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Glama

Apiguru Amazon Data

by app.apiguru

Current Amazon deals with filters

deals
Read-onlyIdempotent

Returns active Amazon deals, filterable by category, brand, minimum star rating, price band, discount band, and Prime early access. Price: $0.01 per call. The bucket parameters are ordinal buckets, not literal prices or percentages. min_product_star_rating rejects 5.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoMarketplace country code.US
limitNoHow many deals 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.
brandsNoBrand filter.
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.
offsetNoPagination offset, non-negative.
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).
categoriesNoCategory filter.
price_rangeNoPrice band bucket 1-5, or ALL.
discount_rangeNoDiscount band bucket 1-4, or ALL.
prime_early_accessNoRestrict to Prime early access deals.
min_product_star_ratingNoMinimum star rating. Only 1, 2, 3, 4 or ALL are accepted - 5 is rejected with 400.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dealsNo
successNo
request_idNo
amazon_request_countNo

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, open-world, and non-destructive behavior. The description adds useful facts beyond that: the $0.01 per-call price, bucket parameters being ordinal rather than literal, and min_product_star_rating rejecting 5. No contradiction with 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?

Three short sentences with the main purpose front-loaded and no filler. Each sentence carries a distinct, useful fact: what the tool does, the cost, and key behavioral caveats.

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 comprehensive input schema, output schema, and rich annotations, the description covers the essential cross-cutting facts an agent needs (pricing, bucket semantics, rejection). Nothing necessary for correct invocation 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% and each parameter already has a clear description. The description adds a cross-cutting semantic clarification that bucket parameters are ordinal buckets, not literal prices or percentages, which applies to price_range, discount_range, and min_product_star_rating. This adds value beyond the schema.

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?

Description states a specific verb and resource: 'Returns active Amazon deals' and enumerates the available filters. This clearly identifies the tool's domain and distinguishes it from product, search, and seller siblings by focusing on deals.

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?

No guidance on when to use this tool versus alternatives like best_sellers or search. It does not state conditions, exclusions, or mention sibling tools at all. The only implicit context is that it returns deals, which is purpose, not usage guidance.

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

All Apiguru Amazon Data toolsInspect
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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