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amazon-product-research-mcp

my_products

Read-only

The seller's OWN connected Amazon catalog fused with Webotee market intelligence. Each product shows EXACT account data (your price, your inventory, FBA/FBM, listing status) alongside OBSERVED/ESTIMATED market data (sourcing score, the observed market buy-box price, how many sellers are on the listing, estimated 30-day units, cross-marketplace spread) and a price_vs_market read (below_market / at_market / above_market). Requires a connected store (Starter+). Use when the seller asks about their own products, 'how am I priced vs the market', 'which of my products are under pressure', or 'show my catalog'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoOrdering: score (default), undercut, price, inventory.score
brandNoOptional — only products of this brand.
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

TDQS

A4.2/5.0
Behavior4/5

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 a prerequisite (connected store Starter+) and a detailed breakdown of output categories: account data (price, inventory, FBA/FBM, status), observed/estimated market data, and price_vs_market classification. This goes beyond 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 opens with the core concept, then enumerates the output fields, then states the prerequisite and use-case triggers. Every sentence carries distinct value and it is compact for the information conveyed.

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 read-only list tool with no required parameters, the description adequately covers purpose, output fields, prerequisites, and example queries. There is no output schema, but the description enumerates the main data categories. Minor gaps are pagination/empty-state behavior and interpretation of 'sourcing score', but these are not critical for invocation.

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

Parameters3/5

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

Schema description coverage is 100% and the three optional parameters are fully documented with defaults, enums, and marketplace ID meanings. The description adds no parameter-specific semantics beyond what the schema already provides, so the baseline of 3 applies.

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 resource — the seller's own connected Amazon catalog — and what it does: shows exact account data alongside market intelligence and a price_vs_market read. The 'OWN' and 'your price, your inventory' wording clearly distinguish it from sibling tools like my_competitors or search_products.

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?

Explicit trigger phrases are given: 'Use when the seller asks about their own products, how am I priced vs the market, which of my products are under pressure, or show my catalog.' It also notes the Starter+ connected-store requirement. It doesn't name an alternative or state when not to use it, but the use cases are unambiguous.

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.