PyAutoGUI▌

by hetaobackend
Automate GUI testing and control across OS with PyAutoGUI. Perform mouse, keyboard, screenshots, and image recognition e
Enables automated GUI testing and control across operating systems by wrapping PyAutoGUI to perform mouse movements, keyboard input, screenshot capture, and image recognition tasks.
best for
- / Automated GUI testing and QA workflows
- / Desktop application automation
- / Screen scraping and UI monitoring
- / Repetitive task automation
capabilities
- / Control mouse movements and clicks
- / Simulate keyboard input and hotkey combinations
- / Take screenshots and capture screen content
- / Find images and get pixel colors on screen
- / Perform drag and drop operations
- / Get screen dimensions and mouse position
what it does
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.
about
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.
how to install
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.
license
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.
readme
mcp-pyautogui-server
A MCP (Model Context Protocol) server that provides automated GUI testing and control capabilities through PyAutoGUI.
Features
- Control mouse movements and clicks
- Simulate keyboard input
- Take screenshots
- Find images on screen
- Get screen information
- Cross-platform support (Windows, macOS, Linux)
Tools
The server implements the following tools:
Mouse Control
- Move mouse to specific coordinates
- Click at current or specified position
- Drag and drop operations
- Get current mouse position
Keyboard Control
- Type text
- Press individual keys
- Hotkey combinations
Screen Operations
- Take screenshots
- Get screen size
- Find image locations on screen
- Get pixel colors
Installation
Prerequisites
- Python 3.12+
- PyAutoGUI
- Other dependencies will be installed automatically
Install Steps
Install the package:
pip install mcp-pyautogui-server
Claude Desktop Configuration
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"
]
}
}
}
Development
Building and Publishing
- Sync dependencies and update lockfile:
uv sync
- Build package distributions:
uv build
- Publish to PyPI:
uv publish
Note: Set PyPI credentials via environment variables or command flags:
- Token:
--tokenorUV_PUBLISH_TOKEN - Username/password:
--username/UV_PUBLISH_USERNAMEand--password/UV_PUBLISH_PASSWORD
Debugging
For 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.
License
This project is licensed under the MIT License - see the LICENSE file for details.
FAQ
- What is the PyAutoGUI MCP server?
- PyAutoGUI is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for PyAutoGUI?
- This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
PyAutoGUI is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Piyush G· Sep 9, 2024
We evaluated PyAutoGUI against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Useful MCP listing: PyAutoGUI is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakshi Patil· Jul 7, 2024
PyAutoGUI reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend PyAutoGUI for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· May 5, 2024
Strong directory entry: PyAutoGUI surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Apr 4, 2024
PyAutoGUI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Rahul Santra· Mar 3, 2024
According to our notes, PyAutoGUI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Pratham Ware· Feb 2, 2024
We wired PyAutoGUI into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Yash Thakker· Jan 1, 2024
PyAutoGUI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.