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amazon-product-research-mcp

top_expanding_operators

Read-only

Find sellers (operators) expanding into the most NEW brands in a recent window. Use when the user asks 'operators expanding into new brands', 'sellers growing fastest by brand count', 'who is moving into new brands this month', or any cross-cutting operator question without a specific seller named.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
seller_nameNoExact seller/operator name (case-insensitive).
window_daysNoDays back for 'new' brands (default 30, max 90).
marketplace_idNo1 = Amazon UK, 2 = Amazon US (default), 4 = Amazon CA, 5 = Amazon AU, 6 = Amazon DE, 7 = Amazon JP, 8 = Amazon IT, 9 = Amazon FR, 10 = Amazon ES, 11 = Amazon MX, 12 = Amazon BR
max_avg_ratingNo
min_avg_ratingNo
min_new_brandsNoMinimum new-brand count to surface (default 3).
max_total_asinsNo
min_total_asinsNo
max_total_brandsNo
min_total_brandsNo
max_avg_rating_countNo
min_avg_rating_countNo
seller_name_containsNo
max_new_brands_in_windowNo
max_operator_fba_share_pctNo
min_operator_fba_share_pctNo
max_total_observed_buybox_daysNo
min_total_observed_buybox_daysNo

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already mark the tool as read-only and non-destructive, so the description does not need to repeat that. It adds some context about the time window and the 'new brands' ranking criterion, but does not disclose sorting details, tie-breaking, or what the result list contains.

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 purpose, and uses example queries efficiently to clarify intent. Every clause contributes to selection or usage.

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?

For a read-only ranking tool, the description provides enough to invoke it with defaults, but with 19 parameters and no output schema it leaves substantial gaps about filtering semantics and returned fields. It is minimally viable but not thorough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 21%, so the description needed to compensate, but it does not explain most parameters. It only implies 'recent window' and 'NEW brands' conceptually; the many min/max filter parameters remain undocumented in both schema and description.

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 and resource: find sellers/operators ranked by how many NEW brands they expanded into in a recent window. It also distinguishes itself from per-seller tools by explicitly saying it applies to 'any cross-cutting operator question without a specific seller named.'

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

Usage Guidelines4/5

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

The description gives concrete example user phrasings and a clear routing condition: use when the question is cross-cutting and no specific seller is named. It does not name alternative sibling tools, but the exclusion of named-seller cases is enough to guide selection.

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

A3.5/5.0
Disambiguation2/5

The tool set is extremely granular, with multiple clusters that overlap in purpose (e.g., amazon_search_results/search_products/shopping_search; watchlist_delta/watchlist_diff; find_undercompeted_brands/category_undercompeted_brands; operator_new_brands/operator_new_on_brand). Although descriptions are detailed, the boundaries between many 'find opportunity' and 'watchlist change' tools are subtle enough that an agent could easily misselect.

Naming Consistency4/5

The vast majority follow a verb_noun snake_case convention with clear prefixes (asin_, brand_, category_, operator_, watchlist_, playbook_, find_, top_). A few noun-style exceptions (competitive_landscape, risk_assessment, brand_under_attack, buybox_loss_alert) break the pattern, but they are minor and do not obscure the overall scheme.

Tool Count1/5

With 82 tools, the server is far beyond the 50+ extreme threshold. Even though the domain is broad, many tools are highly granular variants (e.g., filter_brands_by_fba_share vs filter_operators_by_fba_share; watchlist_delta vs watchlist_diff) that could be merged or parameterized, imposing a heavy cognitive and context burden on agents.

Completeness5/5

The surface is extraordinarily complete for Amazon product research: discovery, ASIN/brand/category analytics, buybox and BSR history, sourcing evaluation, risk/MAP monitoring, watchlists, playbooks, operator intelligence, cross-marketplace checks, and live refreshes. Workflows like authorized_seller_set → buybox_loss_alert and watchlist_add → watchlist_delta are fully supported, with no obvious dead ends.