get_bestbuy_productGet one Best Buy product by SKU, full detail. Errors honestly until this engine is fully live upstream: currently returns not_found (route not yet redeployed on the production commerce-api), then credentials_required once deployed without BESTBUY_API_KEY provisioned.
| Name | Required | Description | Default |
|---|---|---|---|
| sku | Yes | Best Buy SKU, e.g. 6418599. Required. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds valuable context beyond annotations by disclosing the current deployment status and specific error states ('not_found', 'credentials_required') and the dependency on BESTBUY_API_KEY. This is transparent about operational caveats.
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 concise and front-loaded with the core purpose in the first sentence. The second sentence, while detailed, is necessary for operational transparency. No wasteful content, though the error explanation could be slightly more succinct.
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 simple one-parameter schema, presence of annotations, and output schema absence, the description covers the essential context: what the tool does, the parameter, and current error behavior. The vague 'full detail' is acceptable for a get-by-SKU tool, but a note on return format would make it fully 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?
The input schema fully describes the sole parameter 'sku' with an example, and the description adds no extra semantic meaning beyond 'by SKU.' Schema coverage is 100%, so the baseline of 3 applies.
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 tool's action: 'Get one Best Buy product by SKU, full detail.' It specifies the resource (Best Buy product), the identifier (SKU), and the depth of information (full detail), distinguishing it from sibling tools that target other marketplaces or search functionality.
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 when you have a SKU and need full product details, but it does not explicitly contrast itself with the sibling search_bestbuy tool or provide exclusions. There is no guidance on when not to use this tool, though the purpose is self-evident.
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.