# Giving an LLM Eyes and Hands on a Mobile Simulator
The article discusses the integration of a mobile simulator with a perception-action loop for vision-capable LLMs. It explains how existing APIs were utilized to create tools for the model to interact with the simulator. The implementation allows the model to perform actions like tapping and swiping based on visual input, mimicking human interaction.
- ▪The perception-action loop involves looking at the simulator screen, deciding on an action, and executing it.
- ▪The MCP server connects to a tapflow relay and registers 13 tools for interaction with simulators.
- ▪The model can read text, identify UI elements, and perform actions using pixel coordinates from screenshots.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3944002) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Duchan Posted on May 30 # Giving an LLM Eyes and Hands on a Mobile Simulator #opensource #ios #android #mcp The interface a human uses When a person does QA in tapflow, the loop is: Look at the simulator screen Decide what to do (tap, swipe, type) Do it Look again This is exactly the perception-action loop that vision-capable LLMs are built for. The model sees a screenshot, reasons about what it shows, decides what action to take, and calls a tool to execute it.
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