apiguru-amazon-data

Seller profiles for up to 10 seller IDs

seller_profile_batch

Read-onlyIdempotent

Retrieve storefront profiles for up to 10 Amazon seller IDs in one call, including business name, rating, feedback counts, address, and marketplace presence.

Instructions

Returns the storefront profile for each seller id: business name, rating, feedback counts, address and marketplace presence. Price: $0.0036 per item (max 10). Seller ID validation is all-or-nothing: one malformed id rejects the entire request with 400. Every row in results is an object with status: ok (the profile), not_found (Amazon has no page for that id on this marketplace; billed, like a 404) or unavailable (Amazon served nothing usable on any route; NOT billed on the keyed path, retryable: true). A row is never null. billable_requests_count counts ok + not_found rows; on the pay-per-call rail the per-item quote is settled up front, so retry unavailable ids in a separate call rather than expecting a partial refund.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`geo`	No	Marketplace country code.	US
`seller_ids`	Yes	Comma-separated seller IDs, maximum 10. Each must be 13-15 alphanumeric characters or the whole call 400s.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`note`	No
`results`	No
`request_id`	No
`unavailable`	No
`country_code`	No
`response_time`	No
`amazon_request_count`	No
`billable_requests_count`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 27, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Goes well beyond the read-only/idempotent annotations: discloses per-item pricing ($0.0036, max 10), all-or-nothing ID validation with a 400, the three-valued `status` semantics, which statuses are billed, that rows are never null, and how billable_requests_count is computed. This is exactly the billing/error context annotations cannot carry.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads what is returned and then layers billing and status semantics; every clause carries information. Slightly dense with a long final sentence about the pay-per-call rail, but no filler.

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?

For a paid, validated batch tool with two params and an output schema, the description supplies the pricing, validation failure mode, row-status contract, and retry strategy an agent needs. Nothing material is missing, and it does not waste space re-explaining the output schema's fields.

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 coverage is 100%, so the schema already documents both params including the 13-15 char pattern and max-10 constraint. The description restates the all-or-nothing validation rule but adds no syntax or format detail beyond the schema, so baseline 3 applies; `geo` is never discussed.

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?

Names a specific verb+resource+scope: returns storefront profiles for up to 10 seller ids, and enumerates the fields returned (business name, rating, feedback counts, address, marketplace presence). An agent can distinguish this batch profile lookup from seller_products and seller_reviews without inspecting schemas.

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?

Gives concrete operational guidance: retry `unavailable` ids in a separate call rather than expecting a refund, and explains when `not_found` vs `unavailable` occur. It does not name alternative sibling tools (e.g. seller_reviews for review data), so it stops short of explicit when-not guidance.

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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