Skip to main content
Glama
apiguru-app

apiguru-amazon-data

Customer reviews for a single ASIN

product_reviews
Read-onlyIdempotent

Fetch Amazon product reviews for a given ASIN, including overall star rating, rating count, AI-generated summary, and individual reviews. No filters or paging.

Instructions

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: $0.003 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 observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already cover the read-only/idempotent safety profile, but the description adds substantial context beyond them: per-call price ($0.003), 404-billed vs 503-not-billed semantics, and the signed-in-customer auth limitation that explains the absence of filtering. This is rich disclosure that structured fields cannot carry.

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?

Front-loaded with the return payload and every sentence earns its place (pricing, error semantics, scope, alternative). Slight redundancy in restating the returned fields a second time, keeping it just short of a 5.

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 3-param read tool with a full output schema, the description supplies everything else an agent needs: cost, error-billing behavior, auth constraints, and the routing alternative. Nothing material is missing.

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 coverage is 100%, so the baseline is 3, but the description adds meaning the schema cannot express: that there are deliberately no filter/paging/sort parameters and why. It does not re-explain geo or max_reviews, which the schema already documents well.

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?

States a specific verb and resource ('returns the review block for one ASIN') and enumerates exactly what comes back: star rating, rating count, 'customers say' summary, and review list. It also implicitly differentiates from siblings by pointing to product_details for per-star histogram data.

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?

Explicitly scopes usage ('one ASIN', 'takes no filters') and enumerates the alternatives that do not exist (no paging, star filter, or sort), explaining why. It routes the agent to product_details for per-star counts, giving a concrete when-to-use-other-tool rule.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.