MCP server
by hetaobackend
Automate GUI testing and control across OS with PyAutoGUI. Perform mouse, keyboard, screenshots, and image recognition e
Automates GUI interactions by controlling mouse movements, keyboard input, and screen capture across Windows, macOS, and Linux. Enables programmatic control of any desktop application through PyAutoGUI.
PyAutoGUI is a community-built MCP server published by hetaobackend that provides AI assistants with tools and capabilities via the Model Context Protocol. Automate GUI testing and control across OS with PyAutoGUI. Perform mouse, keyboard, screenshots, and image recognition e It is categorized under developer tools.
You can install PyAutoGUI in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
PyAutoGUI is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
PyAutoGUI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
PyAutoGUI is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Strong directory entry: PyAutoGUI surfaces stars and publisher context so we could sanity-check maintenance before adopting.
According to our notes, PyAutoGUI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Useful MCP listing: PyAutoGUI is the kind of server we cite when onboarding engineers to host + tool permissions.
I recommend PyAutoGUI for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
PyAutoGUI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
PyAutoGUI reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We wired PyAutoGUI into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: PyAutoGUI surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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A MCP (Model Context Protocol) server that provides automated GUI testing and control capabilities through PyAutoGUI.
The server implements the following tools:
Install the package:
pip install mcp-pyautogui-server
On MacOS:
~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows:
%APPDATA%/Claude/claude_desktop_config.json
Development/Unpublished Servers Configuration:
{
"mcpServers": {
"mcp-pyautogui-server": {
"command": "uv",
"args": [
"--directory",
"/path/to/mcp-pyautogui-server",
"run",
"mcp-pyautogui-server"
]
}
}
}
Published Servers Configuration:
{
"mcpServers": {
"mcp-pyautogui-server": {
"command": "uvx",
"args": [
"mcp-pyautogui-server"
]
}
}
}
uv sync
uv build
uv publish
Note: Set PyPI credentials via environment variables or command flags:
--token or UV_PUBLISH_TOKEN--username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORDFor the best debugging experience, use the MCP Inspector.
Launch the MCP Inspector via npm:
npx @modelcontextprotocol/inspector uv --directory /path/to/mcp-pyautogui-server run mcp-pyautogui-server
The Inspector will display a URL that you can access in your browser to begin debugging.
This project is licensed under the MIT License - see the LICENSE file for details.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.