Status and results of a Reverse Source scan started with start_reverse_source_scan. Returns progress % and, once complete, the cheapest Google Shopping source found per ASIN (retailer, price, link).
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it returns progress percentage during the scan and final results upon completion. No annotations provided, so description bears full burden; it could mention error handling or polling behavior but is adequate for a status check tool.
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?
Two sentences, front-loaded with purpose, no redundant information. Every sentence adds value.
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 single parameter, no output schema, and no annotations, the description sufficiently covers the tool's purpose, input, and output. Nothing critical missing for its complexity.
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?
Only one parameter (job_id). Schema description coverage is 0%. The description implies job_id identifies a Reverse Source scan but does not explicitly define its origin or format. Baseline 3 due to single param, but minor improvement needed.
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 returns status and results of a Reverse Source scan, specifying both progress percentage and final cheapest source per ASIN. It clearly distinguishes from start_reverse_source_scan which initiates the scan.
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?
Explicitly links to start_reverse_source_scan as the prerequisite, implying this tool is used after starting a scan. Does not explicitly state when not to use, but context is clear.
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.
