Full analysis of an Amazon product by ASIN — everything in analyse_product plus seller/stock breakdown, buy-box history and variations. Slower and heavier than analyse_product; use when the user wants seller-level or historical detail in one shot. Costs 1 analyse credit unless the ASIN was analysed in the last 24 hours.
| Name | Required | Description | Default |
|---|---|---|---|
| asin | Yes | ||
| domain | No | GB |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility. It discloses that the tool is 'slower and heavier,' implies read-only analysis, and mentions cost and caching (free if analysed last 24 hours). It does not hide any destructive behavior. Slight room for improvement: could explicitly state it does not modify data.
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 four sentences, each providing essential information: what the tool does, what it includes, performance comparison, usage guidance, and cost/caching details. It is front-loaded with the core purpose and contains no unnecessary words.
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 only 2 parameters, no output schema, and no annotations, the description covers the tool's purpose, inputs, alternatives, cost, and caching. It does not describe the return format, but that is acceptable without an output schema. Minor gap: could mention that it returns a comprehensive report or data structure.
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 mentions 'ASIN' as the input, which covers the required parameter. However, the optional 'domain' parameter is not described, and the schema has no parameter descriptions (0% coverage). While agents may infer domain from context, the description could add value by explaining that domain is the Amazon marketplace (e.g., GB, US).
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 performs a 'full analysis of an Amazon product by ASIN' and specifies it includes 'seller/stock breakdown, buy-box history and variations.' It distinguishes itself from sibling 'analyse_product' by explicitly saying it includes everything in that tool plus more.
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 this tool: 'use when the user wants seller-level or historical detail in one shot.' It also contrasts with 'analyse_product' by noting it is 'slower and heavier,' providing clear alternatives. Additionally, it mentions the credit cost and caching behavior.
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.
