Resolve a barcode (EAN/UPC, 8-14 digits) to an Amazon ASIN. Call when the user gives a barcode instead of an ASIN, then feed the returned ASIN into analyse_product.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| domain | No | GB |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as read-only nature, rate limits, or side effects. With no annotations, the description carries full burden, and it fails to clarify that the tool performs a read operation.
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 two sentences, perfectly concise, with key information front-loaded. Every sentence adds value, and there is no 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 lookup tool with two parameters and no output schema, the description covers purpose and usage well. However, it lacks details about the 'domain' parameter and whether the return is a single ASIN or list, slightly reducing completeness.
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 description only specifies that the barcode should be 8-14 digits for the 'code' parameter, but does not mention or explain the 'domain' parameter. Since schema coverage is 0%, the description should compensate, but it provides minimal parameter guidance.
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 it resolves a barcode to an Amazon ASIN, specifying the input format (EAN/UPC, 8-14 digits). It distinguishes from siblings like analyse_product and search_by_title by its unique function.
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 explicitly tells when to use the tool ('Call when the user gives a barcode instead of an ASIN') and what to do with the output ('feed the returned ASIN into analyse_product'), providing clear context and a next step.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct data type or action (e.g., product analysis, deal types, FBA operations). Even similar-sounding tools like get_deal_results and get_oa_deals are clearly separated by domain (A2A vs OA) in descriptions. No significant overlap.
All tools follow a clear verb_noun pattern with underscores (e.g., analyse_product, create_deal_task, get_credits). The consistent 'get_' prefix for retrieval tools and varied but predictable action verbs make the set easy to navigate.
At 37 tools, the set is large but covers a broad Amazon seller ecosystem (research, sourcing, FBA, deals, monitoring). Each tool serves a distinct purpose, and the count reflects the domain's complexity without being bloated.
The tool surface covers all major seller workflows: product analysis, profit calculation, sourcing, deal discovery, storefront monitoring, FBA operations, purchase tracking, price alerts, and reconciliation. No obvious gaps for core tasks.
