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Ludo AI Game Assets

by ai.ludo

createSoundEffect

Produce a short sound effect (SFX) from a text description, such as "laser gun firing" or "footsteps on gravel". The job result is a single audio result containing a URL; there is no separate polling step. The description field is required, duration is capped at 10 seconds (0 means auto-pick based on the description), and you may set loop to true for a seamlessly looping effect. Credits are charged on success. Use this for short, discrete sounds; use createAmbiance for a continuous looping background soundscape, createMusic for musical pieces, and createAudioTransform to remix an existing audio sample. Pass an optional request_id to tag the result so you can locate it later via GET /assets/audio/results. 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 2 credits per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestBodyYesPayload for generating a sound effect from a text description

TDQS

A4.6/5.0
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 does so comprehensively. It discloses the async job behavior, credit cost on success, API key requirement, the 50-job concurrency limit, and the exact polling pattern. It also explains the result is a single audio URL. The phrase 'no separate polling step' is slightly confusing given the later polling instruction, but the explicit steps are unambiguous.

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 long but densely packed with necessary operational details for a function with an async job lifecycle. It is front-loaded with the core purpose and alternatives. The 'no separate polling step' sentence is slightly redundant/confusing alongside the explicit polling instructions, preventing a perfect score.

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 description covers required input, optional parameters, async polling, rate limits, auth, credits, and result-handling. No output schema exists, but the description tells the agent exactly where to find the result. For an agent to correctly invoke and monitor this tool, no critical context is missing.

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%, so the schema already documents all parameters. The description adds minimal new meaning beyond the schema, mostly restating duration zero means auto-pick and loop for seamless looping. The request_id tagging is already described in the schema. Thus a baseline 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 states a specific verb and resource: 'Produce a short sound effect (SFX) from a text description'. It clearly distinguishes the tool from siblings by naming createAmbiance, createMusic, and createAudioTransform. An agent can immediately understand what this tool does and how it differs.

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 explicitly states when to use this tool ('short, discrete sounds') and names alternative tools for other scenarios (createAmbiance, createMusic, createAudioTransform). It also provides the full async workflow: call, poll getApiJob with wait:30, and read result. Rate limits and retry-on-429 behavior are clearly specified.

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