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clivejefferies

mobile-debug-mcp

restart_app

Restart a mobile app on Android or iOS, then verify the landing screen to confirm the app reopened correctly.

Instructions

Purpose: Restart a mobile app on Android or iOS.

Inputs:

  • platform

  • appId

  • deviceId (optional)

Output Structure:

  • action_id, timestamp (ISO 8601), action_type

  • lifecycle_state: post-dispatch lifecycle state (pending_verification or failed)

  • source_module: runtime source of the action envelope

  • target.selector = { appId }

  • success = true when the restart command completed

  • failure_code/retryable when restart dispatch fails

  • ui_fingerprint_before/ui_fingerprint_after when available

Recommended Usage:

  1. Define the expected landing screen when it is known

  2. Call restart_app

  3. If needed, wait for transition using wait_for_*

  4. Verify with expect_screen

  5. If verification fails, retry once or capture a snapshot

Verification Guidance:

  • Follow RESOLVE → ACT → WAIT (if needed) → EXPECT

  • expect_screen is the authoritative verification step when the reopened screen is known

  • Do not treat timing or screen change alone as final verification

Failure Handling:

  • TIMEOUT → retry once

  • UNKNOWN → inspect snapshot/logs before retrying

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
appIdYesAndroid package name or iOS bundle id
deviceIdNoDevice UDID (iOS) or Serial (Android). Defaults to booted/connected.
platformYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, but the description fully covers behavioral traits: output structure, success/failure conditions, lifecycle state, and integration with verification steps. It also includes failure handling for TIMEOUT and UNKNOWN cases.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections (Purpose, Inputs, Output, Recommended Usage, etc.). While verbose, every section adds value for an AI agent, and the purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description provides a detailed output structure and workflow integration with sibling tools. It is complete for the tool's complexity and the given context signals.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67%, and the description lists parameter names but adds minimal additional meaning beyond the schema's descriptions. The baseline for this coverage is 3, and the description does not significantly elevate it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'restart' and the resource 'mobile app on Android or iOS'. It distinguishes itself from sibling tools like start_app, terminate_app, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a step-by-step recommended usage pattern including waiting and verification with other tools. Does not explicitly state when not to use it or alternatives, but the guidance is clear and context-aware.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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