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Glama

Apiguru Amazon Data

by app.apiguru

Best-seller rankings for a category

best_sellers
Read-onlyIdempotent

Returns the current Amazon best-seller list for a category, with optional subcategory drill-down and pagination. Price: $0.01 per call. No required parameters - calling it bare returns US appliances page 1.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoMarketplace country code.US
pageNoResult page, 1-based.
limitNoHow many ranked 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).
categoryNoCategory slug, lowercased by the server. Defaults to 'appliances'.appliances
subcategory_codeNoOptional subcategory node id to drill into.

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

A4.3/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds valuable behavioral details beyond those annotations, including the $0.01 per-call cost, the existence of pagination and subcategory drill-down, and the exact default bare-call result. This is useful context that annotations alone do not convey.

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 concise sentences with no filler: purpose, cost, and default behavior. It is front-loaded with the core action and resource, and every sentence adds distinct information that helps the agent decide and invoke correctly.

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 input schema (100% parameter coverage), existing output schema, and comprehensive annotations, the description is complete enough for an agent to understand what the tool does, what a default call returns, and how pagination/subcategory drill-down work. No critical operational context is missing.

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 input schema already documents all seven parameters in detail. The description adds only a high-level summary of the default call rather than deeper parameter semantics, which is acceptable given the schema's completeness. The baseline of 3 applies.

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 states a specific verb ('Returns'), a clear resource ('current Amazon best-seller list for a category'), and key scope modifiers ('optional subcategory drill-down and pagination'). This clearly distinguishes it from sibling tools like search, deals, and product_details, which serve different purposes.

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?

The description gives practical usage context by noting 'No required parameters - calling it bare returns US appliances page 1,' which tells an agent exactly what a minimal call will do. It does not explicitly name alternatives or when-not-to-use conditions, but the intended use case is clear from the description and title.

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