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Glama

Apiguru Amazon Data

by app.apiguru

Full product detail for a single ASIN

product_details
Read-onlyIdempotent

Fetches the complete product record for one ASIN on one marketplace: title, price, star rating, rating count, images, description, feature bullets, variations and category. Price: $0.01 per call. 404 means the ASIN is absent from that marketplace and IS billed. 503 means our fetch failed and is NOT billed - retry. Bullet points and specs are what Amazon shows for the listing; on multi-variant listings they can describe the product family rather than the exact variant. A null field means Amazon did not show it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoMarketplace country code.US
asinYesSingle Amazon ASIN, 10 uppercase alphanumeric characters. Exactly one - comma-separated lists are rejected; use product_details_batch for many.
fieldsNoComma-separated top-level fields to return instead of the compact set, e.g. "tech_specs,product_information". Any response lists what it left out under _omitted_fields.
compactNoReturn the compact record (about 4 KB: identity, price, rating, availability, bullets, category, offer, buy box). false returns the full record (about 75 KB, includes from_manufacturer, tech_specs, product_information, product_reviews).

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

A4.5/5.0
Behavior5/5

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

Beyond the read-only, idempotent, open-world annotations, the description discloses per-call pricing, billing consequences of 404s, retry behavior for 503s, the multi-variant caveat about bullets/specs, and the meaning of null fields. This is exactly the behavioral context an agent needs to set expectations and handle failures.

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?

Each sentence earns its place: scope and data list, cost, billing/error semantics, data-acuracy caveat, and null-field meaning. The most important information is front-loaded, and the prose is dense without being verbose.

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?

With an output schema available, annotations covering safety and idempotence, and parameters fully documented in the schema, the description supplies the missing operational pieces: cost, billing on 404, retry on 503, multi-variant semantics, and null meaning. Nothing essential for using the tool correctly is left out.

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?

The input schema already covers all four parameters fully: asin has a pattern and format, geo has an enum, fields has an example, and compact describes sizes and outputs. The description does not add new parameter-level meaning beyond confirming the single-ASIN scope, so the schema-heavy baseline of 3 is appropriate.

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 clearly states the tool fetches the complete product record for one ASIN on one marketplace, and it enumerates the data returned: title, price, rating, images, bullets, variations, category. The 'one ASIN' scoping distinguishes it from batch and search tools, and the title reinforces the purpose.

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 clear operational guidance: it works on a single ASIN per marketplace, and it explains when to retry (503, not billed) versus when not to (404, billed). It does not explicitly name alternatives such as product_details_batch for multiple ASINs, but the single-ASIN framing and error-handling instructions make the intended use clear.

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