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Optimize performance

optimize_performance

Identify cook-time bottlenecks in a network and optionally reduce resolution of flagged TOPs to improve performance.

Instructions

Scan a network for cook-time bottlenecks and (optionally) fix them. By default it reports the slowest nodes with a concrete suggestion each. With apply:true it lowers the resolution of the flagged TOPs by scale to claw back GPU time. Run get_td_performance first if you just want the numbers; use this to act on them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoNetwork to analyze (recursively)./project1
threshold_msNoFlag nodes whose last cook took at least this many milliseconds.
applyNoIf true, actually lower the resolution of the flagged TOPs by `scale`. Default false = just report the bottlenecks and suggestions.
scaleNo(apply) Resolution multiplier for flagged TOPs (0.5 = half on each axis).
Behavior1/5

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

While the description adds context about default reporting and optional resolution lowering, it contradicts the annotation 'destructiveHint: false' by describing a destructive action (lowering resolution). This inconsistency undermines trust.

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 concise at three sentences, with the main action in the first sentence. Every sentence is informative, and there is no redundant or vague language.

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?

Given no output schema, the description adequately covers the tool's behavior (default report, optional modification) and references an alternative tool. It lacks details on the format of the suggestions but is sufficient for a performance analysis tool.

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 baseline is 3. The description adds value by explaining the interplay between 'apply' and 'scale' parameters, clarifying how the tool modifies TOPs when apply is true. This goes beyond the schema descriptions.

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's purpose: scan a network for cook-time bottlenecks and optionally fix them. It uses a specific verb-resource combination and distinguishes itself from the sibling 'get_td_performance' by contrasting reporting vs acting.

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

Usage Guidelines5/5

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

Explicit guidance is provided: 'Run get_td_performance first if you just want the numbers; use this to act on them.' This clearly indicates when to use which tool, and the description explains the default behavior (report only) vs applying fixes.

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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