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Ludo AI Game Assets

by ai.ludo

animate3DModelPreset

Apply a curated animation preset to an already-rigged 3D model (retargeting). Pass the rigged GLB in model (URL or base64) and a preset_id from the animation presets list (see the spritesheet animation-presets endpoint) - only presets that expose a clip_url can be applied to a 3D model. The model must have a humanoid-template rig (rig it with rig_type humanoid_template or humanoid_template_hands). The job result is one retargeted animation - a standalone animation-only GLB in glb_url plus an mp4 preview_url - in the same animations envelope as the animate endpoint. crop_loop trims the clip to its seamlessly-looping span (omit to follow the preset's own loop flag); in_place removes net travel so the character moves on the spot, as game-engine locomotion expects (omit to follow crop_loop). Credits are charged only on success. Requires an API key (user scope). Returns 202 with a job id immediately; poll getApiJob (pass wait: 30) until status is succeeded, then read its result field, which is exactly the response documented for this operation. Each account may have up to 50 generations queued or running at once; beyond that submissions return 429 (PENDING_JOBS_LIMIT) - wait for jobs to finish.

Credits: This endpoint consumes 0.2 credits per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestBodyYes

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and delivers: async 202 behavior, polling flow, result shape (glb_url and preview_url), credits charged only on success, API-key scope, queue limit of 50, and 429 pending-jobs error. This is far beyond a minimal disclosure.

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 dense but every sentence earns its place: purpose, prerequisites, output envelope, flag behavior, async contract, rate limiting, and credits. It is front-loaded with the primary action and adds detail in a logical order without 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?

Given the complexity, absence of annotations, and absence of an output schema, the description is complete: it specifies inputs, rig prerequisites, preset constraints, result fields, polling instructions, error conditions, rate limits, and auth requirements. Nothing needed to call the tool correctly is omitted.

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

Parameters5/5

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

The context reports 0% schema description coverage, so the description must compensate for `model`, `preset_id`, `crop_loop`, and `in_place`. It does so with concrete details: valid rig types, clip_url restriction, crop_loop/in_place omission semantics, and game-engine locomotion rationale. It also clarifies that request_id offers idempotent re-use via the existing job.

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 first sentence states a specific verb ('Apply'), a specific resource ('curated animation preset'), and target ('already-rigged 3D model'), with 'retargeting' clarifying the operation. This clearly distinguishes it from generic animate3DModel and names the preset source, so the agent can select it correctly.

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 gives concrete when-to-use guidance: supply an already-rigged GLB, use a preset_id from the presets list, and satisfy the humanoid-template rig requirement. It also explains polling via getApiJob. It does not explicitly name an alternative for non-preset animation, though 'same ... as the animate endpoint' gestures toward the related tool.

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

A4/5.0
Disambiguation4/5

Most tools pair a clear action and asset type (create3DModel, editVideo, removeBackground), and overlapping pairs such as animateSprite vs transferMotion vs animateSpriteKeyframes are carefully differentiated by input mode. The main friction is listApiJobs vs listGenerations, which both return generation history from slightly different scopes.

Naming Consistency4/5

The set is overwhelmingly consistent camelCase verb+noun (create*, edit*, list*, animate*, cancel*), with only minor deviations like generatePose/generateWithStyle alongside createImage and the slightly awkward validateApiKeyEndpoint. There is no chaotic mixing of conventions.

Tool Count2/5

At 31 tools this exceeds the 25+ threshold for 'too many', even though the multimodal game-asset scope explains much of the breadth. Agents face a large selection surface with many generation variants across 3D, sprites, images, audio, and video.

Completeness4/5

Core workflows are covered: image-to-3D plus rigging and animation, sprite pose/rotation/animation/editing, image create/edit/style/background-removal, video create/edit/upscale, and audio SFX/ambiance/music/voice. Minor gaps remain, such as no image upscaler and no individual asset retrieval or deletion.

Resources