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Ludo AI Game Assets

by ai.ludo

createSpeechPreset

Convert text to speech using a named built-in preset voice, with optional emotion and language settings. Both text and voice_preset_id are required and the text is limited to 1000 characters; invalid input returns 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 want a ready-made catalog voice and do not need to supply your own sample; use createSpeech to clone a voice from an audio sample instead, and createVoice to design a new voice from a text description. 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 a voice preset

TDQS

A4/5.0
Behavior3/5

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

The description provides rich behavioral context: required fields, 1000-character limit, HTTP ۴00 on invalid input, async 202 response, polling instructions, credit consumption, concurrency limit, and 429 error. However, it contains an internal contradiction: 'there is no separate polling step' is followed by detailed instructions to poll getApiJob. This could confuse an agent about whether polling is necessary, despite the later clarification.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a clear one-sentence summary, but the overall text is long and contains redundancy such as repeating required-field information and the contradictory polling statement. The credit line at the end is useful but could be integrated more succinctly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the polling contradiction, the description covers nearly every important aspect for properly invoking the tool: auth, async behavior, polling method with wait parameter, result shape (single audio URL, result field), error codes, credits, concurrency limits, and idempotent request_id. For a complex async tool with no output schema and no annotations, this is quite complete.

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 schema covers 100% of parameters, so baseline is 3. The description adds some value by emphasizing that text and voice_predict_id are required, reinforcing the 100-character limit, and explaining request_id as a tracking/tagging mechanism. It does not significantly extend parameter meaning beyond 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: 'Convert text to speech using a named built-in preset voice', which clearly distinguishes it from the sibling createSpeech (voice cloning from sample) and createVoice (design from text). It also names accepted optional dimensions (emotion, language) without ambiguity.

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: 'Use this when you want a ready-made catalog voice and do not need to supply your own sample', and names the alternatives with their distinct purposes. It also mentions API key requirement, concurrency limits, and the 429 condition, which are essential usage constraints.

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