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Ludo AI Game Assets

by ai.ludo

editImage

Modify an existing image according to text instructions: supply a source image (URL or base64) and a prompt describing the changes (e.g. "add clouds", "warmer color scheme"), with an optional reference_image for extra style or content guidance. The job result is an array of image results, each with a url; request n (1-4) to control the number of edited variations. Provided images are uploaded and validated, and any image larger than 15MB is rejected with HTTP 400. Credits are charged only on success, scaled to the number of images produced. Use editImage to transform a specific existing image; use createImage to generate from text alone, generateWithStyle to borrow a reference's art style, and removeBackground for the dedicated background-removal case. Pass an optional request_id to tag the results so you can retrieve them later via GET /assets/images/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 0.5 credits per result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestBodyYesPayload for editing an existing image based on text instructions

TDQS

A4.6/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It details validation (15MB rejection), credits only on success, API key requirement, 202 async response, polling pattern, and 429 rate limit. This is comprehensive and transparent.

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

Conciseness2/5

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

The description is verbose and repetitive, restating the same information (e.g., credit charging, job results, polling) multiple times in a single wall of text. Lacks clear sectioning or bullet points, reducing readability.

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?

Covers all essential operational details: purpose, async behavior, idempotency, error scenarios, rate limiting, and cost. Nothing a caller needs to know is omitted.

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?

Schema coverage is 100% with clear descriptions for each parameter. The tool description adds context like credit cost and error behavior, but does not significantly expand on parameter meaning beyond the schema examples.

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?

States a specific verb ('Modify') and resource ('existing image'), clearly distinguishes from siblings by explicitly contrasting with createImage, generateWithStyle, and removeBackground. The purpose is unambiguous.

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?

Provides explicit guidance on when to use and when to avoid: directs to editImage for transforming an existing image and to alternatives for other scenarios. Also covers async polling, request_id idempotency, and rate limits.

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