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Glama

Apiguru Amazon Data

by app.apiguru

Feedback reviews for a seller

seller_reviews
Read-onlyIdempotent

Returns paginated seller feedback, optionally filtered to a star-rating window. Price: $0.01 per call. from_rating and to_rating are optional; omit both for unfiltered feedback.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoMarketplace country code.US
pageNoResult page, 1-based.
limitNoHow many seller reviews to return from this page (0 = all of them). A full page is up to 48 rows and about 54 KB, which most clients spill to a file instead of showing inline. The answer carries _truncated with the true count when it trims.
fieldsNoComma-separated row fields to return instead of the light set, e.g. "asin,product_title,product_price". Rows list what they left out under _omitted_fields.
compactNoReturn light rows: identity, price, rating, badges and one delivery_date, dropping the long delivery prose that repeats itself across three fields. false returns every field the REST API sends (roughly 3x the size).
seller_idYesAmazon seller ID. Required.
to_ratingNoUpper bound of the star-rating filter.
from_ratingNoLower bound of the star-rating filter.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
successNo
request_idNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds meaningful context beyond that: the per-call price ($0.01), pagination behavior, and the optional rating-window filtering. Pricing is a cost signal annotations cannot express, so this exceeds the baseline.

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?

Three terse sentences, front-loaded with the core purpose, then the price, then filter usage. Every sentence earns its place and there is no repetition of schema content. The description is appropriately sized for a tool whose parameter details live in a strong schema."

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

Completeness4/5

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

Given the 100% schema coverage, a rich output schema, and annotations covering the safety and idempotency profile, the description is complete enough for an agent to decide to invoke and understand the call shape. The only minor gap is the lack of explicit sibling routing, which the distinctive 'seller feedback' resource largely mitigates.

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 and the schema already documents all 8 parameters thoroughly. The description adds only a minor clarification about the from_rating/to_rating interaction (omitting both yields unfiltered feedback) which is not explicitly stated in the individual parameter docs but does not substantially exceed schema coverage.

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 first clause, 'Returns paginated seller feedback, optionally filtered to a star-rating window,' states a specific verb, resource, and scope. The phrase 'seller feedback' clearly distinguishes it from the sibling product_reviews, so an agent can tell them apart without opening the schema.

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

Usage Guidelines3/5

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

The description gives filter guidance ('from_rating and to_rating are optional; omit both for unfiltered feedback') and discloses pricing, but provides no explicit when-to-use versus alternatives. Routing is implied by the resource name rather than stated, and no exclusions are given relative to sibling tools like product_reviews or seller_products.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools target clearly distinct resources—product details, reviews, offers, sellers, deals—and the batch/meta tools are obvious. A couple of adjacent tools could be confused (product_details vs offers_stock, or search by seller vs seller_products), but the descriptions are detailed enough to prevent serious misselection.

Naming Consistency3/5

Names are all snake_case and use readable resource prefixes (product_*, seller_*), but the verb/noun pattern is inconsistent: bare plural nouns (best_sellers, deals), single verbs (search), and compound nouns (offers_stock, product_details_batch) are mixed. It is readable but not a uniform convention.

Tool Count5/5

Twelve tools is a well-scoped size for an Amazon data API. Each tool serves a distinct data-access or meta purpose with no obvious redundancy or bloat.

Completeness4/5

The toolkit covers the core Amazon data surface: product lookup (single/batch), discovery (search, best sellers, deals), offers/stock, reviews, and seller information. Minor gaps exist—no category-tree endpoint and review pagination is not explicit—but primary agent workflows are covered.

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