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post-skybox-base360-inferences

Generate seamless 360-degree skybox images from text prompts or input images for immersive environments and panoramic scenes.

Instructions

Trigger the generation of a 360 skybox seamless image.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originalAssetsNoIf set to true, returns the original asset without transformation
dryRunNo
imageNoThe image to use as a starting point for the skybox generation. Must reference an existing AssetId or be a data URL.
styleFidelityNo(deprecated) Condition the influence of the style image. The higher the value, the more the style image will influence the generated skybox image.
structureImageNoThe control image for structure. A canny detector will be applied to this image. Must reference an existing AssetId.
seedNoUsed to reproduce previous results. Default: randomly generated number.
strengthNoControls the noise intensity introduced to the input image, where a value of 1.0 completely erases the original image's details. Available for img2img and inpainting. (within [0.01, 1.0], default: 0.75)
styleImagesNo
depthFidelityNoThe depth fidelity if a depth image provided
numOutputsNoThe number of outputs to generate.
depthImageNoThe control image processed by depth estimator. Must reference an existing AssetId.
negativePromptNoA negative full text prompt that discourages the skybox model from generating certain characteristics. It is recommended to test without using a negative prompt. Default: empty string. Example: "Low resolution, blurry, pixelated, noisy."
numInferenceStepsNoThe number of denoising steps for each image generation.
overrideEmbeddingsNo(deprecated) Override the embeddings of the model. Only your prompt and negativePrompt will be used. Use with caution.
promptFidelityNoIncrease the fidelity to the prompt during upscale.
widthNoThe width of the rendered image.
styleNoStyle to apply for generation.
negativePromptStrengthNoControls the influence of the negative prompt. Default 0 means the negative prompt has no effect. Higher values increase negative prompt influence. Must be > 0 if negativePrompt is provided.
structureFidelityNoThe structure fidelity if a structureImage or a cannyStructureImage image is provided.
promptYesA full text prompt to guide the skybox generation process. Default: empty string. Example: "a mountain landscape"
cannyStructureImageNoThe control image already processed by canny detector. Must reference an existing AssetId.
geometryEnforcementNoApply extra control to the Skybox 360 geometry. The higher the value, the more the 360 geometry will influence the generated skybox image. Use with caution. Default is adapted to the other parameters.
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'trigger the generation' which implies a potentially long-running or asynchronous process, but fails to detail execution behavior, error handling, rate limits, authentication needs, or output format. This is inadequate for a complex tool with many parameters.

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?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it easy to parse quickly.

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

Completeness2/5

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

Given the tool's complexity (22 parameters, no output schema, no annotations), the description is insufficient. It lacks information on output format, execution behavior, error cases, and practical usage context. For a generative AI tool with many configuration options, more guidance is needed to help an agent use it effectively.

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 description coverage is high at 91%, so most parameters are documented in the schema. The description adds no additional parameter semantics beyond what's in the schema, such as explaining interactions between parameters or typical usage patterns. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('trigger the generation') and the resource ('a 360 skybox seamless image'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from its sibling 'post-skybox-upscale360-inferences', which might handle upscaling rather than base generation, leaving some ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as other skybox or image generation tools in the sibling list. It lacks context about prerequisites, typical use cases, or comparisons to similar tools like 'post-skybox-upscale360-inferences'.

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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