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Get operator runtime state

get_node_state_runtime
Read-only

Inspect runtime telemetry for a single TouchDesigner operator, including cook time, count, resolution, GPU memory, and errors, to diagnose per-node performance issues.

Instructions

Read-only: inspect a single operator's runtime telemetry — cook time, cook count, last-cook frame, resolution (TOPs), channel/sample counts (CHOPs), GPU memory usage, and cook errors. Complements get_td_performance (which aggregates cook times across a network) by providing deep per-op detail for the 'why is it black / why is it slow' diagnostic loop. Returns {path, type, family, cook_time_ms, cook_count, last_cook_frame, resolution, num_chans, num_samples, gpu_memory, errors[], warnings[], extra}. Attribute names are flagged UNVERIFIED and vary by TD build; the extra map records which attrs were actually present for live confirmation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesFull path of the operator to inspect (e.g. '/project1/noise1').

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesEchoed operator path.
typeYesOperator type string (e.g. 'noiseTOP').
familyNoOperator family: TOP, CHOP, SOP, DAT, COMP, MAT, etc.
cook_time_msNoLast cook duration in milliseconds (op.cookTime * 1000). UNVERIFIED attr name.
cook_countNoTotal number of times the op has cooked (op.totalCooks / op.cookCount). UNVERIFIED.
last_cook_frameNoAbsolute frame number of the last cook (op.cookAbsFrame). UNVERIFIED attr name.
resolutionNo[width, height] for TOPs (op.width, op.height). UNVERIFIED.
num_chansNoNumber of channels for CHOPs (op.numChans). UNVERIFIED.
num_samplesNoNumber of samples per channel for CHOPs (op.numSamples). UNVERIFIED.
gpu_memoryNoGPU memory used in bytes for TOPs (op.gpuMemory). UNVERIFIED attr name.
errorsYesCook errors from op.errors(recurse=False).
warningsYesBridge-level warnings about unreadable attributes.
extraNoAdditional Info attributes found via getattr probing — allows live-validation to confirm real attr names.
Behavior5/5

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

Description notes the read-only nature (consistent with annotations) and adds important behavioral context: attribute names are UNVERIFIED and vary by build, with the extra map providing confirmation. This goes beyond annotations.

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?

Description is concise yet comprehensive, covering purpose, usage, return fields, and a notable caveat in a single well-structured paragraph. No wasted words.

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 simple parameter (single required path), presence of output schema, and annotations covering read-only and non-destructive behavior, the description provides complete context including diagnostic use case and attribute name variability.

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 a clear description of the 'path' parameter. The description adds minimal extra meaning beyond the schema (e.g., example path), so 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?

Description clearly states it inspects a single operator's runtime telemetry, listing specific data fields. It also distinguishes itself from the sibling tool get_td_performance by noting it provides per-op detail for diagnostics.

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?

Description explicitly states when to use this tool ('why is it black/why is it slow diagnostic loop') and contrasts it with get_td_performance for aggregated data, offering clear usage guidance.

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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