Skip to main content
Glama

Find a Product Across Shopify Stores

shopify_get_product_everywhere
Read-onlyIdempotent

Search multiple Shopify stores by exact SKU or product handle to get a unified view of product, price, status, and inventory.

Instructions

Find one exact SKU or product handle across selected stores and return a normalized product, price, status, and inventory matrix.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
storesYesOne to one hundred configured store aliases
matchByYesWhether the identifier is an exact SKU or exact product handle.
identifierYesExact SKU or product handle to find.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.6.0
    • changedInput schema / properties / stores / description
      Previous value: -"One to ten configured store aliases"New value: +"One to one hundred configured store aliases"
    • changedInput schema / properties / stores / maxItems
      Previous value: -10New value: +100
  2. Addedv1.5.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds useful behavioral context by promising a normalized cross-store matrix and naming the exact output categories, which goes beyond the annotations without contradicting them.

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?

One sentence conveys the action, scope, lookup key, and output in a front-loaded, compact way. Every word contributes meaning, with no filler or redundancy.

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

Completeness4/5

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

For a read-only lookup tool with fully described parameters, the description is nearly complete. The main gap is that 'normalized product' and 'inventory matrix' are not further explained, and there is no output schema to define those structures, but the high-level output is sufficient for most selection and invocation needs.

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?

All three parameters are fully documented in the schema, so the description does not need to repeat them. It slightly reinforces the exactness of 'identifier' and the selected-store scope, but it adds little semantic value beyond what the schema already provides.

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 names a specific verb ('Find'), a precise resource (one exact SKU or product handle), and the scope ('across selected stores'), which clearly separates it from single-store tools like shopify_get_product and search-oriented tools like shopify_search_products. It also states the output shape, a normalized product, price, status, and inventory matrix, making the tool's purpose unmistakable.

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

Usage Guidelines4/5

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

The description makes clear it is for exact SKU/handle lookup across multiple selected stores, which implies when to use it over list/search tools. It does not explicitly name alternatives or state when not to use it, but the context is strong enough for an agent to select it correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.