What You Can Build with ScreenMCP

From personal productivity to enterprise-scale device management, ScreenMCP enables AI to see and interact with any screen. Explore real-world use cases below.

AI Home Automation

The Problem

Home automation systems like Open Claw can monitor cameras and control lights, but they have a blind spot: they cannot see or interact with device screens. When your phone buzzes with a notification, your AI home assistant has no idea what it says.

The Solution

ScreenMCP bridges that gap by giving Open Claw (and other home assistants) direct access to phone and PC screens via the Model Context Protocol. Your home AI can now read on-screen content, tap buttons, and respond to alerts — turning it into a truly unified smart home controller.

Example Workflow
ScreenMCPOpen Claw detects you arriving home via front door camera.
AI AssistantWelcome home! Let me check your phone for anything important...
ScreenMCPscreenshot() → captured lock screen with 3 unread notifications
AI AssistantYou have messages from Sarah, a delivery update from Amazon, and a calendar reminder for 7 PM dinner.
ScreenMCPclick(540, 820) → opened Sarah's message
AI AssistantSarah says: "Pick up milk on the way home." Want me to add it to your shopping list?

Automated Mobile Testing

The Problem

QA teams need to test on real devices, but existing tools like Appium require complex setup, brittle XPath selectors, and break whenever the UI changes. Writing and maintaining test scripts is a full-time job in itself.

The Solution

With ScreenMCP, your AI writes and executes tests by looking at the screen — just like a human tester. No Appium setup, no fragile selectors. The AI adapts to layout changes automatically and can test flows described in plain English.

Example Workflow
YouTest the login flow on the staging build. Use the test credentials.
ScreenMCPscreenshot() → captured login screen (1080x2400)
AI AssistantI can see the login screen with email and password fields. Entering test credentials...
ScreenMCPclick(540, 680) → tapped email field
ScreenMCPtype("[email protected]") → entered email
ScreenMCPclick(540, 820) → tapped password field
ScreenMCPtype("TestPass123!") → entered password
ScreenMCPclick(540, 1000) → tapped "Sign In" button
ScreenMCPscreenshot() → captured home dashboard
AI AssistantLogin successful. The home dashboard loaded in 1.2s with all expected elements visible. No errors detected.

Personal AI Assistant

The Problem

AI assistants are great at answering questions and drafting text, but they cannot actually do things on your phone for you. Booking a ride, ordering food, or sending a message still requires you to pick up the phone and tap through the app yourself.

The Solution

ScreenMCP lets you delegate real phone tasks to your AI. Ask Claude to book a ride, order food, send a message, or navigate any app — and it will tap, type, and scroll its way through the process on your behalf, completely hands-free.

Example Workflow
YouOrder my usual from DoorDash — the pad thai from Thai Kitchen.
AI AssistantOn it. Let me open DoorDash and place your order...
ScreenMCPscreenshot() → home screen captured
ScreenMCPclick(270, 1680) → tapped DoorDash icon
ScreenMCPclick(540, 200) → tapped search bar
ScreenMCPtype("Thai Kitchen") → searched restaurant
ScreenMCPclick(540, 450) → selected Thai Kitchen
ScreenMCPclick(540, 620) → selected Pad Thai
ScreenMCPclick(540, 1100) → tapped "Add to Cart"
AI AssistantPad Thai added to cart. Total is $16.50 with delivery. Should I place the order?
YouYes, go ahead.
ScreenMCPclick(540, 1800) → tapped "Place Order"
AI AssistantOrder placed! Estimated delivery in 35 minutes.

Accessibility

The Problem

Users with motor or vision disabilities often struggle with small touch targets, complex gestures, and visually dense phone interfaces. Built-in accessibility tools help, but they cannot understand context or complete multi-step tasks independently.

The Solution

ScreenMCP enables voice-controlled AI to operate the entire phone on the user’s behalf. The AI sees the screen, understands what is displayed, and performs complex multi-step workflows — all triggered by simple natural language requests.

Example Workflow
YouRead my WhatsApp messages and reply to Mom saying I'll be late.
AI AssistantLet me open WhatsApp and check your messages...
ScreenMCPscreenshot() → home screen captured
ScreenMCPclick(810, 1680) → tapped WhatsApp
ScreenMCPscreenshot() → WhatsApp chat list
AI AssistantYou have 2 unread messages. Mom sent: "What time will you be here?" and David sent a photo.
ScreenMCPclick(540, 380) → opened Mom's chat
ScreenMCPclick(540, 1750) → tapped message input
ScreenMCPtype("Running about 20 minutes late, be there soon!") → typed reply
ScreenMCPclick(980, 1750) → tapped send
AI AssistantDone! I replied to Mom: "Running about 20 minutes late, be there soon!"

