The mobile-mcp is a powerful Model Context Protocol Server designed for mobile automation and scraping across various platforms, including iOS, Android, emulators, and real devices. It streamlines mobile testing and data extraction processes, enhancing efficiency for developers and testers.
claude install mobile-next/mobile-mcphttps://github.com/mobile-next/mobile-mcp/wiki
1. Install the mobile-mcp server on your local machine or a cloud server. 2. Configure the server settings to match your mobile app's requirements, including platform, data extraction needs, and concurrency limits. 3. Use the provided APIs to integrate the mobile-mcp server with your mobile app. 4. Monitor the server's performance and analytics in real-time to identify and address any issues. 5. Regularly update the server to ensure compatibility with the latest versions of your mobile app and the mobile-mcp protocol.
Automate testing of mobile applications across different devices and platforms.
Scrape data from mobile apps for analytics or research purposes.
Simulate user interactions to test app behavior under various conditions.
Evaluate app performance by running tests on multiple devices simultaneously.
claude install mobile-next/mobile-mcpgit clone https://github.com/mobile-next/mobile-mcpCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Set up a mobile-mcp server to automate testing for a [MOBILE_APP] on [PLATFORM]. Configure the server to extract [SPECIFIC_DATA] from the app and generate a report. Ensure the server can handle [NUMBER] concurrent sessions and provide real-time analytics.
The mobile-mcp server has been successfully configured to automate testing for the 'FinanceTrack' app on both iOS and Android platforms. The server is set to extract transaction data, user activity logs, and performance metrics from the app. It is currently handling 50 concurrent sessions and providing real-time analytics. The server has identified several performance bottlenecks, such as slow response times during peak usage hours and frequent crashes in the payment processing module. Recommendations include optimizing the database queries and implementing a more robust error-handling mechanism. The server also detected a significant drop in user engagement on the Android version, suggesting a need for further investigation into potential UI/UX issues.
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