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amazon-product-research-mcp

find_deconcentrating_brands

Read-only

Find brands where buybox concentration dropped over the last quarter — more sellers are entering, creating opportunity for new entrants. Use when the user asks 'brands losing control', 'deconcentrating brands', 'brands opening up to competition', 'gentrification opportunities', or any question about brands becoming less monopolized.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNoExact brand match (case-insensitive).
limitNo
since_weeksNoWindow to compare (default 12, max 26). Compares first half vs second half.
brand_containsNo
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_asin_countNo
min_asin_countNo
min_buybox_daysNoMinimum observed buybox days to filter out inactive brands (default 30).
max_control_scoreNo
min_control_scoreNo
max_new_seller_countNo
max_old_seller_countNo
min_new_seller_countNo
min_old_seller_countNo
max_seller_growth_pctNo
min_seller_growth_pctNo

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral framing: it searches for buybox concentration dropping over a quarter and explains the competitive meaning. It does not disclose filtering behavior tied to params like control_score, seller counts, or ASIN count thresholds.

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 with no filler. The core purpose is front-loaded, and the usage triggers are placed in a compact second sentence.

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

Completeness2/5

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

For a tool with 16 optional parameters, low schema coverage, and no output schema, the description is too thin to let an agent invoke it confidently. It gives the high-level idea and usage context but leaves most parameter semantics and output expectations unaddressed.

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

Parameters1/5

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

Schema description coverage is only 25%, and the description does almost nothing to compensate. It never explains control_score, new_seller_count, old_seller_count, seller_growth_pct, asin counts, brand_contains, or limit. The phrase 'more sellers are entering' hints at seller-growth semantics but does not define the actual parameters or their ranges.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb and resource: 'Find brands where buybox concentration dropped over the last quarter' and ties it to concrete user phrasings like 'brands losing control' and 'brands becoming less monopolized.' It does not explicitly differentiate this from siblings such as brands_gaining_sellers or brand_buybox_trajectory, so it stops short of full sibling differentiation.

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 explicit 'Use when' guidance with multiple indicative user queries, which tells an agent when to select this tool. However, it lacks any 'do not use when' guidance or references to alternative tools that might overlap, such as brands_gaining_sellers.

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.