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ApparelHub-AI

apparelhub-mcp

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by ApparelHub-AI
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TypeScript
Hybrid

set_product_images

Set a product's listing gallery by replacing images, reordering them, and choosing a cover. The list replaces all images; read before adding.

Instructions

Set an existing product's listing images: attach an uploaded photo or a generated lifestyle shot, reorder them, and choose the cover. Use this AFTER the product exists — create_product / ship_product pick the initial mockup themselves.

⚠️ THE LIST REPLACES, IT DOES NOT MERGE. What you send becomes the whole gallery, in the order given. To add one image, READ the current list first and send it back with the new entry in it — sending the new entry alone deletes every other image. Pass images: null to reset the gallery back to the product's provider mockups.

⚠️ ORDER IS FUNCTIONAL, NOT COSMETIC. Channels cap how many images a listing may carry and TRUNCATE IN GALLERY ORDER, so position decides what actually ships: TikTok Shop takes 9, Wix 15, Shopify and WooCommerce are unlimited. On a capped channel an image in position 10 is not a lower-priority image, it is an absent one. Put the images that must survive first. The platform stores at most 20.

Each entry carries provenance. source says where the file came from (mockup / upload / ai_mockup / print_file / unknown). ai_generated is SEPARATE and tri-state on purpose: an uploaded photo may itself have been AI-generated and the platform cannot detect that, so only you can say. Set it truthfully — true, false, or leave it unset when you genuinely do not know. Do not guess it from source.

cover sets the display image independently of order, so the cover need not be first. A cover that is not in the gallery is added to it. Replace the gallery without naming a cover and the cover follows to the new first image.

CONCURRENCY: this reads the product first and passes its version back with the write, so a change someone else made in between is REFUSED rather than silently overwritten. On a conflict the tool re-reads and returns conflict: true with the current images — it does NOT retry, because the list you built was based on a gallery that no longer exists. Rebuild from current_images and call again.

[#781240]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coverNoURL of the image to show as the listing cover. Independent of gallery order. Added to the gallery if it is not already in it.
imagesNoThe COMPLETE ordered gallery, replacing whatever is there. Null resets to the product's provider mockups. Omit to change only the cover.
workspaceNoWorkspace uuid (agency accounts).
product_uuidYesThe product whose listing images to set.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.15.2

TDQS

A4.9/5.0
Behavior5/5

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

With only openWorldHint in annotations, the description carries the full behavioral disclosure burden and does so exceptionally well. It discloses destructive replacement semantics, gallery truncation by channel order, maximum 20 images, provenance and tri-state ai_generated meaning, cover independence, and optimistic-concurrency conflict behavior including no automatic retry. There is no contradiction with annotations.

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 long but front-loads purpose and the most dangerous behavior (replacement, ordering, concurrency). Most sentences carry operational value. It loses a point for density and a stray issue tag (#781240), which adds no user-facing value, though the overall structure is navigable and appropriate for a high-stakes mutation tool.

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 mutation complexity, minimal annotations, and lack of an output schema, the description is complete enough for correct invocation. It covers prerequisites, destructive reset semantics, channel truncation, provenance handling, cover behavior, and conflict return behavior (conflict: true, current_images, no retry). No critical gap is apparent.

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?

Schema description coverage is 100%, so a baseline of 3 would be acceptable, but the description adds substantial meaning beyond the schema. It explains that images replaces rather than merges, omitting images changes only the cover, images: null resets to provider mockups, order affects channel truncation, cover need not be first and is added if absent, and ai_generated must not be guessed from source.

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+resource: 'Set an existing product's listing images.' It also enumerates the core operations (attach, reorder, choose cover) and explicitly distinguishes this from create_product / ship_product, which pick initial mockups. An agent can identify and differentiate the tool 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?

It gives explicit timing ('AFTER the product exists') and names the alternatives that handle initial image selection. It also provides critical operational guidance: read the current list before adding, send the full list, use images: null to reset, and call again after a conflict. No major usage ambiguity remains.

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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