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Apiguru Amazon Data

Product detail for up to 20 ASINs in one call

product_details_batch
Read-onlyIdempotent

Batch variant of product_details. Accepts a comma-separated ASIN list, deduplicates it, and fetches all of them concurrently. Far cheaper and faster than N single calls. Price: $0.008 per item (max 20). Billed per ASIN processed, including ones that come back not-found. More than 20 ASINs returns 413. 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
asinsYesComma-separated ASIN list, maximum 20 after de-duplication. Each must be 10 uppercase alphanumeric characters.
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
resultsNo
request_idNo
country_codeNo
response_timeNo
amazon_request_countNo
billable_requests_countNo

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description discloses deduplication, concurrent fetching, per-ASIN billing including not-found results, the 413 limit beyond 20 ASINs, the multi-variant listing caveat for bullets/specs, and the meaning of null fields. This is far more behavioral transparency than annotations alone provide.

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?

Every sentence carries distinct operational value: batch identity, concurrency, cost, billing edge case, limit behavior, data caveat, and null-field interpretation. The description is compact relative to the amount of behavior it discloses and front-loads the most important facts.

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?

The description, combined with the detailed schema and annotations, covers what the tool does, when to use it, its limits, its costs, edge-case behaviors, and returned-field semantics. With an output schema present, return-value details are already handled, so no meaningful operational gap remains.

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%, so the baseline is 3; the description adds meaningful semantics by explaining deduplication and the 20-ASIN maximum, the not-found billing consequence, and the semantic caveat that bullets/specs may describe the product family rather than the exact variant. This enriches the parameter meaning without contradicting 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?

The description states a specific verb and resource: 'Batch variant of product_details', 'Accepts a comma-separated ASIN list... fetches all of them concurrently.' It clearly differentiates itself from product_details and other siblings by focusing on the batch ASIN capability rather than reviews, sellers, or offers.

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 context: use this batch variant when you need multiple ASINs, and it is 'far cheaper and faster than N single calls.' It names product_details as the base variant, implying single-ASIN use is routed there, though it does not explicitly state when-not or list alternative tools for overlapping needs.

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.