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amazon-product-research-mcp

map_violations_today

Read-only

Show active MAP (Minimum Advertised Price) violations for products in the workspace. Use when the user asks 'MAP violations', 'who is selling below MAP', 'price violations today', 'are there any MAP breaches', or any MAP-enforcement question.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNoFilter to a specific brand. Omit for all workspace brands.
limitNo
last_seen_toNo
first_seen_toNo
retailer_nameNoExact retailer name (case-insensitive).
brand_containsNoSubstring match on brand (use `brand` for an exact match).
last_seen_fromNo
first_seen_fromNoYYYY-MM-DD.
retailer_domainNoExact retailer domain (case-insensitive).
max_map_floor_usdNo
max_violation_pctNoMaximum violation percentage below MAP.
min_map_floor_usdNo
min_violation_pctNoMinimum violation percentage below MAP (default 0, meaning any violation).
max_observed_price_usdNo
min_observed_price_usdNo
product_title_containsNo
retailer_name_containsNo
retailer_domain_containsNo

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, so the safety profile is covered. The description adds the 'active' and workspace-scope context, but leaves important behavior vague: what 'active' means, whether 'today' is a hard time window, and what the result looks like. This is acceptable but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first states purpose, the second gives concrete user-phrase triggers. It is front-loaded and generally efficient, though the list of example queries is slightly redundant with the closing 'any MAP-enforcement question.'

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 18 optional filters, no output schema, and sparse parameter descriptions, this description is incomplete for confident invocation. It covers purpose and routing, but not result contents, filter combination, date formats, pagination, or the exact semantics of 'active' and 'today.'

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?

The tool has 18 optional parameters but only 39% schema description coverage. The description provides no parameter-level guidance at all—no mention of filtering by brand, retailer, date, price, violation percentage, or product title. It does not compensate for the large undocumented filter surface.

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 the operation ('Show'), the resource ('active MAP violations'), expands the acronym, and scopes it to 'products in the workspace.' It is specific and easy to distinguish from unrelated sibling tools, though it does not explicitly name a contrasting sibling.

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 provides explicit trigger phrases ('MAP violations', 'who is selling below MAP', 'price violations today', 'are there any MAP breaches') and generalizes to 'any MAP-enforcement question,' giving clear routing guidance. However, it does not mention when not to use the tool or name an alternative, so it stops short of a 5.

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.