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Ludo AI Game Assets

by ai.ludo

listGenerations

List your generation history across the API and the web app - filter by type, source (api|web|all), text search, and date range. Returns {items, page, page_size, has_more}; keep paging while has_more is true. API-originated results expire after 7 days - download anything you want to retain. For an in-flight job's status use getApiJob instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesWhich kind of generation to list
searchNoFree-text search. Every whitespace-separated term must match (case-insensitive substring) the item's tags or one of its text fields (prompt, hints, style, label, ...), so "dwarf axe" narrows to items matching both.
sourceNoWhich surface the items were generated from: api (your API/MCP generations, last 7 days only), web (your Ludo web studio generations, no time limit), or all (default, both)all
date_toNoOnly return items generated at or before this time (unix seconds)
date_fromNoOnly return items generated at or after this time (unix seconds)
page_sizeNoItems per page (1-100)
request_idNoOnly return items tagged with this request_id when you generated them
page_numberNo1-based page number

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the load and exceeds expectations: it reveals the paging contract (items/page/page_size/has_more, keep paging while has_more), the 7-day expiry of API-originated results, and the advised alternative for status. These are non-obvious behavioral facts an agent needs.

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

Conciseness5/5

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

Two sentences pack purpose, filters, return shape, paging behavior, retention policy, and routing to getApiJob with no wasted words. The key purpose is front-loaded and each sentence earms its place.

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, the description usefully specifies the return object and how to paging. It also covers the important retention caveat and provides enough context for an agent to invoke the tool correctly alongside the fully-described input schema.

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%, so each parameter is already documented in the schema. The description only summarizes filter dimensions (type, source, text search, date range) without adding extra meaning beyond the schema, meritng the baseline 3.

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 clearly states a specific verb ('List') and resource ('your generation history across the API and the web app'), and immediately enumerates the filtering dimensions. It also differentiates from a sibling by routing in-flight status lookups to getApiJob, so an agent can distinguish this tool without opening schemas.

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 gives concrete usge context: liste history across API/web, filter, and paging. It also explicitly says when not to use it ('For an in-flight job's status') and names the alternative (getApiJob), making the choice clear.

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