spriteship

Fix an animation's edge artifacts (re-matte)

rematte_animation

FREE, no AI. Try a cleaner background removal on ONE animation's source video — the repair for a quality read showing deliveredIssues containing "matte" (green fringe, flickering holes, ragged edges). The cleaner version is kept ONLY if it measures better, so this can never make the animation worse; the motion itself is unchanged. One attempt per capture: if the same frames were already tried (matteRepair stamp), a repeat call is a cheap re-check + free re-render — regenerate the animation for a fresh take instead. Fails with SOURCE_VIDEO_EXPIRED when the source video aged past its 30-day retention. Returns { jobId } — poll get_job, then re-read the character's quality block: "matte" gone = the cleaner version shipped.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`animation`	Yes	Animation slug to re-matte in place.
`characterId`	Yes	Character id.

TDQS

A4.6/5.0

Behavior5/5

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

Annotations indicate readOnly=false, destructiveHint=false, idempotentHint=false, which are consistent with the description's statement that the cleaner version is kept only if it measures better, and that it can never make the animation worse. The description goes beyond annotations by detailing the matteRepair stamp semantics, the conditional keep, the unchanged motion, the async pattern (returns { jobId } and poll get_job), and the source video expiration failure. This rich context is not redundant with annotations and enhances transparency. No contradiction.

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?

The description is concise yet packed with valuable details: purpose, safety guarantee, retry behavior, failure mode, and return usage. It is front-loaded with the main action and condition. The opening 'FREE, no AI' may be slightly tangential but adds context about cost/technology. Overall, each sentence earns its place, though the density could be overwhelming for a quick scan.

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?

Given that there is no output schema, the description compensates by clearly stating the return shape ({ jobId }) and the subsequent steps (poll get_job, re-read quality block). It also covers prerequisites (deliveredIssues containing 'matte'), the non-destructive conditional behavior, retry semantics, and the SOURCE_VIDEO_EXPIRED failure mode. This completes the tool's context for an async mutation operation, making it highly informative.

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 input schema already provides 100% coverage for both parameters, with descriptions for 'animation' (Animation slug to re-matte in place) and 'characterId' (Character id). The tool description does not add significant parameter-specific meaning; it only mentions 'ONE animation's source video', which aligns with the existing schema. Given the high schema coverage, the baseline of 3 is appropriate, with no need for the description to compensate.

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 clearly states the tool fixes edge artifacts (matte) on one animation's source video, specifically as a repair for quality read issues containing 'matte'. It distinguishes itself by targeting animation-specific quality defects and explicitly mentions the resource (animation) and the action (re-matte). This provides a specific verb+resource combination that sets it apart from sibling tools.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use guidance: 'the repair for a quality read showing deliveredIssues containing "matte"' with examples (green fringe, flickering holes, ragged edges). It also provides an exclusion: if the same frames were already tried (matteRepair stamp), regenerate the animation instead. It further mentions the FREE and no-AI nature, as well as the SOURCE_VIDEO_EXPIRED failure, giving clear context for decision-making.

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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