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Measure animation motion

spine_measure
Read-only

Samples a Spine animation to measure bone rotation ranges, motion paths, peak speeds, pops, holds, loop seams, root-motion travel and foot sliding, and slot travel for animation QA.

Instructions

Samples an animation in spine-core (constraints, physics warmed up); per bone: rotation range, path, peak speeds, pops, holds, loop seam. rootMotion: what SkeletonRootMotion reads (travel, sliding feet, in-place speed). parts: how far each slot travels.

Input Schema

NameRequiredDescriptionDefault
fpsNo
skinNo
bonesNo
inputYes.spine, .json or .skel(.bytes)
limitNoMost-moving bones shown (default 12)
partsNoScale: px per unit
skeletonNoNeeded only with several skeletons
animationYes
rootMotionNo
warmupLoopsNo
includeSamplesNoPer-frame world x/y/rotation

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.70.1

TDQS

B3/5.0
Behavior3/5

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

readOnlyHint=true already tells the agent this is a safe read. The description does add real value beyond the annotation by disclosing that constraints are solved and physics are warmed up before sampling, which affects result fidelity. It says nothing about cost, runtime, or limits on large animations, so the behavioral picture is partial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tightly packed sentences, results front-loaded by output category, with no filler. The telegraphic semicolon style is dense but every clause carries information, so the compression is justified.

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

Completeness3/5

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

For an 11-parameter tool with a nested object and no output schema, the description outlines the returned measurement categories well, which is valuable. However, with no output schema and many undocumented parameters, an agent still lacks the detail needed to pick fps, bones, warmupLoops, or interpret the result structure precisely.

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 only 45% with 11 parameters, so the description needs to compensate, but it does not explain fps, bones, skin, animation, warmupLoops, or the top-level rootMotion object. Its 'rootMotion:' and 'parts:' clauses read as output-section labels rather than parameter guidance, and the closest thing to parameter meaning (rootMotionBoneName) is already in the schema.

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?

States a specific verb and resource ('samples an animation in spine-core') and enumerates the measurements produced: rotation range, path, peak speeds, pops, holds, loop seam, rootMotion travel and foot sliding. This clearly separates it from sibling analysis tools like spine_compare_animations or spine_motion_visual, though it never names those siblings explicitly.

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 when-to-use or when-not-to-use guidance, and no routing to alternatives such as spine_compare_animations, spine_motion_visual, or spine_timing. The agent must infer from the output list that this is a diagnostic measurement tool, which is only implied usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.