus stealing.
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    MIT
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    Enables AI assistants to interact with native Linux desktop applications through AT-SPI2 accessibility interfaces. Provides semantic element targeting, natural language search, and automation capabilities (clicking, typing, keyboard shortcuts) across GTK, Qt, and Electron applications.
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    MIT