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Agent Device

Automates and verifies apps across mobile, TV, web, macOS, and Linux through CLI, MCP tools, or a typed Node.js API.

Install Agent Device

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Open setup steps on GitHub

Overview

  • mobile-testing
  • app-automation
  • mcp
  • developer-tools

The agent-device MCP server gives coding agents structured access to running iOS, Android, HarmonyOS, TV, web, macOS, and Linux apps. It maintains device sessions, reads accessibility snapshots, interacts through refs and selectors, and captures evidence such as screenshots, logs, traces, and performance data. Use it when an agent needs to verify UI changes, investigate failures, or replay workflows on simulators, emulators, physical devices, or remote device infrastructure. The server runs as the official stdio MCP entry point and exposes the same command path as the CLI.

Use cases

  • Verify UI changes on iOS simulators and Android emulators
  • Reproduce crashes and collect diagnostic evidence
  • Capture screenshots and video for pull-request review
  • Save explored workflows as replay scripts for CI
  • Profile React Native rendering and app performance

Key features

  • Accessibility snapshots with refs and selectors
  • Cross-platform app interaction
  • Screenshots, video, logs, traces, and crash data
  • Replay scripts and Maestro YAML export
  • Typed Node.js client
  • Remote device and parallel worktree coordination

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

9/9 checks healthy over the last 34d

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars5.1kStargazers on the source repository.
Last commit2d agoMost recent push to the default branch.
Directory activity2 viewsConfig copies, upvotes, and views on AllMCPs.

How Agent Device works

What agent-device does

The agent-device MCP server connects AI coding agents to running applications and supported devices so they can inspect, control, debug, and verify app behavior. Supported targets include iOS, Android, and HarmonyOS simulators, emulators, and physical devices, along with tvOS, Android TV, Amazon Vega OS TV through a Vega Virtual Device, web, macOS, and Linux.

Agents can inspect accessibility state instead of relying only on screenshots. They can identify controls with refs, selectors, labels, roles, and test IDs, then perform actions such as pressing controls, filling fields, scrolling, gesturing, waiting, asserting state, and responding to alerts.

How it works

The agent-device MCP server starts through the installed CLI with agent-device mcp. It exposes structured MCP tools over stdio and follows the same execution path as the command-line interface. Sessions hold device and app state while an agent explores a workflow, captures evidence, saves a replay, or closes the session.

Platform backends vary. iOS uses an accessibility bridge for simulator snapshots and XCTest for interactions, physical iOS devices, and tvOS. Android uses ADB and a snapshot helper. HarmonyOS uses HDC and ArkUI uitest, while Vega devices use Vega CLI and VDA. macOS uses a local helper, and Linux uses AT-SPI.

The project also provides a typed Node.js client through createAgentDeviceClient(). That client can be used directly from orchestration code or as the runtime behind agents built with the AI SDK or Eve.

Setup and configuration

Install the CLI globally with Node.js 22.12 or newer. Web automation requires Node.js 24 or newer. Run agent-device doctor to check the local setup, then use agent-device help workflow to find installed-version guidance for debugging, replay, and profiling.

Add the MCP server to a client by configuring the command agent-device with the argument mcp. The server can also be used without MCP through the CLI or from Node.js code. Local, CI/CD, and remote-device workflows are supported; remote use can involve a proxy or a connected device cloud.

Tools and capabilities

The agent-device MCP server supports capabilities for:

  • Reading accessibility snapshots, interactive elements, refs, selectors, and React Native component trees.
  • Pressing controls, entering text, scrolling, making gestures, waiting for UI changes, asserting state, and handling alerts.
  • Capturing screenshots and video, plus collecting logs, traces, network data, performance samples, crash details, and React profiles.
  • Saving completed workflows as .ad replay scripts for local execution or CI.
  • Exporting strict Maestro YAML when that format is required.
  • Coordinating device access across parallel agent worktrees and connecting to remote device clouds.

Limitations and notes

Support depth differs by platform. The README specifically notes that newer backends, including HarmonyOS and Vega OS, implement only a subset of commands. Check capabilities for the target platform before depending on a workflow.

Refs are tied to the latest command output. After an action with settling enabled, use refs from the returned diff; take another snapshot when the diff does not contain the needed element. Accessibility metadata affects reliability, so screenshots or video may be needed when the app exposes poor labels, roles, or test IDs. Evidence capture and command availability also vary by target.

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Frequently Asked Questions about Agent Device

agent-device is an MCP server that connects AI coding agents to running iOS, Android, HarmonyOS, TV, web, macOS, and Linux apps. Its main tools inspect accessibility snapshots, control UI elements, capture screenshots and diagnostics, profile apps, and save workflows as replay scripts for local or CI verification.

Install it with Node.js 22.12 or newer using `npm install -g agent-device@latest`, then run `agent-device doctor`. Start the MCP endpoint with `agent-device mcp`; web automation requires Node.js 24 or newer.

Configure a stdio MCP server whose command is `agent-device` and whose argument is `mcp`.

It supports iOS, Android, and HarmonyOS simulators, emulators, and physical devices, plus tvOS, Android TV, Amazon Vega OS TV, web, macOS, and Linux. Available commands vary by platform backend.

Yes. Workflows can be saved as `.ad` scripts for local use or CI, and strict Maestro YAML can be exported when needed. The same session and evidence model can be used during exploration and later automated validation.

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