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  3. Agent Device
Agent Device logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 2:17:44 PM

Agent Device

User RatingsBe the first to rate and review this MCP server!
View Repository4.5k GitHub StarsTotal stargazers on GitHub for the source repository (4,472 stars).Visit Website
automationmobiletestingcliaccessibility

CLI tool enabling AI agents to inspect, control, and verify apps on iOS, Android, tvOS, macOS, Linux, and web via accessibility snapshots and actions.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet β€” we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself β€” its README, its docs, or a verified owner. We haven’t found those for callstackincubator/agent-device, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Directory Badge Claim listing AlternativesπŸ“‚ More in Browser Automation

Overview

agent-device is a command-line interface for AI agents to automate and verify app behavior across multiple platforms including iOS, Android, tvOS, macOS, Linux, and web. It provides accessibility-based snapshots, UI element referencing, device actions, and evidence capture such as screenshots and videos. Use it to enable coding agents or QA tools to inspect app state, perform UI interactions, and diagnose issues in a repeatable workflow. It supports integration with agent terminals like Cursor, Codex, Claude Code, and Windsurf.

Use cases

β€’Inspect app UI state using accessibility snapshots and selectors
β€’Perform UI actions like tap, fill, scroll, and back with settling
β€’Capture evidence such as screenshots, videos, and logs for verification
β€’Automate and repeat workflows via saved scripts or exported YAML
β€’Diagnose app failures with performance samples, crash details, and React profiles

Key features

β€’Cross-platform support for iOS, Android, tvOS, macOS, Linux, and web
β€’Accessibility tree snapshots with token-efficient element references
β€’UI actions including press, fill, scroll, wait, and alert handling
β€’Evidence capture: screenshots, video, logs, traces, and network data
β€’Replayable workflows with script export and integration with agent terminals

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Agent Device.

Extracted Tool Capabilities
Cross-platform support for iOS, Android, tvOS, macOS, Linux, and web
Accessibility tree snapshots with token-efficient element references
UI actions including press, fill, scroll, wait, and alert handling
Evidence capture: screenshots, video, logs, traces, and network data
Replayable workflows with script export and integration with agent terminals

Documentation Overview

agent-device: mobile app automation and verification for AI coding agents

agent-device

npm version CI License: MIT Glama MCP server

Mobile app automation and verification for AI coding agents. Give coding agents a live app feedback loop through a CLI, built-in MCP server, or typed Node.js API.

Let your coding agent verify its changes in the running app. agent-device lets agents inspect, control, debug, and verify apps on iOS, Android, and HarmonyOS (simulators, emulators, and physical devices), plus tvOS, Android TV, Amazon Vega OS TV (Vega Virtual Device), web, macOS, and Linux. Agents read token-efficient accessibility snapshots instead of reasoning over screenshots alone, act through refs and selectors, and save evidence for review. It also coordinates device access across parallel agent worktrees and connects to remote device clouds.

Works with Claude Code, Codex, Cursor, Windsurf, Cline, Goose, and any agent that can run a CLI or connect over MCP, or as the runtime under agents you build with the AI SDK or Eve. Developers at Expensify, Shopify, and others use it to verify their apps.

Quick start

Install the CLI and check setup. It requires Node.js 22.12 or newer; web automation requires Node.js 24 or newer. See Installation for target requirements.

Terminal
npm install -g agent-device@latest
agent-device doctor
agent-device help workflow

Run doctor yourself before handing the CLI to an agent; help workflow links to the guides for debugging, replay, and profiling, and the installed help always matches the installed version.

Drive an app from the CLI

Add a contact in the built-in iOS Contacts app:

bash
# Start a session.
agent-device open Contacts --platform ios

# Inspect the screen. The example below shows the output; refs vary.
agent-device snapshot -i
# @e2 [button] "Add"

# Use the ref and wait for the UI to settle.
agent-device press @e2 --settle
# The diff includes:
# + @e7 [text-field] "First name"

agent-device fill @e7 "Ada" --settle
# The next diff shows changed values and current refs:
# - @e7 [text-field] "First name"
# + @e14 [text-field] "Ada"
# = @e15 [text-field] "Last name"

# Capture evidence and close the session.
agent-device screenshot ./contact-form.png
agent-device close

Refs are only valid from the latest output: after a --settle command, use the refs in its diff, and take a new snapshot only if the diff omits what you need. Snapshots come from the app's accessibility tree, so clear labels, roles, and test IDs make agent runs more reliable; use screenshots and video as evidence or when accessibility data is poor.

agent-device demo showing Codex using agent-device to create a new contact in the iOS Contacts app from a simple prompt

Add MCP tools to your agent

agent-device mcp starts the official stdio MCP server, exposing the installed commands as structured tools over the same execution path as the CLI:

config.json
{
  "mcpServers": {
    "agent-device": {
      "command": "agent-device",
      "args": ["mcp"]
    }
  }
}

See AI Agent Setup for per-client setup and when to prefer plain CLI over MCP.

