spriteship

Select animation frames

select_frames

FREE, no AI. Curate which frames an animation keeps, by index into the full capture, in order. Pass an empty array to reset to all frames. Non-destructive: the full capture is retained, so you can re-select at any time. Use get_frames with format:"sheet" first to choose indices. Returns a jobId — poll get_job.

Input Schema

TableJSON Schema

Name	Required	Description
`frames`	Yes	Ordered frame indices to keep. Empty array resets to the full capture.
`pairId`	No	Motion pair id — required when target=assetPair.
`target`	Yes	A character animation, or an asset motion pair.
`entityId`	Yes	Character id (target=character) or asset id/slug (target=assetPair).
`animation`	No	Animation slug — required when target=character.
`compositing`	No	Characters only — apply scale/offset in the same rebuild.

TDQS

A4.5/5.0

Behavior5/5

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

Beyond annotations (which only indicate non-read-only, non-destructive, open-world), the description adds valuable behavioral detail: the full capture is retained (non-destructive), re-selection is possible, empty array resets, and it returns a jobId requiring polling via get_job. This gives the agent a clear mental model of the tool's behavior.

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 five concise sentences, each providing distinct value: cost/aspect, core purpose, reset behavior, non-destructive guarantee, prerequisite workflow, and async pattern. It is front-loaded with the most critical information and contains no filler.

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?

Despite having 6 parameters and no output schema, the description covers the essential workflow (get_frames first), the async job pattern, reset behavior, and non-destructive nature. The target variations are handled by the schema, so the description is complete enough for an agent to invoke the tool correctly.

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 description coverage is 100%, and the schema already documents the key param semantics (ordered frames, empty array reset, target types, etc.). The tool description repeats some of this but adds no new meaning beyond the schema, so the baseline of 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?

The description uses a specific verb ('Curate') with a clear resource ('which frames an animation keeps'), and specifies the mechanism ('by index into the full capture, in order'). This clearly distinguishes it from sibling tools like frame_animation, which likely deals with creating/editing animation frames rather than selecting subsets.

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 provides explicit usage context: 'Use get_frames with format:"sheet" first to choose indices' and explains the reset behavior via empty array. It also notes the non-destructive nature enabling re-selection. However, it does not explicitly name alternative tools or state when NOT to use this tool, so it falls just short of a 5.

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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