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Profile cook cost

profile_cook_cost
Read-only

Diagnose intermittent stalls by sampling cook times over a window and ranking hotspot nodes by p95 cook time.

Instructions

Read-only: sample cook times over a window (N samples × intervalMs) and rank hotspot nodes by p95 cook time. Use this to diagnose intermittent stalls that a single get_td_performance snapshot misses. Returns {path, samples, intervalMs, targetFps, frameBudgetMs, windowMs, hotspots[], warnings[]}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopePathNoNetwork root to profile (recursive)./project1
samplesNoHow many snapshots to take across the window.
intervalMsNoDelay between snapshots in milliseconds (>= one frame at 60fps).
topNNoHow many hotspots to return, ranked desc by p95.
targetFpsNoForwarded to get_td_performance for the per-frame budget annotation.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
samplesYes
intervalMsYes
targetFpsYes
frameBudgetMsYes
windowMsYes
hotspotsYes
warningsYes
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral details: it samples over a window, ranks by p95, and returns a structured object with hotspots and warnings. This goes beyond annotations without contradicting them.

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?

Two sentences: first states what it does and how, second gives usage guidance and the return structure. Every word carries weight; no fluff. Well 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?

Given that annotations exist, schema covers all parameters, and the description explicitly lists the return fields (enough to understand the output), the description is fully complete for the tool's complexity. No missing critical information.

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 100% with detailed parameter descriptions. The description mentions 'N samples × intervalMs' which aligns with the samples and intervalMs parameters, but adds no new semantic meaning beyond what the schema provides. Baseline 3 is appropriate.

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 uses specific verbs ('profile', 'rank') and clearly identifies the resource (cook times over a window, hotspot nodes) with p95 ranking. It explicitly contrasts with the sibling tool get_td_performance, making the purpose unmistakable.

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 second sentence directly advises when to use this tool: 'diagnose intermittent stalls that a single get_td_performance snapshot misses.' It names the alternative tool, providing clear context. However, it does not explicitly state when NOT to use it, leaving a minor gap.

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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