APIClaw

openapi_v2_markets_seller_concentrations

Function

Select up to 100 Amazon Listings with the highest monthly unit sales or monthly revenue in the market. Aggregate and rank them by seller using that same sales metric. Show the Top 3/5/10/20 seller groups’ share of that sample’s monthly unit sales or monthly revenue. Return Listing counts, sales totals and per-Listing means for price, star rating, rating count, monthly unit sales and monthly revenue, plus new-product counts, sales totals, shares and means for the preceding 1/3/6/12 months. Up to 20 leading sellers include Listing and brand counts, sales shares, the same per-Listing means and example products.

Use cases

Assess whether sales are concentrated among a few sellers, compare Top 3/5/10/20 seller concentration and new-product composition, and examine Listing counts, brand counts and example products to understand leading sellers.

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 Omit for the latest available common snapshot; an unpublished explicit date returns no data.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 coverage, sample-based results, missing metrics remaining null, date-snapshot requirements, balance checks, billing rules, and the fact that empty data costs zero. These details are important for correct invocation and cost expectations.

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 front-loaded with the function and organized into numbered sections, making it easy to scan despite its length. It is verbose for a tool definition, but most details serve the complexity of the endpoint.

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?

For a complex market-analysis tool with five parameters and an output schema, the description covers sample construction, date behavior, billing, null handling, and scope limitations. Nothing material appears missing for an agent to invoke it correctly.

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 schema already documents the five parameters thoroughly. The description reinforces sample and date semantics, but adds little parameter meaning beyond what the schema provides, making 3 the appropriate baseline.

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 gives a specific verb and resource: it selects up to 100 Amazon Listings, aggregates and ranks them by seller, and reports Top 3/5/10/20 seller concentration. This clearly distinguishes the seller-concentration analysis from sibling tools such as brand and product concentration tools, even without naming them.

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?

A dedicated 'Use cases' section states when the tool is useful: assessing seller concentration, comparing Top 3/5/10/20 seller concentration, and examining leading sellers. It does not explicitly name alternatives like brand_concentrations or top_product_concentrations, so the agent must infer the boundary from the tool names.

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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