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ApparelHub-AI

apparelhub-mcp

Official

fit_aspect

Reshape an existing design image to a target aspect ratio (pad or crop) without generating a new one. Adapt square designs to product print areas like phone cases or mugs.

Instructions

Fit an EXISTING design image to a target aspect ratio without generating a new one. mode="pad" letterboxes it onto a background (keeps the whole design, nothing cropped); mode="crop" center-crops (trims the edges to fill the shape). QUOTA-FREE: this reshapes an existing image and does NOT consume an image-generation credit. Use to adapt a square design to a product's print area (e.g. a tall 9:16 for a phone case or poster, a wide 16:9 for a mug or banner). Returns a NEW design (image uuid + url). Note: for an AI-generated EXTENSION of the borders (outpainting) instead of a flat pad/crop, generate a new image with generate_image at the target size — that DOES use the image-generation quota.

[#9ca805]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYespad (default): letterbox onto a background, keeping the whole design (nothing lost). crop: center-crop to fill the shape, trimming the edges.pad
aspectYesTarget aspect ratio as "W:H". Common: 9:16 tall (phone cases, posters), 16:9 wide (mugs, banners), 1:1 square, 4:5 portrait.
workspaceNo
backgroundNoFill color for the padded bars as #RRGGBB (pad mode only; ignored for crop). Defaults to transparent/white on the platform when omitted.
image_uuidYesThe uuid of an existing design to reshape (from generate_image / list_my_designs).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.15.2

TDQS

A4.4/5.0
Behavior3/5

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

The only annotation is openWorldHint=true, so the description carries nearly the full behavioral burden, which it does well: it discloses that the operation is quota-free and does NOT consume an image-generation credit, and that it returns a NEW design (uuid + url). However, it doesn't state authorization requirements, whether the source image is left untouched, or any size/format limits, so it stops short of full disclosure.

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?

Front-loads the core action and the quota-free guarantee, then elaborates on modes and the generate_image alternative. It is information-dense rather than padded, though the trailing '[#9ca805]' token is stray noise and the parenthetical alternatives make it longer than strictly necessary.

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?

With no output schema, the description compensates by specifying the return shape (uuid + url), and it covers mode semantics, the quota implication, and the sibling alternative. An agent has everything needed to call it 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 80%, so most parameters are already documented. The description still adds genuine value by mapping aspect ratios to real use cases (tall 9:16 for phone cases, wide 16:9 for mugs) and by re-explaining pad vs crop in outcome terms (nothing cropped vs trims the edges).

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?

States a specific verb and resource ('Fit an EXISTING design image to a target aspect ratio without generating a new one') and explicitly negates the adjacent generative operation, so it is unmistakable against generate_image. The scope ('reshapes an existing image') is front-loaded and unambiguous.

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?

Gives concrete when-to-use context (adapt a square design to a product's print area, with 9:16 phone case / 16:9 mug examples) and names the alternative explicitly: use generate_image for outpainting instead of flat pad/crop. Both the routing condition and the exclusion are stated.

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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