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Apiguru Amazon Data

Customer reviews for a single ASIN

product_reviews
Read-onlyIdempotent

Returns the review block for one ASIN: overall star rating, total rating count, Amazon's 'customers say' AI summary, and the individual review list. Price: $0.01 per call. Same 404-billed / 503-not-billed semantics as product_details. Takes no filters: it returns the rating, rating count, the 'customers say' summary and the reviews Amazon shows on the product page itself. There is no paging, star filter or sort -- Amazon's review pages require a signed-in customer, and the API does not sign in. For per-star counts read the rating histogram on product_details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoMarketplace country code.US
asinYesSingle Amazon ASIN, 10 uppercase alphanumeric characters.
max_reviewsNoCap on individual reviews returned (0 = all). The rating summary and customers_say are always returned; _reviews_total says how many exist.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
successNo
request_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations, the description discloses pricing per call, billing semantics for 404 vs 503 responses, and the reason no paging exists: Amazon's review pages require a signed-in customer and the API does not sign in. This adds substantial behavioral context beyond the structured hints.

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

Conciseness5/5

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

The description is compact and front-loaded, with each sentence earning its place: what it returns, cost and billing context, constraints, and an alternative. No filler or repetition.

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 the rich annotations, complete schema coverage, and the presence of an output schema, the description covers the key operational constraints, pricing, and alternative routing. An agent has everything needed to decide whether and how to call it.

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 asin, geo, and max_reviews clearly. The tool description adds general behavioral context but no additional parameter-level semantics beyond what the input schema provides.

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 opens with a specific verb and resource: 'Returns the review block for one ASIN' and enumerates the exact contents (star rating, rating count, 'customers say' summary, review list). It also distinguishes itself from product_details by explicitly noting what it does not provide, such as star filters and paging.

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

Usage Guidelines5/5

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

The description clearly states that this tool takes no filters and has no paging, star filter, or sort, which tells an agent what not to expect. It explicitly directs agents to product_details for per-star counts, giving an actionable alternative.

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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