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react-profiler-stop

Stops the active CPU profiling session and collects CPU profile and React commit data. Returns a summary including duration, sample count, and fiber renders captured.

Instructions

Stop CPU profiling and collect the cpuProfile + React commit tree. Reads commit data from the in-app React DevTools backend. Stores results in the ReactProfilerSession for later use by react-profiler-analyze or react-profiler-cpu-summary. Call react-profiler-start first, then exercise the app, then call this. Returns { duration_ms, sample_count, fiber_renders_captured, total_react_commits, hot_commit_indices } summarizing the session. When any commit had fibers whose display name could not be resolved at stop time (typically transient components like modals/tooltips/animations that unmounted before stop), the response also includes { unattributed_ms, unattributed_fiber_count, unattributed_commit_count } — these quantify how much work is not accounted for in the per-component breakdown (the per-commit duration itself remains correct). Fails if no active profiling session exists or the CDP connection was lost during recording.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
portNoMetro server port
device_idYesDevice id from list-devices — the SAME id you passed to debugger-connect (iOS simulator UDID or Android serial).
Behavior5/5

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

With no annotations, the description fully discloses behavior: it reads from the in-app React DevTools backend, stores results, and details the return structure. It also explains edge cases with unattributed data when fibers unmount before stop.

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?

The description is a single paragraph but well-structured with logical flow: action, source, storage, sequence, return, edge case. Each sentence adds value without redundancy.

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?

For a tool with 2 parameters (100% schema coverage) and no output schema, the description comprehensively covers purpose, usage, return fields, and edge cases. It also positions the tool within the profiling workflow.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already documents parameters. However, the description adds context: port defaults to 8081, device_id must be the same as passed to debugger-connect. This enhances understanding beyond the schema.

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 it stops CPU profiling and collects the cpuProfile + React commit tree. It specifies the action (stop), resource (CPU profiling), and outcome (collect data). It distinguishes from siblings like native-profiler-stop by mentioning React-specific 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?

The description provides a clear usage sequence: call react-profiler-start first, then exercise the app, then call this. It also mentions failure conditions (no active session or CDP lost). It does not explicitly state when not to use, but the sequence is unambiguous.

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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