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move_create_volume_with_human

Calibrate a multicam motion capture setup by creating a volume from footage of a person of known height, providing the foundation for accurate tracking.

Instructions

Calibrate a multicam setup from footage of a person of known height.

This is step one of the multicam workflow and consumes processing credits. Poll move_get_volume until state is FINISHED, then create a multicam take with the same device_labels and lenses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
expandNo
sourcesYesOne entry per camera, each with the lens in camera_settings. Footage should show a single person moving through the capture area.
metadataNo
area_typeNoNORMAL for capture areas under 20 m², LARGE for bigger areas.NORMAL
clip_windowNo
sync_methodNoClap window or timecode sync across cameras.
human_heightYesHeight of the person in the calibration footage, in metres.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Install Server

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the annotations, the description discloses that this operation consumes processing credits and is asynchronous, requiring polling of move_get_volume. This adds meaningful behavioral context about cost and expected follow-up that annotations do not convey.

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 compact and front-loads the core purpose in the first sentence. Every subsequent sentence adds critical workflow information: credit consumption, polling, and the required follow-up take creation. No filler.

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?

For an 8-parameter tool with an output schema, the description sufficiently orients the agent: it explains the async lifecycle, cost implication, and how this step connects to the multicam workflow. It could mention sync_method or area_type constraints, but those are already documented in the schema.

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?

The description adds little parameter-level detail, but the schema already documents key parameters like human_height, sources, camera_settings, area_type, and sync_method. It reinforces that device_labels and lenses must stay consistent, which helps across the workflow, but leaves some params like name, expand, metadata, and clip_window to the schema alone.

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 states a specific verb and resource: 'Calibrate a multicam setup from footage of a person of known height.' It also identifies the tool as step one of the multicam workflow, which clearly separates it from sibling tools like move_create_multi_cam_take.

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 gives clear workflow context: use this first, then poll move_get_volume until FINISHED, then create a multicam take with matching device_labels and lenses. It does not explicitly mention alternatives or when not to use it, but the sequential guidance is strong enough to route an agent.

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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