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native-profiler-start

Start native profiling on a booted device to capture CPU, hang, and memory data from a running app. Automatically detects the app process unless specified.

Instructions

Start native profiling on a booted device. iOS: Instruments via xctrace (CPU, hangs, memory). Android: Perfetto (CPU, jank, RSS-growth weak signal). Auto-detects the running app process unless app_process is explicitly provided. After starting, let the user interact with the app, then call native-profiler-stop. Use when you want to capture native CPU, hang, and memory data for a running app. Returns { status, pid, traceFile } confirming the recording has started. Fails if no app is running on the device, or the profiler cannot attach to the process.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
device_idYesTarget device id from `list-devices` (iOS UDID or Android serial).
app_processNoiOS: the CFBundleExecutable or display name of the app to profile. Android: the app's package name. If omitted, auto-detects the currently running foreground app. Only provide this if auto-detection picks the wrong app.
template_pathNoiOS-only: path to an Instruments .tracetemplate file (defaults to bundled Argent template). Ignored on Android.
Behavior4/5

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

With no annotations, description carries full burden. It discloses auto-detection, platform-specific tools, return structure, and failure conditions. Does not mention rate limits or concurrency constraints.

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

Conciseness5/5

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

Concise (~100 words), well-structured with platform details, workflow, and failure modes. No unnecessary content.

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

Completeness4/5

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

Covers purpose, usage, parameters, return, and failures. Could detail return format more, but acceptable without output schema.

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

Parameters5/5

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

Schema coverage is 100%, yet description adds meaning: explains auto-detection, when to provide app_process, and iOS-specific template_path. Also describes return value.

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?

Description clearly states it starts native profiling on a booted device, distinguishes between iOS and Android, and differentiates from sibling tools like native-profiler-stop and native-profiler-analyze. It specifies the purpose: capturing native CPU, hang, and memory data.

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 clear context: use when wanting native profiling data, workflow instructions (start, let user interact, then call stop), and auto-detection vs explicit app_process. But does not explicitly mention alternatives like react-profiler-start for React profiling.

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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