Script it from Node.js

createAgentDeviceClient() gives Node.js code typed access to the same commands, as model tools in your own agent or from orchestration code:

server.ts
import { createAgentDeviceClient } from 'agent-device';

const client = createAgentDeviceClient({ session: 'qa-run' });
try {
  await client.apps.open({ app: 'com.apple.Preferences', platform: 'ios' });
  const snapshot = await client.capture.snapshot({ interactiveOnly: true });
  const button = snapshot.nodes.find((node) => node.role === 'button');
  if (button) await client.interactions.press({ ref: button.ref });
} finally {
  await client.sessions.close();
}

See the Node.js API, the runnable examples, and the AI SDK and Eve integration guides.

What agents can do

  • Inspect app state through accessibility snapshots, refs, selectors, and React Native component trees.
  • Act on visible UI by tapping or pressing elements, filling fields, scrolling, making gestures, waiting, asserting state, and handling alerts.
  • Diagnose failures with screenshots, video, logs, traces, network data, performance samples, crash details, and React profiles.
  • Repeat workflows by saving working steps as .ad scripts for local use or CI. Export strict Maestro YAML when needed.

See Commands for the commands and evidence each target supports.

Diagram of the agentic development loop: humans assign tasks, agents write and review code, agent-device verifies mobile apps, pull requests receive evidence, and bugs or performance issues lead to fixes

What to ask your agent

With the CLI installed, prompts like these work end to end:

  • "Implement the onboarding screen, run it on the iOS simulator and Android emulator, and attach screenshots."
  • "Reproduce this crash and capture the logs that lead up to it."
  • "Check whether this change causes unnecessary React Native re-renders."
  • "Explore the checkout flow once, save it as a replay script, and run it in CI."
  • "Verify this pull request on a physical device and attach reviewable evidence."

Next steps

  • AI Agent Setup: skills, project rules, and per-client setup for Cursor, Codex, Claude Code, Windsurf, and others.
  • Quick Start: a guided run on the bundled Expo test app with screenshots, replay, and performance data.
  • Replay & E2E and Debugging & Profiling: repeatable tests and bug hunting.

Where to run agent-device

The same session and evidence model works at every step: the agent explores the app, captures evidence, saves a replay, runs it in CI, and moves onto remote devices.

PathBest forStart with
LocalTrying commands and debugging apps on simulators, emulators, physical devices, macOS, and Linux.Follow the Quick Start.
CI/CDAutomated pull request and merge validation with replay scripts and captured artifacts.Try the EAS workflow template.
Cloud / remoteLinux runners, managed devices, and remote jobs.Set up a remote proxy, connect a device cloud (BrowserStack, AWS Device Farm, Limrun), or contact Callstack for team QA.

How it works

agent-device keeps device state in sessions. It uses a local accessibility bridge for iOS Simulator snapshots and XCTest for iOS interactions, physical iOS, and tvOS; ADB and the snapshot helper on Android; HDC and ArkUI uitest on HarmonyOS; Vega CLI/VDA on the Vega Virtual Device; a local helper on macOS; and AT-SPI on Linux.

Support depth varies by target. Newer backends such as HarmonyOS and Vega OS cover a subset of commands; run agent-device capabilities --platform <platform> to see what a target supports.

Sessions are scoped to the caller's git worktree, and host-local device claims stop parallel agents from taking over each other's simulators and emulators. Inspect ownership without a daemon via agent-device device status, and settle provably dead owners with agent-device device release --stale. The same commands drive hosted devices on BrowserStack, AWS Device Farm, and Limrun.

agent-device uses the inspect-act-verify process from Vercel's agent-browser for mobile, TV, and desktop apps. Basic --platform web support runs agent-browser in the same session and replay system.

FAQ

What is agent-device?

agent-device is a command-line tool and MCP server that lets AI coding agents inspect, control, and verify mobile apps and save evidence for review. It supports iOS, Android, HarmonyOS, TV, web, macOS, and Linux.

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
4.5k
Stargazers on the source repository.
Last commit
1d ago
Most recent push to the default branch.
Directory activity
1 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Agent Device

It supports iOS, Android, tvOS, Android TV, Amazon Vega OS TV (VVD only), web, macOS, and Linux.

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Technical Specs & Signals

CategoryπŸ“‚Browser Automation
PricingFree
More technical detailsExpand β–Ύ
AuthNo auth required
LicenseMIT
ClientsCursor, Claude Desktop, Windsurf, Cline / VS Code
Last updatedSep 9, 2026
10/10 checks healthy over the last 31d
Views1
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars4,472
GitHub Star CountTotal stargazers on GitHub representing community popularity (4,472 stars).
Last commit1d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 9, 2026
52Quality signal: Good Β· 52/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools19/30
Adoption & activity10/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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