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Ludo AI Game Assets

by ai.ludo

editVideo

Edit a previously generated video with a text prompt and optional reference images (video-to-video). Pass the video url you received from createVideo, createVideoFromReferences, or an earlier edit - it must be a video you generated within the last 7 days; arbitrary external videos are not accepted. Optionally add up to 5 reference images (URL or base64) to guide the edit. The job result is the new video URL and its actual duration in seconds. Credits are charged only on success, based on the produced duration and never more than the duration you requested. Pass an optional request_id to tag the result so you can locate it later via GET /assets/videos/results. Related tools: createVideo to generate the source clip, createVideoFromReferences for reference-driven generation. 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: cost varies by model and duration (credits/sec): Eagle 2/s, Forge Pixel 2/s (min 4); see this endpoint's full pricing table in the API docs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestBodyYesPayload for editing a previously generated video with a text prompt and optional reference images.

TDQS

A4.8/5.0
Behavior5/5

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

No annotations were provided, but the description discloses critical behaviors: immediate 202 response with job id, polling via getApiJob, credit charging only on success, rate limit of 50 concurrent jobs, and idempotent request_id behavior. This meets the full burden of behavioral transparency.

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 informative but not concise. It repeats the credits section twice and the async polling instructions appear in both the description and the final paragraph. The structure is bulky and could be streamlined to improve readability without losing essential details.

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?

Despite the lack of an output schema, the description explains the response format (202 + job id, poll getApiJob for result), credit costs, rate limits, and idempotency. Combined with full parameter descriptions, the agent has all necessary context to call this tool correctly.

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

Parameters5/5

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

The schema provides detailed descriptions for every parameter, including model capabilities and per-model duration values. The description reinforces key points like video URL restrictions and request_id uniqueness. With 100% schema coverage, parameter semantics are fully explained.

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 explicitly states 'Edit a previously generated video' with a specific verb and resource, and distinguishes itself from createVideo and createVideoFromReferences by requiring an existing generated video. It clearly identifies the tool's video-to-video editing purpose.

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 provides explicit usage constraints: the video must be generated within the last 7 days and external URLs are not accepted. It also explains the async flow (returns 202, poll getApiJob) and mentions related tools. This leaves no ambiguity about when and how to invoke it.

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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