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openapi_v2_markets_structure_profile

  1. Function

Return one selected Top 100 product distribution for one US category market. The required dimension parameter selects exactly one of brand, seller, price, sellerCountry, fulfillment, ratingCount, rating, listingAge, listingYear or productFeature. The response echoes data.dimension and returns its groups in data.buckets[], including product count, estimated sales and revenue, Amazon self-operated contributions within each group, 1/3/6/12-calendar-month new-product measures, and example ASINs. Products that do not fit a named business group belong to the other/unclassified group.

  1. Use cases

Use structure-profile to inspect one grouping behind the market metrics; call it again with another dimension when needed. Use search for overall metrics and history for changes over time.

  1. Example

Call with {"categoryId":"1045564","includeDescendantCategoryProducts":true,"sampleType":"unitSalesTop100","dimension":"price"}. Read data.dimension="price", data.sampleSkuCount, data.buckets[].skuRate, data.buckets[].revenueRate and data.buckets[].amazonSelfOperatedRevenueRate. The bucket product counts add up to data.sampleSkuCount.

  1. Data range

US only. Buckets describe the selected Top 100 products for the resolved data.date, not all products in the category. Product shares use the actual selected product count; sales and revenue shares use their respective valid totals for that same sample. Shares total 1 when the denominator is positive, subject to rounding. Each bucket returns all four new-product windows so an Agent can compare them without additional calls.

  1. New-product definition

For each bucket newProductMetrics[] item with periodMonths=N, a product is new only when its business launch date is later than data.date minus N calendar months and no later than data.date. The business launch date prefers Amazon Date First Available; when unavailable, it uses the earliest valid SKU first-observed date, SKU first-review date, or parent-product first-review date. Products without a business launch date are not counted as new, but remain in the bucket product-count denominator.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoMarket lookup date (YYYY-MM-DD); defaults to the latest published snapshot. The response date shows the actual snapshot date.
dimensionYesOne distribution to return: brand, seller, price, sellerCountry, fulfillment, ratingCount, rating, listingAge, listingYear, or productFeature.
categoryIdYesAmazon category node ID to analyze.
sampleTypeNoHow to select the Top 100 products within the category scope: by estimated monthly unit sales or by estimated monthly revenue.unitSalesTop100
marketplaceNoAmazon marketplace; currently US only.US
includeDescendantCategoryProductsNoWhether market metrics include products assigned to descendant category nodes; defaults to true.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description takes on the full burden. It explains the response structure (data.buckets[]), the data range (US only, selected Top 100), the calculations (shares total 1 subject to rounding), and the new-product definition in detail, which is valuable behavioral context beyond what the schema provides.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with numbered sections, front-loading the core function and example call. It is long but every section provides necessary detail, and the numbering aids skimming. Minor redundancy in example and use cases but not excessive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers everything an agent needs: what the tool does, how to call it with a concrete example, what the response contains, the data scope and limitations, and the definition of key terms like new-product metrics. Given the complexity, it's remarkably complete without an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage, so baseline is 3. The description adds extra meaning for the dimension parameter (lists all allowed values) and clarifies the date parameter default behavior, plus the example call shows how parameters combine correctly. This goes beyond just restating the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns one selected Top 100 product distribution for a US category market, with a required dimension parameter that selects the grouping. It lists all possible dimensions and distinguishes it from siblings like openapi_v2_markets_search and history, making its purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The use cases section explicitly says to use structure-profile for inspecting one grouping and to use search for overall metrics and history for changes over time. It doesn't explicitly state when not to use it beyond that, but the guidance is clear enough.

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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