Skip to main content
Glama

Ludo AI Game Assets

by ai.ludo

generatePose

Re-pose an existing sprite into a new target pose while preserving the character, taking a source image (URL or base64), a pose name (or "Other" with a free-text description), and an optional n (1-4) for how many variations to produce. Only works with sprite image types (not icons, screenshots, etc.). The job result is an array of pose results, each containing the generated image url, the pose and description used, and a suggested motion_prompt tuned for that pose. Credits are charged only on success, scaled by the number of images generated. This is typically the first step before animating: call generatePose to set the character's pose, then feed the result (and its suggested motion_prompt) into animateSprite for the best animation quality; use rotateSprite instead when you want to change the camera angle rather than the pose. Pass an optional request_id to tag the results so you can locate them later via GET /assets/sprites/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 generating a new pose for an existing sprite

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden, and it does so thoroughly. It discloses the async 202/job-id flow, polling via getApiJob with wait: 30, the result structure, success-based credit charging (0.5 per result), queue limits (50), and the 429 PENDING_JOBS_LIMIT behavior. It even mentions request_id idempotency, which is valuable behavioral context.

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 dense and front-loaded with the core purpose, then workflow, then operational constraints. It is long, but nearly every sentence adds needed information given the lack of annotations and output schema. The only minor weakness is the first long run-on sentence, but overall it is well organized.

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 the tool's async nature, no annotations, and no output schema, the description is remarkably complete. It covers inputs, valid image types, pose selection, variations, result shape, polling instructions, credit costs, rate limits, and sibling-tool routing. An agent has everything needed to invoke and process the result correctly.

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%, so the baseline is 3. The description adds meaningful semantics beyond the schema by explaining the special 'Other' pose usage with a free-text description, the 1-4 variation range, and that credits scale with the number of images generated. It also connects the result's suggested motion_prompt to animateSprite, enriching the meaning of the output parameters.

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 states a specific verb and resource ('Re-pose an existing sprite into a new target pose while preserving the character') and clearly differentiates from siblings by naming rotateSprite as the alternative for camera-angle changes and animateSprite as the downstream animation step. An agent can identify what this tool does without opening the schema.

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?

Usage guidance is explicit: it says this is typically the first step before animating, names the next tool (animateSprite), and identifies rotateSprite as the correct choice for camera-angle changes. It also adds exclusion criteria ('Only works with sprite image types (not icons, screenshots, etc.)'), which helps avoid misuse.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Ludo AI Game Assets toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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