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amazon-product-research-mcp

watchlist_add

Create or add to a saved tracking list the user can monitor over time. list_type is one of asin, brand, seller, niche; name is the user's label for the list; items are the identifiers to track (ASINs, brand names, seller names, or niche keys). Captures a baseline of the current observed state so a later 'what changed' check can show new sellers and score moves. Use when the user says 'track these ASINs', 'add Nike to my brand watchlist', 'start monitoring ...'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe list's label (e.g. 'Q4 arbitrage candidates').
itemsYesIdentifiers to add.
list_typeYesasin | brand | seller | niche
marketplace_idNoMarketplace (2 = Amazon US).

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only convey readOnlyHint=false, openWorldHint=false, and destructiveHint=false. The description goes beyond this by disclosing a meaningful side effect: it captures a baseline of the current observed state so a later 'what changed' check can show new sellers and score moves. This is essential behavioral context that the annotations do not provide.

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?

Three sentences with no fluff. The main action is front-loaded, parameter semantics are summarized compactly, and the use triggers are placed at the end. Every sentence contributes to selection or invocation.

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

Completeness4/5

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

For a write tool with four parameters and no output schema, the description covers the action, parameter roles, and the baseline-capture behavior well. It does not describe the return value or what happens when a list with the same name already exists, but these are minor gaps given the clear trigger phrases and schema coverage.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds semantic value by explaining that name is the user's label, items are the identifiers being tracked, and it maps items to their corresponding list_type (ASINs, brand names, seller names, niche keys). It does not add much about marketplace_id, but the schema already documents that clearly.

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 identifies the verb ('Create or add') and resource ('saved tracking list'), and it distinguishes this tool from watchlist_remove, watchlist_list, watchlist_diff, and watchlist_add_rule by describing list creation and item tracking. It also enumerates the four list types and what they represent, leaving no ambiguity about what the tool operates on.

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 invocation triggers: 'track these ASINs', 'add Nike to my brand watchlist', and 'start monitoring ...'. It does not explicitly state when not to use this tool or name alternatives like watchlist_remove or watchlist_add_rule, but the context is clear enough for an agent to select it over siblings.

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.