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Ludo AI Game Assets

by ai.ludo

createAudioTransform

Remix an existing audio sample (a sound effect, ambiance, or music clip) into a variation guided by a text prompt, for example turning a track into an 80s synthwave or metal version. Both the sample and the prompt are required; the sample is uploaded as a URL or base64 audio and must be at most 15MB or the call returns HTTP 400, and duration must be one of the allowed values (0 means match the source, otherwise multiples of 10 up to 180 seconds). The job result is a single audio result containing a URL; there is no separate polling step. The optional modification_strength (0 to 1, default 0.5) controls how far the result departs from the original. Credits are charged on success. Use this to transform existing audio you already have; use createSoundEffect, createAmbiance, or createMusic to generate audio from scratch. 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 3 credits per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestBodyYesPayload for remixing an audio sample (sound effect, ambiance or music) into a variation guided by a text prompt

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly: it discloses 202 + job id async behavior, polling via getApiJob, 15MB limit with 400 error, credit charging on success, concurrency limit with 429, and request_id idempotency. This goes well beyond what structured fields would reveal.

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 deliberately front-loaded with the core purpose and then flows into constraints, usage alternatives, async behavior, and limits. Nearly every sentence carries actionable information, though a few operational details could be tightened.

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 no output schema and no annotations, this description provides virtually everything an agent needs: auth scope, success/error semantics, job polling instructions, result extraction, idempotency, credit cost, and concurrency limits. No critical operational gap prevents correct invocation.

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?

Input schema coverage is 100% and the schema already provides strong descriptions for all parameters. The description reinforces key constraints such as sample size, required fields, and modification_strength range, but mostly repeats rather than substantially extends the parameter-level semantics already present in the schema.

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 opens with a specific verb and resource ('Remix an existing audio sample ... into a variation guided by a text prompt') and explicitly contrasts itself with sibling tools for generating audio from scratch. This makes the tool's identity and scope unmistakable.

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?

It gives explicit when-to-use guidance ('Use this to transform existing audio you already have') and names concrete alternatives (createSoundEffect, createAmbiance, createMusic) for the opposite case. It also covers prerequisites like API key scope and the polling path via getApiJob.

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.

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