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Glama

Apiguru Amazon Data

by app.apiguru

Products listed by a seller

seller_products
Read-onlyIdempotent

Returns the paginated catalogue of products offered by a given seller storefront. Price: $0.01 per call. Unlike seller_profile_batch, seller_id format is not pattern-validated here.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoMarketplace country code.US
pageNoResult page, 1-based.
limitNoHow many the seller's products 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.

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

A3.8/5.0
Behavior4/5

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

Annotations already cover safety with readOnlyHint, idempotentHint, and destructiveHint=false. The description adds meaningful behavioral context beyond those: a $0.01 per-call cost, paginated results, and the fact that seller_id is not pattern-validated unlike seller_profile_batch. This is useful extra transparency without contradicting the annotations.

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 three sentences with no filler. It front-loads the core purpose, then adds the cost and a single high-value caveat about seller_id validation. Every sentence earns its place.

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?

The tool has six parameters, a rich input schema, an output schema, and strong safety annotations. The description adds price, pagination, and a validation caveat, covering the main behavioral facts an agent needs. It is missing explicit routing to sibling tools like offers_stock, but the title and purpose statement are sufficient in combination with the structured metadata.

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%, and the parameter descriptions are already rich: limit explains 0=all and truncation behavior, compact explains the size tradeoff, and fields describes _omitted_fields. The tool description adds no parameter-specific semantics, so the baseline of 3 applies because the schema carries the burden.

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 paginated catalogue of products offered by a given seller storefront.' This clearly identifies what the tool does and who the data belongs to. It also distinguishes itself from seller_profile_batch by noting the seller_id validation difference, which supports sibling differentiation.

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

Usage Guidelines2/5

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

The description does not state when to choose this tool over alternatives such as offers_stock, product_details, or search. The only comparison is a validation caveat about seller_profile_batch, which is a behavior nuance rather than usage guidance. No explicit 'use this when' or 'use that instead' guidance is provided.

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.

Resources