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Get network performance

get_td_performance
Read-only

Measure cook times in a TouchDesigner network, flag nodes exceeding the target frame budget, and return performance metrics including slowest nodes and total cook time.

Instructions

Read-only: report cook times under a network (recursively by default, slowest node first) and warn about nodes that exceed the frame budget. Returns {targetFps, frameBudgetMs, totalCookMs, nodes[], warnings[]} and changes nothing. Use this to just measure; use optimize_performance when you want suggestions and the option to auto-shrink the slow TOPs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
root_pathNoNetwork root to measure cook times under./project1
target_fpsNoFrame-rate target used to flag slow nodes.
recursiveNoMeasure every descendant (true, default) so cook time inside generated containers is counted, not just the root's direct children.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesThe network root that was measured, echoing the request.
targetFpsYesThe frame-rate target used to derive the per-frame budget.
frameBudgetMsYesMilliseconds available per frame at the target FPS (1000 / targetFps).
totalCookMsYesSum of the measured nodes' last cook times, in milliseconds.
nodesYesPer-node cook times, slowest first.
warningsYesBudget warnings: one line per node whose cook time exceeds the frame budget, plus a final aggregate line when the summed total cook time exceeds the budget. Empty when everything is within budget.
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false; the description reinforces this by saying 'changes nothing' and listing the exact return structure. It adds detail beyond annotations without contradiction.

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, front-loaded with the core function and return shape, then a clear alternative. Every sentence is useful with no 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?

Given the output schema and well-documented input schema, the description sufficiently covers purpose, output structure, and sibling differentiation. No missing information for correct invocation.

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?

The input schema has 100% coverage, so the baseline is high. The description adds context implying the recursive default (default is true) and slowest-first ordering, which are not in the schema. This adds value though not fully necessary.

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 explicitly states the tool reports cook times and warns about budget-exceeding nodes, and distinguishes itself from 'optimize_performance' by specifying it's read-only. This provides a clear, specific verb-resource-scope pairing.

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?

The description directly contrasts this tool with 'optimize_performance', stating when to use each: 'Use this to just measure; use optimize_performance when you want suggestions...' This gives precise usage 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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