get_shopify_productGet one product from a public Shopify store by its handle (slug), from that store's own /products.json — keyless, live now.
| Name | Required | Description | Default |
|---|---|---|---|
| store | Yes | Shopify store domain, e.g. allbirds.com. Required. | |
| handle | Yes | Product handle (slug). Required. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly, idempotent, openWorld, and non-destructive behavior. The description adds 'keyless' (no auth needed), 'live now' (real-time fetch), and the specific source endpoint, which are valuable beyond the annotations. Missing details like error handling are minor for this simple tool.
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 concise sentence that front-loads the primary action and key constraints, with zero filler 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?
For a simple two-parameter read-only tool with strong annotations, the description covers the essential context (public store, keyless, live, source endpoint). It does not explain return format, but without an output schema and given the simplicity, this is an acceptable gap.
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 coverage is 100%, with both parameters (store and handle) already described with examples and types. The description mentions 'handle (slug)' which is redundant with the schema, so it does not add meaningful semantics beyond what the schema provides.
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 clearly states the verb 'Get', the resource 'one product from a public Shopify store by its handle (slug)', and distinguishes it from siblings like search_shopify_products by focusing on a single product. It also adds a specific data source (/products.json), making the purpose unambiguous.
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 implies when to use this tool: when you have a store and handle for a public Shopify product and need keyless, live data. It does not explicitly name alternatives or when-not-to-use, but the context from title and sibling tools provides sufficient guidance.
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.