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bestseller_list

Get top-selling products in a category from Amazon or Google Shopping. Returns ranked list with prices, ratings, and reviews. Great for market research.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results (1-20, default: 10)
sourceNoSource: 'amazon' (default), 'google', 'all'
countryNoCountry code (default: DE)
categoryNoProduct category (e.g. 'headphones', 'laptops', 'gaming mice') (required)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / limit / maximum
      Added value: +20
    • addedInput schema / properties / limit / minimum
      Added value: +1
  2. First observed

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the output ('ranked list with prices, ratings, and reviews') and external sources, but does not disclose whether the operation is read-only, requires authentication, has rate limits, or how it handles invalid categories or network errors. This leaves the agent without important behavioral context.

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 core action and scope. It avoids redundant details and includes only relevant claims about the return format and use case. Every sentence earns its place, and there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description conveys the primary purpose and output, but for a tool with 4 parameters, no annotations, and no output schema, it lacks some completeness. It does not clarify that 'category' is effectively required (despite schema saying 0 required), nor does it mention default behaviors or edge cases like empty results. The description is adequate but not exhaustive.

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?

Schema description coverage is 100%, so the schema already documents all four parameters with clear descriptions. The tool description adds no additional parameter semantics beyond pointing out 'category' as the filter. Since schema coverage is high, the baseline of 3 applies; the description does not enhance or clarify parameters further.

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's purpose: 'Get top-selling products in a category from Amazon or Google Shopping.' It uses a specific verb ('Get'), names the resource ('top-selling products'), and scopes it by source and category. This distinguishes it from sibling tools like product_search or compare_prices, which have different functions.

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

Usage Guidelines3/5

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

The description implies usage for market research ('Great for market research') but provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or exclusions, such as when to prefer product_search or compare_prices. The usage context is hinted but not fully developed.

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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