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gen_uthana_create_locomotion

Generate Uthana locomotion for a character using travel angle, move speed, and stride count. Specify style and confirm usage to create the motion.

Instructions

Generate Uthana locomotion for a character using travel angle, speed, and stride count.

KB: see knowledge_base/31_GENERATIVE_CONTENT_PIPELINE.md#uthana-motion-task-family Example: gen_uthana_create_locomotion(character_id="character-id", travel_angle=45, confirm_usage=True)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stridesNo
style_idNo
timeout_sNo
move_speedNo
character_idNo
session_nameNodefault
travel_angleNo
confirm_usageNo
estimated_secondsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention whether this is an asynchronous job, whether it consumes credits, whether the user must confirm usage, or whether returned output must be polled via gen_uthana_get_job. The confirm_usage parameter appears in the example but its meaning is never explained.

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-loaded with the core purpose. The KB reference and example are useful additions without fluff. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 9-parameter generative tool with no annotations, this description is too thin. It lacks pipeline context such as job polling, download/import follow-ups, confirmation implications, and return behavior. An agent could attempt a basic call but would not know how to handle the result or what side effects to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It maps natural-language concepts to some parameters: travel angle, speed, and stride count correspond to travel_angle, move_speed, and strides. However, it leaves style_id, session_name, timeout_s, confirm_usage, and estimated_seconds unexplained, and the example only shows character_id, travel_angle, and confirm_usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action and resource: 'Generate Uthana locomotion for a character using travel angle, speed, and stride count.' This clearly identifies what the tool does. However, it does not explicitly distinguish itself from sibling tools like gen_uthana_text_to_motion or gen_uthana_video_to_motion, relying mostly on the tool name and parameter set for differentiation.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives, nor any prerequisites such as creating a character first. The KB reference is a pointer but not actual usage guidance, and the example shows a call but does not explain selection criteria or required setup.

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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