Skip to main content
Glama

amazon-product-research-mcp

by com.webotee

keyword_cross_marketplace

Read-only

Check which of the 11 Amazon marketplaces a keyword was observed in Amazon's own autocomplete suggestions, with first/last observed dates and an example current suggestion-list position per marketplace. Use when a seller asks 'does anyone type X on Amazon Germany/Japan/…', compares keyword presence across countries, or plans a marketplace expansion (pair with brand_xmarket / operator_xmarket_presence). Exact-keyword match — not volumes, not rankings. Amazon marketplaces only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordYesThe exact keyword to check (e.g. 'electric toothbrush').

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark this as read-only and non-destructive, and the description adds meaningful behavioral detail beyond that: it reveals the data source is Amazon's autocomplete suggestions, the match is exact, and it returns per-marketplace observed dates and an example suggestion-list position. It also scopes the data to Amazon marketplaces only.

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 front-loaded with the core function in the first sentence, followed by usage guidance and constraints. Every sentence serves a purpose: what, when, and what-not.

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

Completeness5/5

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

Despite having no output schema, the description adequately sets expectations by mentioning observed marketplaces, first/last observed dates, and an example current suggestion-list position. It covers selection context, limitations, and sibling relationships, making it complete for an agent to invoke correctly.

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?

The schema already fully documents the single 'keyword' parameter, so the baseline is 3. The description adds value by clarifying the exact-match behavior and giving concrete usage context, though it doesn't need to add much since coverage is 100%.

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?

States a specific verb and resource: 'Check which of the 11 Amazon marketplaces a keyword was observed in Amazon's own autocomplete suggestions.' It also clearly distinguishes from sibling tools by specifying exact-keyword matching and explicitly excluding volumes and rankings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use scenarios: a seller asking if a keyword is typed on a specific marketplace, cross-country keyword presence comparison, and marketplace expansion planning. It names related sibling tools (brand_xmarket / operator_xmarket_presence) and states exclusions: exact match only, not volumes/rankings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All amazon-product-research-mcp toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources