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Amazon customer reviews of a product

amazon-reviews
Read-only

Amazon product reviews API for AI agents: send an ASIN or an Amazon product URL and get up to 50 customer reviews as JSON: rating, title, text, date, verified-purchase flag and helpful votes. Sort by recent or helpful, filter by stars, and pick the marketplace (US, UK, DE, FR, IT, ES, CA, JP). Reviewer names and photos are not returned. A product with no reviews is not charged.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoorder of the reviewsrecent
limitNonumber of reviews wanted, 1 to 50 (default 10). A larger value is lowered to 50 and 0 or a negative one means the default; the quote follows the value used.
starsNokeep only reviews with this many stars (5 to 1), the positive ones (4 and 5) or the critical ones (1 to 3)all
countryNoAmazon marketplace of an ASIN (a URL sets its own; a different value is refused)us
productYesan ASIN (10 characters, e.g. B079JLY5M5) or an Amazon product URL (.../dp/ASIN) on amazon.com, .co.uk, .de, .fr, .it, .es, .ca or .co.jp; a URL sets the marketplace

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the read-only, non-destructive, open-world profile, and the description adds genuinely new behavioral facts: the 50-review ceiling, that reviewer names and photos are omitted, and that a product with no reviews is not charged. The billing/charging behavior is especially useful given idempotentHint=false, though the cost model itself is only hinted at.

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?

A single front-loaded sentence pair that leads with the core action and payload, then packs sort/filter/marketplace options and exclusions without redundancy. Every clause carries information an agent needs.

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 no output schema, the description compensates by enumerating returned fields (rating, title, text, date, verified flag, helpful votes) and explicitly noting what is not returned. Combined with billing and filtering behavior, an agent has everything needed to call it correctly.

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 all five parameters are already fully documented in the schema, including the URL-sets-marketplace rule and the 1-50 clamping behavior. The description only restates the high-level capability (sort, star filter, marketplaces) without adding syntax or edge-case meaning beyond the schema, so the baseline 3 applies.

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?

States a specific verb and resource ('send an ASIN or an Amazon product URL and get up to 50 customer reviews as JSON') and enumerates the returned fields. It is unambiguously distinguishable from sibling review tools like google-maps-reviews or youtube-comments, which target other platforms.

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?

Usage context is clear: use it to fetch customer reviews for an Amazon product, with sort/filter/marketplace options spelled out. There is no explicit when-not or named alternative, though among the siblings none competes for Amazon review data, so the gap is minor.

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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