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Argent

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native-profiler-start

Start native profiling on a booted iOS or Android device to capture CPU, hang, and memory data from a running app. Auto-detects the process and returns a trace file.

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.
malloc_stack_loggingNoiOS-only. When true, cold-launches the app under the profiler with Malloc Stack Logging enabled so memory leaks carry an allocation backtrace (responsible frame + library). Without it, leaks are still detected but unattributable — Instruments reports '<Call stack limit reached>'. Trade-offs: this RESTARTS the app (current state is lost), adds memory/CPU overhead, and makes the app noticeably slow to launch (every startup allocation records a backtrace), so leave it off for pure CPU/hang profiling. Requires a non-degraded Xcode: on Xcode 26.4 and later the cold-launch path is broken, so the call is rejected up front (re-run without the flag, or set ARGENT_IOS_CAPTURE=device to override if the device path works on your host). ARGENT_IOS_CAPTURE=all-processes — e.g. exported globally for the normal capture path — also rejects this flag up front, since that fallback cannot cold-launch; unset it (or set it to device) first. Ignored on Android.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv0.22.0
    • addedInput schema / properties / device_id / minLength
      Added value: +1
  2. Changed1 schema field changedv0.16.0
    • addedInput schema / properties / malloc_stack_logging
      Added value: +{
+  "description": "iOS-only. When true, cold-launches the app under the profiler with Malloc Stack Logging enabled so memory leaks carry an allocation backtrace (responsible frame + library). Without it, leaks are still detected but unattributable — Instruments reports '<Call stack limit reached>'. Trade-offs: this RESTARTS the app (current state is lost), adds memory/CPU overhead, and makes the app noticeably slow to launch (every startup allocation records a backtrace), so leave it off for pure CPU/hang profiling. Requires a non-degraded Xcode: on Xcode 26.4 and later the cold-launch path is broken, so the call is rejected up front (re-run without the flag, or set ARGENT_IOS_CAPTURE=device to override if the device path works on your host). ARGENT_IOS_CAPTURE=all-processes — e.g. exported globally for the normal capture path — also rejects this flag up front, since that fallback cannot cold-launch; unset it (or set it to device) first. Ignored on Android.",
+  "type": "boolean"
+}
  3. First observedv0.15.0

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly. It discloses auto-detection behavior, return shape ({ status, pid, traceFile }), failure conditions, platform-specific behavior, and detailed trade-offs for malloc_stack_logging including app restart, overhead, Xcode limitations, and environment variable interactions.

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?

The description is long but every sentence earns its place. It is well-structured with platform breakdown, usage flow, return value, failure modes, and parameter-specific caveats, making it dense but not wasteful.

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?

Given the tool's complexity (two platforms, four parameters, no output schema, no annotations), the description is remarkably complete. It covers return values, failure conditions, platform differences, and operational workflow, leaving no major gaps for an agent to misuse the tool.

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?

Although schema coverage is 100%, the description adds significant value beyond the schema: it explains when to provide app_process (only if auto-detection picks wrong app), that template_path defaults to a bundled Argent template and is ignored on Android, and gives extensive context for malloc_stack_logging including when to leave it off and when the call is rejected.

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 tool starts native profiling on a booted device, with platform-specific details (iOS Instruments via xctrace, Android Perfetto). It distinguishes itself from sibling tools like react-profiler-start and native-profiler-stop by specifying native CPU, hang, and memory capture.

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?

The description explicitly says 'Use when you want to capture native CPU, hang, and memory data for a running app' and instructs to call native-profiler-stop after user interaction. It does not explicitly contrast with react-profiler-start, but the native vs. React distinction is implied by tool naming and context.

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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