get_shopify_collectionsList a public Shopify store's collections (id, handle, title, product count, image) — keyless, live now.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Default 1. | |
| limit | No | Default 50. | |
| store | Yes | Shopify store domain, e.g. allbirds.com. Required. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful extra context, including 'keyless' (no authentication required) and 'live now' (fresh data), plus a list of returned fields. This goes beyond the structured 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, dense sentence that front-loads the purpose, specifies output fields, and includes access mode and freshness. There is no fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is a simple list operation with robust schema and annotations. The description names output fields and clarifies the public, keyless nature. It does not explicitly state pagination behavior, but the schema already provides page and limit details, so the description is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all three parameters having descriptions (store, page, limit). The description itself does not add additional parameter-level meaning beyond what the schema already provides, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and resource ('public Shopify store's collections'), and enumerates the returned fields (id, handle, title, product count, image). This clearly distinguishes it from sibling tools like get_shopify_product and search_shopify.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: it is for listing public Shopify collections and emphasizes 'keyless, live now.' It does not explicitly state when to use it over alternatives or when not to use it, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinguished by platform and entity (e.g., get_etsy_shop vs get_etsy_listing), but search_shopify and search_shopify_products have overlapping purposes and near-identical names, which could cause misselection. The individual descriptions help, but the boundary is fuzzy.
The dominant pattern is get_<platform>_<entity> and search_<platform>_<entity>, which is clear and consistent. However, deviations like search_shopify_products (which lists all products rather than performing a filtered search) and the use of plural get_ebay_items for bulk retrieval break the clean verb_noun convention in a few places.
14 tools across four distinct platforms is well-scoped, with each platform getting a focused set of search and get operations. This is comfortably within the ideal range and each tool serves a concrete purpose.
The server provides solid read-only coverage for product search and detail retrieval across four platforms. Minor gaps exist, such as no way to fetch products within a Shopify collection or list Best Buy categories, but these are workaround-able and do not severely impair agent workflows.