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Glama

Easyparser — Amazon Product & Seller Data

get_bestseller_rank

Read-only

Get a product's current Best Sellers Rank (BSR): its numerical rank in main categories and sub-category node identifiers. A lightweight, single-purpose tool for popularity and demand tracking.

Use this tool when you only need the rank — e.g. 'Is this product selling well?' or daily rank monitoring. Lower rank number means higher sales velocity. If you also need price, rating or content, use get_product_detail instead (it includes BSR along with everything else). For historical rank trends, use get_sales_history. 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

A4.5/5.0
Behavior4/5

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

Annotations already provide readOnlyHint/openWorldHint/destructiveHint, and the description adds useful behavioral context: the result reflects current rank, the tool is lightweight and single-purpose, rank semantics are inverted (lower is better), and it costs one credit. It does not contradict annotations and provides more than the minimum without needing to repeat schema information.

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, front-loaded with the core action, and every sentence is purposeful: current scope, output content, use cases, ranking direction, alternatives, and cost. It avoids redundancy and is easy to scan.

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 lightweight two-parameter tool with no output schema, the description is complete: it explains what is returned (rank and category node identifiers), how to interpret it, when to prefer alternatives, marketplace via domain, and cost. An agent has enough context to decide whether and how to invoke it.

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 input schema already covers both parameters with detailed descriptions (asin pattern/URL source and domain enum/marketplace guidance), so the baseline is 3. The description adds no extra meaning about parameter semantics beyond what the schema states; it mostly restates that the tool returns rank data.

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 opens with a specific verb and resource: 'Get a product's current Best Sellers Rank (BSR)', and immediately clarifies scope by mentioning numerical rank and sub-category node identifiers. It also differentiates from siblings by naming get_product_detail and get_sales_history as alternatives that cover broader detail or historical trends.

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 says when to use this tool ('when you only need the rank'), gives a concrete example ('Is this product selling well?'), clarifies the ranking direction ('Lower rank number means higher sales velocity'), and states exactly when to use get_product_detail or get_sales_history instead. It also adds cost awareness with 'Costs 1 credit'.

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.