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Amazon Product Offers & Buy Box

ecommerce.amazon.offers
Read-onlyIdempotent

Compare Amazon product offers from third-party sellers: view prices, conditions, seller ratings, Buy Box status, and delivery estimates across marketplaces.

Instructions

Get all third-party seller offers for an Amazon product — price, condition (new/used), seller name & rating, Buy Box winner flag, Fulfilled by Amazon, delivery estimate. Price comparison across sellers (Canopy API)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asinYesAmazon ASIN product ID. Returns all third-party offers with Buy Box winner, seller ratings, delivery info
domainNoAmazon marketplace: US, UK, CA, DE, FR, IT, ES, AU, IN, MX, BR, JP (default US)US

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.

Schema Changelog

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

  1. Addedv1.5.0
  2. Removedv1.0.20
  3. Addedv1.0.19

TDQS

A4.3/5.0
Behavior3/5

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

Annotations (readOnlyHint, idempotentHint, etc.) already convey the non-destructive, idempotent nature. The description adds context about third-party offers and the Canopy API source but does not disclose behavioral traits beyond what annotations provide, such as rate limits or authentication needs.

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?

The description is two sentences, front-loaded with the most important information, and contains no extraneous words. Every sentence adds value.

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 output schema exists, the description sufficiently covers the key aspects of what the tool returns. It also aligns with annotations. There are no obvious gaps for a data retrieval tool of this complexity.

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?

Both parameters (asin, domain) are described in the schema with 100% coverage. The description adds extra meaning by enumerating the return fields (Buy Box winner, seller ratings, delivery info), enhancing understanding beyond the schema alone.

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 clearly states the tool retrieves all third-party seller offers for an Amazon product, listing specific data fields (price, condition, seller, buy box). It distinguishes itself from siblings like ecommerce.amazon.product (product details) and ecommerce.amazon.search (product search) by focusing on offers and price comparison.

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 implies usage for price comparison across sellers and offer details, but lacks explicit when-to-use or when-not-to-use guidance compared to sibling tools. However, the context is clear enough for an agent to infer appropriate use cases.

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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