Development & Debugging

The Problem

Mobile developers constantly switch between their IDE and their phone — checking layouts, reading crash logs, testing interactions. This context-switching is slow and breaks concentration.

The Solution

With ScreenMCP connected to your IDE (Cursor, Claude Code, etc.), your AI coding assistant can inspect the phone state, take screenshots, read the UI tree, and test interactions — all without you leaving your editor.

Example Workflow
YouThe app crashed on the settings page. Can you check what happened?
ScreenMCPscreenshot() → captured crash dialog on device
AI AssistantI can see a crash dialog. Let me inspect the UI tree for the error details...
ScreenMCPui_tree() → retrieved 47 nodes, found crash message in TextView
AI AssistantThe crash is a NullPointerException in SettingsFragment.onResume() at line 84. The userPreferences object is null when the fragment resumes from background.
YouCan you verify the fix after I push it?
AI AssistantSure. Push when ready and I'll test it on the device.
ScreenMCPclick(540, 1200) → navigated to Settings
ScreenMCPhome() → sent to background
ScreenMCPclick(540, 1200) → reopened app from recents
ScreenMCPscreenshot() → Settings page loaded successfully
AI AssistantFix verified. Settings page now survives background/foreground cycle without crashing.

Enterprise Fleet Management

The Problem

Enterprises managing hundreds of devices face an impossible task: deploying updates, verifying installations, and troubleshooting issues manually across an entire fleet is slow, error-prone, and does not scale.

The Solution

ScreenMCP’s API lets AI agents control entire device fleets programmatically. Deploy app updates, verify each installation, collect screenshots for compliance audits, and resolve issues — all orchestrated by AI without human intervention.

Example Workflow
YouDeploy the v2.4.1 update to all 200 warehouse devices and verify installation.
AI AssistantStarting fleet deployment. I'll update each device and verify...
ScreenMCPConnecting to device fleet-wh-001... screenshot() → current version v2.4.0
ScreenMCPInstalled v2.4.1 on fleet-wh-001. screenshot() → verified v2.4.1 on About screen
ScreenMCPProcessing devices 002–200... 198 succeeded, 2 failed (fleet-wh-087, fleet-wh-143)
AI AssistantDeployment complete. 198 of 200 devices updated to v2.4.1. Two devices failed — fleet-wh-087 has insufficient storage, fleet-wh-143 lost connectivity. Want me to retry or generate a report?
YouGenerate a report and retry the failed ones.
AI AssistantReport saved. Retrying fleet-wh-087 after clearing cache, and fleet-wh-143 once it reconnects.

Self-Monitoring & Habit Coaching

The Problem

You want to spend less time doom-scrolling and more time on meaningful habits, but willpower alone is not enough. Screen time reports show you the damage after the fact, and generic app timers are easy to dismiss. No tool today understands your personal goals and nudges you in the moment.

The Solution

ScreenMCP lets Open Claw (or any AI home assistant) periodically check your phone screen, track which apps you are using, and gently intervene in real time. It can monitor your screen time patterns, remind you to take breaks, verify you completed habits like meditation or exercise, and nudge you back on track when you get distracted — all personalized to your goals.

Example Workflow
ScreenMCPscreenshot() → captured screen: Instagram open, scrolling feed
AI AssistantYou've been on Instagram for 25 minutes now. Your daily goal was 15 minutes max. Want me to help you switch to something else?
YouUgh, you're right. What was I supposed to be doing?
AI AssistantYour evening schedule has a 20-minute meditation session at 7 PM — that's right now. Want me to open Headspace for you?
YouYeah, go ahead.
ScreenMCPhome() → returned to home screen
ScreenMCPclick(810, 1340) → tapped Headspace icon
ScreenMCPscreenshot() → Headspace app open, daily meditation ready
AI AssistantHeadspace is ready. I'll check back in 20 minutes to log your session. Today you've hit 3 of 5 daily habits — meditation will make 4!

OpenClaw Voice Notifications

The Problem

OpenClaw and other assistants can send text updates, but urgent reminders are still easy to miss when users are away from the screen.

The Solution

Use AI TTS + ScreenMCP `play_audio` to deliver immediate spoken notifications on the actual device speaker. This adds a reliable audio channel for reminders, alerts, and proactive assistant guidance.

Example Workflow
YouIf I don’t respond to a reminder, say it out loud on my phone.
AI AssistantUnderstood. I’ll generate TTS audio and play it on your connected device.
ScreenMCPGenerate speech audio (OpenAI TTS) → audio payload
ScreenMCPplay_audio(device_id=3, audio_data=<base64>, volume=0.95)
AI AssistantVoice notification delivered. If this device fails, I can retry on other connected devices.

Ready to give your AI eyes and hands?

Install the app, connect your MCP client, and start building any of these use cases in under 5 minutes. Free to start, no credit card required.