search_shopifyFilter a public Shopify store's catalog by keyword across title, vendor, product type, and tags — keyless, live now.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Keyword filter, e.g. "wool runner". Required. | |
| 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 mark the tool as read-only, idempotent, and non-destructive. The description adds valuable context beyond annotations: it specifies that the store must be public, requires no key, operates on live data, and searches specific fields. This meaningfully clarifies behavior.
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, tightly written sentence with no filler. It front-loads the core action and includes only high-signal additions ('keyless, live now') that are not redundantly stated in the annotations.
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?
Given the tool's simplicity, strong annotations, and fully documented schema, the description covers the key operational context: public access, keyless usage, live data, and search scope. It does not describe return format, but no output schema exists and the absence is acceptable for a straightforward search tool.
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%, providing descriptions for all three parameters. The description adds extra meaning by listing which fields the keyword applies to ('title, vendor, product type, and tags'), enriching the semantics of the 'q' parameter beyond the schema's basic example.
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 states a specific verb ('Filter') and resource ('public Shopify store's catalog') with clear scope ('by keyword across title, vendor, product type, and tags'). It is unambiguous, though it does not explicitly distinguish itself from the sibling tool 'search_shopify_products'.
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 usage for public Shopify stores with no API key ('keyless, live now'), but it does not state when to use this tool instead of alternatives like 'search_shopify_products', nor does it mention any exclusions or conditions.
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.