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ApparelHub-AI

apparelhub-mcp

Official

create_product

Create a standalone product from a design, applying correct fields, pricing floor, embroidery routing, and face-goods fill defaults. Add mockups and variants before listing on a store.

Instructions

Create a STANDALONE product from a design (split primitive) — it is NOT placed on any store yet. Applies the correct field names + pricing floor, routes EMBROIDERY garments (caps/beanies) to their real embroidery placement with Printful thread colors (derived or explicit), and defaults face goods (canvas/backpacks/bags/socks/towels/blankets/pillows/cases...) to print_style "fill" (design recomposed onto a matching background, printed edge-to-edge). Set generate_mockup: true to render a garment mockup as the display image (it auto-derives representative variants from the catalog, so you do NOT need mockup_variant_ids) — otherwise the raw design is used as the display image. To get it onto a store and listed, the required sequence is: add_variants -> sync_to_fulfillment(product_uuid, store_uuid) [associates it with the store + syncs to Printful/Printify] -> sync_to_channel [sales channel]. To run that whole pipeline in one call instead, use ship_product.

[#e8704a]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
garmentYes
pricingYes
workspaceNo
design_urlNo
design_uuidYesThe design to print. Comes from generate_image / design_apparel, OR from upload_design when the merchant already owns the artwork (a logo, a brand mark, a cleared cover). Never regenerate a mark you were given as a file.
print_styleNoHow the design sits on the print face. "fill": recompose onto a matching background and print edge-to-edge (default for face goods). "placed": transparency preserved (default for apparel and embroidery). "auto" (default) picks by garment.
product_metaYes
thread_colorsNoEMBROIDERY garments only: explicit Printful thread palette colors. Omit to auto-derive from the design.
generate_mockupNo
mockup_variant_idsNoRepresentative variant ids for the mockup preview (numeric on Printful/Printify, string productUids on Gelato).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.15.2

TDQS

A4.6/5.0
Behavior4/5

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

Annotations only declare openWorldHint=true, so the description carries real weight and delivers: embroidery routing to real placements with derived Printful thread colors, fill-by-default for face goods, and that generate_mockup auto-derives variants so mockup_variant_ids is unnecessary. It does not cover failure modes, auth/permission needs, or what the call returns, so it falls short of a 5.

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 purpose and the standalone constraint are front-loaded, and the pipeline routing is condensed into one sentence. It is dense but not wasteful; the main deduction is the trailing stray artifact "[#e8704a]" and a very long single paragraph that is harder to scan than bulleted clauses.

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?

For a 10-param, nested-object creation tool with no output schema and minimal annotations, the description supplies the missing context: what state the product lands in, how garment-specific defaults resolve, and the downstream calls needed to publish it. An agent has enough to invoke 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?

With only 40% schema description coverage, the description compensates well: it explains print_style defaults per garment class, thread_colors' embroidery-only scope and auto-derivation, and generate_mockup's variant derivation. It says less about garment, pricing, workspace, and design_url, leaving some of the 10 params thin.

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?

Starts with a specific verb+resource ("Create a STANDALONE product from a design") and immediately scopes it ("it is NOT placed on any store yet"), which cleanly separates it from ship_product and the sync_* siblings. An agent can classify this 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?

Explicitly names the required follow-on sequence (add_variants -> sync_to_fulfillment -> sync_to_channel) and the alternative ("To run that whole pipeline in one call instead, use ship_product"). This is exactly the when-to-use/when-to-use-something-else guidance the dimension asks for.

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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