Products found by the user's storefront monitors (tracking other sellers' Amazon storefronts), newest first. Call when the user asks what their storefront monitors have found; optionally filter to one store_id. Paginated — 'total' in the response is the full count, request further pages for more. per_page max 200 (keep small: rows are wide).
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| per_page | No | ||
| store_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses pagination ('Paginated'), the meaning of 'total', per_page max of 200, ordering ('newest first'), and scoping to the user's monitors. This is substantial behavioral context.
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?
Three sentences, front-loaded with purpose, then usage, then pagination details. Every sentence adds value with 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 list tool with no output schema, the description covers purpose, usage, ordering, pagination, and parameter details. It lacks explicit return field information beyond 'total', but that's a minor gap for a products list.
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?
Schema description coverage is 0%, so the description compensates. It explains store_id as an optional filter, implies page for pagination, and specifies per_page max 200. All three parameters are addressed meaningfully.
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 products found by the user's storefront monitors, newest first. It distinguishes itself from sibling tools like get_storefront_monitors by focusing on results rather than the monitors themselves.
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?
It explicitly says 'Call when the user asks what their storefront monitors have found', providing a clear when-to-use context. It doesn't mention when not to use it or name alternatives, but the instruction is direct and sufficient.
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.
