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Glama

Apiguru Amazon Data

by app.apiguru

Live offers and inventory for up to 10 ASINs

offers_stock
Read-onlyIdempotent

Returns the current offer list per ASIN (seller, price, condition, buy-box winner) and, optionally, the actual purchasable stock quantity. Price: $0.015 per item (max 10). Billed per upstream Amazon request, which is more than one per ASIN when check_inventory is true. /scrape is a legacy alias for the same handler.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoMarketplace country code.US
asinsYesComma-separated ASIN list, maximum 10. Each must be 10 uppercase alphanumeric characters; malformed entries are rejected with 400.
conditionNoComma-separated condition filter. Any of ALL, NEW, USED_LIKE_NEW, USED_VERY_GOOD, USED_GOOD, USED_ACCEPTABLE. Unrecognised values silently fall back to ALL.
offers_countNo'all' for every offer, 'winner' for the buy-box offer only, or a specific alphanumeric Offer ID.all
check_inventoryNoResolve the true purchasable stock quantity. Slower and bills more upstream requests, so leave off unless you need the number.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsNo
request_idNo
country_codeNo
response_timeNo
amazon_request_countNo

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?

Annotations already mark it read-only and idempotent, and the description adds valuable behavioral context: per-item pricing, upstream request billing, extra upstream requests when check_inventory is true, and the /scrape legacy alias. This goes beyond what annotations or schema 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?

Three dense sentences with no fluff, opening with the core return value and then covering cost, billing behavior, and the legacy alias. Every sentence adds actionable information.

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 read-only annotations, full schema coverage, and an output schema, the description supplies the missing operational context: pricing, upstream requests, and the optional inventory mode. An agent has enough to invoke and interpret the tool correctly.

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, but the description adds meaning by explaining the cost and latency implication of check_inventory and reinforcing the maximum of 10 ASINs. It also clarifies the optional, purchasable-stock intent beyond the schema's field label.

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 uses a specific verb and resource: 'Returns the current offer list per ASIN' with seller, price, condition, and buy-box winner, plus optional inventory. It is clearly distinct from siblings like product_details or search, and the title reinforces the 'up to 10 ASINs' scope.

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

Usage Guidelines3/5

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

The purpose statement makes it clear when offers/inventory data is needed, and the check_inventory guidance says to leave it off unless the stock number is required. However, it does not explicitly name alternative tools or state when not to use this tool versus siblings such as product_details_batch.

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