spriteship

Split an animation into game phases

phase_split

FREE, no AI. Carve ONE multi-state clip into separate game-phase animations by frame windows — the classic case is a jump becoming crouch/rise/fall/land, with fall looping while airborne and the rest playing once. Each phase becomes a real exportable animation; the source animation is untouched. Jump-family animations often carry a ready-made suggestion: get_character → animations[].phaseProposal — pass its phases through verbatim. Frame windows index the FULL capture. Returns { jobId, created } — poll get_job.

Input Schema

TableJSON Schema

Name	Required	Description
`phases`	Yes	2-6 phase windows. animations[<anim>].phaseProposal from get_character usually supplies these verbatim.
`animation`	Yes	Source animation slug (e.g. "jump").
`characterId`	Yes	Character id.

TDQS

A4.7/5.0

Behavior5/5

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

Annotations indicate non-destructive (destructiveHint=false) but description adds crucial behavior: 'the source animation is untouched,' 'FREE, no AI,' and async returns via job polling. It also discloses that frame windows index the FULL capture, which is an important caveat for correct use.

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?

Every sentence delivers value: purpose, classic example, proposal hint, indexing note, and return contract. It is front-loaded with the core action and remains compact despite covering multiple important nuances.

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?

The tool is async (returns jobId), has a non-trivial input (phase windows), and relies on external context (phaseProposal). The description covers all of this: how to get proposals, indexing semantics, output shape, and polling guidance. No significant gaps remain.

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?

Schema covers 100% of parameters, so baseline is 3. The description adds meaningful semantics beyond schema: clarifies that frame windows are absolute indices into the FULL capture, and suggests phaseProposal as a verbatim source for the phases array. This elevates the score.

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+resource: 'Carve ONE multi-state clip into separate game-phase animations by frame windows.' It gives a concrete example (jump → crouch/rise/fall/land) and distinguishes this from other animation tools by emphasizing it splits one clip into multiple exportable animations.

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 clearly explains when to use this tool (multi-state clips needing game-phase separation) and provides the classic jump example. It even guides users to leverage phaseProposal from get_character. However, it doesn't explicitly mention alternatives or when not to use it, leaving room for minor ambiguity.

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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TDQS

A3.9/5.0

Disambiguation4/5

Most tools have clearly distinct purposes, and the descriptions are extremely detailed with cross-references (e.g., animate_asset vs frame_animation vs generate_character_animation). A few pairs like reprocess_asset vs revise_asset could be confused initially, but their descriptions and use cases are explicit enough to prevent misselection.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., create_project, get_asset, cancel_job). Verbs are imperative and nouns are appropriately singular/plural, making the API predictable and readable.

Tool Count2/5

At 41 tools, the server is far beyond the 15-25 range considered reasonable for most APIs. While the domain is broad (project, assets, characters, animations, jobs, exports, credits), the sheer number creates a heavy surface that may overwhelm agents and suggests the API could be consolidated into higher-level operations.

Completeness4/5

The tool set covers the full creative pipeline: project creation, asset/character generation, animation (both AI and frame-based), revisions, exports, and job management. Minor gaps include lack of delete operations for assets/characters/projects and no listing of all jobs, but these are not critical for the core workflow and are likely intentional for a generative art platform.

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