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Ludo AI Game Assets

by ai.ludo

createSpeech

Convert text to speech by cloning the voice from an audio sample you provide (voice-cloning text-to-speech). Both text and sample are required; the text is limited to 1000 characters and the sample is supplied as a URL or base64 audio that must be at most 15MB, with violations returning HTTP 400. The job result is a single audio result containing a URL; there is no separate polling step. Credits are charged on success. Use this when you have a reference voice sample to clone; use createSpeechPreset to speak with a built-in named preset voice instead, and createVoice to design a brand-new voice from a text description rather than cloning one. 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 1 credits per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestBodyYesPayload for text-to-speech generation using voice cloning

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does so extensively: it covers required inputs, size limits, 400 errors, auth scope, credit cost, 202 job responses, getApiJob polling, result shape, and 429 rate limiting. The only weakness is the internally confusing statement 'there is no separate polling step' followed later by instructions to poll getApiJob.

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 dense and front-loaded: it opens with purpose, then constraints, then usage routing, then async behavior. It earns most of its length, but the redundant credits sentence and the contradictory 'no separate polling step' line keep it from full marks.

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 is remarkably complete: it explains the async 202/getApiJob flow, the result field, the single audio URL output, auth requirements, credit consumption, and the 50-job queue cap. Nothing essential for calling the tool correctly is missing.

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

Parameters4/5

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

The input schema already covers text, sample, and request_id well, so the baseline is 3. The description adds meaningful constraints beyond the schema, especially the 15MB sample size cap, the HTTP 400 violation behavior, and how request_id can be used to locate results via GET /assets/audio/results.

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: 'Convert text to speech by cloning the voice from an audio sample you provide.' It further distinguishes itself from createSpeechPreset and createVoice, making the tool's unique role 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?

Explicit usage guidance is present: 'Use this when you have a reference voice sample to clone; use createSpeechPreset to speak with a built-in named preset voice instead, and createVoice to design a brand-new voice from a text description rather than cloning one.' This clearly tells an agent when to choose this tool versus alternatives.

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