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Glama

Easyparser — Amazon Product & Seller Data

get_product_offers

Read-only

Fetch the list of seller offers for an Amazon product: every merchant selling the item with their price, shipping cost, condition (new/used), fulfillment type (FBA/FBM), and seller rating. Also returns the Buy Box winner. This is the tool for competitive price analysis, reseller monitoring, and availability tracking.

Use this tool when the question is about WHO sells the product and at WHAT price. If you need the product's own details (title, images, rating), use get_product_detail instead.

Each requested page costs 1 credit. Start with a single page (default) and only increase max_page if the user needs the full offer list. Use the condition and shipping filters to narrow results instead of paginating — it is cheaper.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asinYesAmazon Standard Identification Number — a 10-character alphanumeric product identifier (e.g. B0CJB6V2L5). Found in the product URL after /dp/ or /gp/product/.
primeNoIf true, return only Prime-eligible offers.
domainNoAmazon marketplace domain extension. Determines the regional Amazon site the data is fetched from. Use the domain that matches the user's market — prices, availability and rankings differ across marketplaces..com
max_pageNoEnding page number. Each page costs 1 credit; max 5 pages per request. Default 1.
min_pageNoStarting page number. Default 1.
conditionNoFilter offers by item condition. Omit to include all conditions.
free_shippingNoIf true, return only offers with free shipping.

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond the schema: each page costs 1 credit, pagination has a max of 5 pages, and filtering is cheaper than paginating. It doesn't describe the response shape, but with no output schema and read-only annotations, the credit-cost disclosure is valuable context.

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?

The description is well-structured: a clear first sentence, a second sentence explaining purpose, a third routing sentence, and a cost-guidance paragraph. It is slightly longer than necessary, but every sentence earns its place—especially the credit-cost and pagination guidance, which is essential for safe cost-conscious invocation.

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?

For a read-only list tool with 7 parameters and no output schema, the description covers what the tool returns, when to use it, and the cost model. It does not explicitly describe the response format, but given the output schema absence and the listing nature of the tool, the description is complete enough for correct invocation. Minor gap: it doesn't mention what happens when no seller offers exist or whether pagination is per-page sequential.

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. The description adds semantic meaning by framing the credit cost per page and recommending filters instead of pagination, which directly informs how to set max_page and condition/shipping parameters. It also clarifies the domain principle ('use the domain that matches the user's market'), though that already appears in the schema. The description reinforces the filter behavior and page-cost tradeoff beyond the schema's raw field definitions.

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 ('Fetch the list of seller offers for an Amazon product') and enumerates exactly what data is returned: merchant price, shipping cost, condition, fulfillment type, seller rating, and Buy Box winner. It also explicitly distinguishes itself from get_product_detail, which is the sibling it could be confused with.

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

Usage Guidelines5/5

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

Provides explicit when-to-use guidance ('when the question is about WHO sells the product and at WHAT price') and names the alternative (get_product_detail) with clear exclusion criteria. It also gives cost-conscious guidance on pagination and filtering, which is actionable and specific.

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

A4.4/5.0
Disambiguation5/5

Each tool maps to a distinct resource or action, and overlapping data is carefully disambiguated in the descriptions—e.g., get_product_detail includes BSR and dimensions, but get_bestseller_rank and get_package_dimensions are explicitly positioned as narrower alternatives. The bulk-job tools also form a clear pipeline with no realistic confusion between listing jobs, inspecting items, fetching item data, and checking webhook logs.

Naming Consistency4/5

The dominant get_* pattern is consistent for data retrieval, and list_* is used for collection-style endpoints. Minor deviations like check_credits, lookup_product, and search_products are understandable but break the strict verb_noun consistency enough to prevent a perfect score.

Tool Count4/5

At 17 tools, the server is slightly above the ideal 3-15 range, but the count is justified by the breadth of the domain: product details, offers, sales history, seller intelligence, bulk job monitoring, account credits, and error logs. Each tool earns its place, and the heavier count does not feel bloated.

Completeness4/5

Real-time product and seller data coverage is strong, including search, barcode lookup, product detail, offers, BSR, dimensions, sales history, seller profile, seller products, and seller feedback. The main gap is that bulk jobs can be listed and inspected but there is no tool to create or submit a new bulk job from the MCP server, leaving that workflow incomplete.