The user's own top-selling ASINs by net profit for each of the last N months, derived from their sales. Call for 'what are my best sellers?' or 'top products this year'. months up to 24, top_n up to 50. Requires a connected Amazon account.
| Name | Required | Description | Default |
|---|---|---|---|
| top_n | No | ||
| months | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It states the requirement of a connected Amazon account and that data is derived from the user's sales, implying read-only. However, it does not discuss data freshness, error handling (e.g., no sales), caching, or side effects. While the constraints are helpful, the lack of deeper behavioral details leaves gaps.
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 highly concise with two sentences. The first sentence immediately states the core purpose. The second sentence packs usage guidance and parameter limits efficiently. No redundant information is present, and key details are front-loaded.
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 has two optional parameters and no output schema or annotations. The description covers purpose, usage, parameter limits, and a prerequisite. However, it does not describe the output format (e.g., whether it returns a list, what fields are included), which is needed for an agent to use the tool effectively. Given these gaps, the description is adequate but not 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?
With 0% schema description coverage, the description must compensate. It explicitly explains both parameters: months as the number of recent months (up to 24) and top_n as the number of top sellers (up to 50). This goes beyond the schema's names and types, providing meaningful context. However, it does not describe the exact format or relationship between parameters (e.g., whether months and top_n are independent) which prevents a 5.
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 returns the user's own top-selling ASINs by net profit over recent months. It is specific about the resource (ASINs), the metric (net profit), and the scope (user's own sales). The usage phrases 'what are my best sellers?' and 'top products this year' further clarify purpose, and the tool is distinct from sibling tools like get_dashboard_summary or get_product_performance.
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 explicit use cases ('what are my best sellers?', 'top products this year') and states constraints (months up to 24, top_n up to 50) and a prerequisite (connected Amazon account). However, it does not mention when not to use this tool or explicitly point to alternative sibling tools, preventing a score of 5.
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.
