APIClaw

openapi_v2_markets_price_distributions

Function

Select up to 100 Amazon Listings with the highest monthly unit sales or monthly revenue in the market, then group them by automatically generated price bands. Each group returns Listing count and share, monthly unit sales and share, monthly revenue and share, and per-Listing means for price, star rating, rating count, monthly unit sales and monthly revenue.

Use cases

Profile the market by price band: compare Listing supply, unit sales and revenue across price ranges in the selected sample, and inform pricing and price-band positioning.

Example

{"categoryIds":["679399011"],"sampleType":"unitSalesTop100"}

Data range

US marketplace only. Each category is analyzed independently; product coverage can include or exclude descendants. Missing metrics remain null. Based on a leading sample of up to 100 Amazon Listings selected by monthly unit sales or revenue; results describe the sample. Omit the date for the latest available snapshot. An explicit date requires a published snapshot on that day; otherwise return no data. Use the resolvedDate returned by market search to query the same snapshot. Before querying, balance must cover the distinct requested market count at the endpoint unit price; this check does not deduct credits. Billing counts distinct returned markets (site and category ID); empty data costs zero. Price and rating means in this response exclude Listings whose corresponding metric is null; zero values participate. Different means can have different denominators; no valid values returns null.

Input Schema

Name	Required	Description	Default
`date`	No	Published snapshot date (YYYY-MM-DD). Omit for the latest available common snapshot; an unpublished explicit date returns no data. Use the resolvedDate returned by market search to query the same snapshot.
`sampleType`	No	unitSalesTop100 selects up to 100 Amazon Listings by monthly unit sales; revenueTop100 selects up to 100 by monthly revenue. Concentration analysis uses the same metric to rank products, brands or sellers and calculate their sample shares.	unitSalesTop100
`categoryIds`	Yes	Real category IDs; each ID selects an independent market.
`marketplace`	No	Marketplace code; currently US only.	US
`includeDescendantCategoryProducts`	No	true includes products from this category and its descendants; false counts only directly assigned products. Controls product statistics, not market-row selection.

Output Schema

Name	Required	Description
`data`	No	Response data payload
`meta`	Yes	Response metadata including pagination info
`error`	No	Error details if request failed
`success`	No	Whether the request was successful

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 21, 2026

TDQS

A4.4/5.0

Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so extensively: US-only scope, independent category analysis, descendant inclusion behavior, null handling, snapshot/date semantics, billing unit price and empty-data cost, and denominator caveats for means. This is rich operational context beyond the schema.

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 long but organized into numbered sections with the core function front-loaded. Most sentences carry useful operational detail, though the length is on the higher side for a single tool.

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?

Given a complex tool with five parameters, no annotations, and an output schema, the description is complete enough. It covers sample definition, date behavior, marketplace restriction, descendant handling, billing, and missing-data semantics without needing to explain return values.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description reinforces sample selection and date behavior, but most parameter-level meaning is already supplied by the schema, so it does not substantially exceed the structured documentation.

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 states a specific verb and resource: select up to 100 Amazon Listings by monthly unit sales or revenue and group them into price bands. It clearly distinguishes this tool from sibling distribution tools by focusing on price bands and the returned price/rating/sales metrics.

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 description provides a use case: profiling the market by price band for pricing and price-band positioning. It does not explicitly name alternative sibling tools or state when not to use this tool, but the context is clear enough for an agent to know when it applies.

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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