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Glama

Easyparser — Amazon Product & Seller Data

get_seller_profile

Read-only

Unmask the legal identity and reputation of any Amazon merchant: legal business name, registered business address, and granular feedback trends over 30/90/365 days and lifetime. This is the supplier-verification and brand-protection tool.

Use this tool when the user asks 'who is this seller?', 'is this merchant legitimate?', or needs a supplier's legal entity for compliance or invoicing. To see WHAT the seller sells, use get_seller_products. To read individual buyer reviews about the seller, use get_seller_feedback.

Provide EITHER seller_id OR url — never both. You can discover a product's seller by calling get_product_offers first. Costs 1 credit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoFull Amazon seller profile or storefront URL (e.g. https://www.amazon.com/sp?seller=A1MCYUGJD2ILFU).
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
languageNoLanguage code for the Amazon page, in locale format (e.g. en_US, de_DE, tr_TR). Affects the language of titles, descriptions and reviews in the response.
seller_idNoAmazon's unique seller identifier (e.g. A1MCYUGJD2ILFU). Found in seller profile URLs as the 'seller=' or 'me=' parameter.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so no contradiction. The description adds useful behavioral context beyond annotations: the operation costs 1 credit, and the caller must supply exactly one of seller_id or url. It does not detail error behavior or pagination, but the core safety profile is covered by 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?

The description is compact and front-loaded with the tool's core output, then usage guidance, then the critical mutual-exclusion constraint and credit cost. There is no filler or repetition; every sentence contributes to selection or invocation.

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 4-parameter tool with no output schema, the description covers what is returned, when to use it, which alternatives to prefer, the required argument pattern, and the cost. Nothing an agent needs for ordinary invocation is missing.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful parameter relationship guidance: seller_id and url are mutually exclusive, and get_product_offers can resolve a seller_id. This goes beyond the per-property schema descriptions.

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 resource (Amazon merchant identity) and returns legal business name, address, and feedback trends. It explicitly positions itself as the supplier-verification tool and distinguishes itself from get_seller_products and get_seller_feedback, so an agent can select it correctly.

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?

It explicitly states when to use the tool ('who is this seller?', 'is this merchant legitimate?', compliance/invoicing) and names alternatives for adjacent tasks (get_seller_products for items, get_seller_feedback for individual reviews). It also gives an input rule and points to get_product_offers as a way to discover the seller_id.

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.