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Glama

Easyparser — Amazon Product & Seller Data

get_package_dimensions

Read-only

Get a product's precise physical and logistical attributes: package height, width, length, weight, and its Amazon fee category (e.g. Computers, Apparel). This is the FBA fee-estimation and shipping-cost tool.

Use this tool when the question is about shipping, storage, FBA fees, or warehouse planning. Do NOT use it for general product info — get_product_detail includes a human-readable dimensions string, but only THIS tool returns the structured fee category and exact measurements needed for fee calculation. Costs 1 credit.

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/.
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

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds valuable context beyond that: it costs 1 credit, it returns structured fee-category data, and it is the fee-calculation source rather than a human-readable description. No contradiction with annotations.

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?

Every sentence earns its place: the first defines the resource, the second provides the routing decision with an explicit alternative, and the third states cost. It is front-loaded and free of redundancy.

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?

The description covers what the tool returns, when to use it, what to avoid, the key alternative, and the cost. With no output schema, it lists the return attributes but omits units of measurement and error behavior; however, these are minor gaps given the tool's simple read-only nature.

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?

The schema already provides 100% coverage for both parameters, including ASIN format and domain enum/default. The description adds no parameter-specific semantics beyond reiterating the tool's purpose. Per the baseline rule for high schema coverage, a 3 is appropriate.

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 states a specific verb ('Get'), a precise resource ('a product's precise physical and logistical attributes'), and names the exact attributes: package height, width, length, weight, and Amazon fee category. It also positions the tool as the FBA fee-estimation and shipping-cost tool, clearly distinguishing it from get_product_detail, which returns a human-readable dimensions string.

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?

The description explicitly says when to use the tool ('when the question is about shipping, storage, FBA fees, or warehouse planning') and when not to ('Do NOT use it for general product info'). It names the alternative tool (get_product_detail) and explains the differentiator, giving an agent a concrete decision rule.

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.