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post-pixelate-inferences

Apply pixelation effects to images with customizable grid sizes, noise reduction, and background removal for artistic transformations.

Instructions

Advanced pixelization of an image.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originalAssetsNoIf set to true, returns the original asset without transformation
dryRunNo
imageYesThe asset ID (example: "asset_GTrL3mq4SXWyMxkOHRxlpw") to pixelate.
pixelGridSizeYesThe size of the pixel grid in the output image. Should be 16, 32, 64, 128, or 256.
removeNoiseYesReduce pixel art artifacts.
removeBackgroundNoRemove the background from the image.
colorPaletteNo
colorPaletteSizeNoIf no colorPalette is provided, you can provide a palette size. Value should be between 2 and 256.
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Advanced pixelization' implies a mutation operation that transforms an image, but it doesn't specify whether this is destructive to the original, requires specific permissions, has rate limits, or what the output looks like (e.g., returns a new asset ID). The description lacks critical behavioral context for a tool with 8 parameters and no output schema, leaving the agent uncertain about side effects and results.

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: 'Advanced pixelization of an image.' It's front-loaded with the core action and resource, with zero wasted words. While it could be more informative, it meets conciseness criteria by avoiding redundancy and being appropriately sized for a tool name that hints at functionality.

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 (8 parameters, 3 required, no output schema, and no annotations), the description is incomplete. It doesn't explain the transformation's output, error conditions, or behavioral traits. For an image processing tool with multiple options like 'removeBackground' and 'colorPalette', more context is needed to guide the agent effectively. The description fails to compensate for the lack of structured data, leaving significant gaps.

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?

Schema description coverage is 75%, with 6 of 8 parameters having descriptions (e.g., 'image', 'pixelGridSize', 'removeNoise'). The description adds no parameter-specific information beyond the schema. For parameters without schema descriptions ('dryRun', 'colorPalette'), the description doesn't compensate. Given the high schema coverage, the baseline is 3, as the description doesn't enhance parameter understanding but doesn't detract either.

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

Purpose3/5

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

The description 'Advanced pixelization of an image' states the tool's purpose (pixelization) but is vague about what 'advanced' entails. It distinguishes from siblings like 'post-upscale-inferences' or 'post-vectorize-inferences' by specifying pixelization, but doesn't clarify how it differs from other image transformation tools (e.g., 'post-img2img-inferences') in terms of output or method. The verb 'pixelization' is clear, but the modifier 'advanced' lacks specificity.

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. With many sibling tools for image processing (e.g., 'post-upscale-inferences', 'post-remove-background-inferences'), there's no indication of scenarios where pixelization is preferred, prerequisites, or exclusions. The agent must infer usage from the tool name and parameters alone, which is insufficient for effective selection.

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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