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amazon-product-research-mcp

watchlist_delta

Read-only

Show what changed on the user's sourcing watchlist since last check. Returns each tracked ASIN with its score delta plus product identity (brand, title, price or price range) and fulfillment (FBA/FBM/AMZ with amz/fba share). Use when the user asks 'what changed on my watchlist', 'watchlist updates', 'any changes this week', or any watchlist-status question.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asinNoExact ASIN match.
limitNo
max_priceNo
min_priceNo
since_daysNoHow far back to look for changes (default 7, max 30).
asin_containsNo
product_brandNoExact product brand (case-insensitive).
fulfillment_inNoComma-separated FBA/FBM/AMZ to keep.
marketplace_idNoMarketplace to scope to: 1 = Amazon UK, 2 = Amazon US (default), 3 = Walmart US, 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. One marketplace per call.
max_score_deltaNo
min_score_deltaNo
max_current_scoreNo
min_current_scoreNoOnly tracked ASINs with current sourcing score >= this.
max_previous_scoreNo
min_previous_scoreNo
product_brand_containsNo
product_title_containsNo
max_fulfillment_amz_dom_pctNo
max_fulfillment_fba_pen_pctNo
min_fulfillment_amz_dom_pctNo
min_fulfillment_fba_pen_pctNo

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already mark this as readOnly and non-destructive. The description adds meaningful behavioral context by stating the tool returns per-ASIN score deltas, product identity, and fulfillment details with AMZ/FBA share, and it implies a persistent 'last check' reference. There is no contradiction with the annotations.

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 exactly two sentences: the first front-loads what the tool does and what it returns, the second lists concrete trigger phrases. There is no filler or redundancy, and every sentence earns its place.

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 tool with 21 optional parameters and no output schema, the description provides only a high-level output summary and usage triggers. It does not clarify default behavior, how 'since last check' relates to the since_days parameter, or how filters combine, leaving notable ambiguity for an agent selecting or invoking the tool.

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 29% (6 of 21 parameters documented), and the description compensates for almost none of that gap. It does not explain any filters, defaults, or how parameters like max_fulfillment_amz_dom_pct or min_score_delta work; the only helpful hint is the phrase 'amz/fba share', which relates to fulfillment parameters.

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 uses a specific verb phrase ('Show what changed... since last check') and specifies the resource ('user's sourcing watchlist') and the returned data (score delta, product identity, fulfillment). It clearly conveys a delta/status read tool, though it does not explicitly differentiate itself from sibling tools like watchlist_diff or watchlist_webwide.

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 example user phrasings ('what changed on my watchlist', 'watchlist updates', 'any changes this week') and broadly covers watchlist-status questions. It does not mention when not to use it or point to alternatives, so it stops short of the full when/when-not guidance.

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.