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Glama

Apiguru Amazon Data

Seller profiles for up to 10 seller IDs

seller_profile_batch
Read-onlyIdempotent

Returns the storefront profile for each seller id: business name, rating, feedback counts, address and marketplace presence. Price: $0.012 per item (max 10). Seller ID validation is all-or-nothing: one malformed id rejects the entire request with 400.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoMarketplace country code.US
seller_idsYesComma-separated seller IDs, maximum 10. Each must be 13-15 alphanumeric characters or the whole call 400s.

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

A4/5.0
Behavior4/5

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

Annotations already declare read-only, non-destructive, idempotent behavior; the description adds valuable edge-case behavior (all-or-nothing validation with a 400 for any malformed ID) and the per-item price. This goes beyond what annotations capture.

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?

Two sentences with no filler: the first states output and content, the second states cost and the critical failure mode. Key information is front-loaded and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With a rich input schema, an output schema, and safety annotations, the description covers the important operational details: output fields, batch cap, cost, and failure mode. It is complete enough for correct invocation, though a note comparing it to seller_products/seller_reviews would round it 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?

Schema description coverage is 100%, so the schema already documents seller_ids, the 13-15 character rule, the max of 10, and the geo enum/default. The description mostly restates these facts without adding new parameter-level meaning.

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 names a specific verb and resource ('Returns the storefront profile for each seller id') and enumerates the exact payload fields (business name, rating, feedback counts, address, marketplace presence). This clearly distinguishes it from sibling tools like seller_products or seller_reviews.

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 description implies batch storefront-profile use and notes the 10-ID maximum and cost, but it provides no explicit when-to-use guidance or comparisons with alternatives such as seller_products or product_details_batch. Usage context is inferable, not stated.

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.