Check whether an operator sells on Amazon US, Amazon UK, and/or Walmart. Returns per-marketplace brand count, ASIN count, and observed buybox days. Use when the user asks 'does this seller sell on Walmart too', 'cross-marketplace presence', 'is this operator on Amazon UK', or any multi-marketplace operator question.
| Name | Required | Description | Default |
|---|---|---|---|
| operator_name | Yes | Seller/operator name. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a safe read-only operation (readOnlyHint=true, destructiveHint=false). The description adds meaningful context about scope and output: per-marketplace brand count, ASIN count, and observed buybox days. It does not discuss data recency or interpretation caveats, but given the strong annotation coverage, this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. The main action and scope are front-loaded, followed by return values and concrete example queries. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter read-only lookup with no output schema, the description is complete: it states what marketplaces are covered, what metrics are returned, and exactly what user questions should trigger this tool. Nothing essential is missing for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the single parameter is adequately described as 'Seller/operator name.' The description does not add meaning beyond the schema; it clarifies the domain by using 'operator' and marketplace context, but the parameter semantics remain essentially identical to the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks whether an operator sells on Amazon US, Amazon UK, and/or Walmart, and lists the returned metrics (brand count, ASIN count, buybox days). This is a specific verb+resource combination. However, it does not name or distinguish itself from closely related sibling tools such as brand_xmarket, keyword_cross_marketplace, or xmkt_pricing_compare, so sibling differentiation is incomplete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit query examples ('does this seller sell on Walmart too', 'is this operator on Amazon UK') and a general trigger phrase ('any multi-marketplace operator question'). This gives clear context for when to use it. It does not state when not to use it or name alternatives, stopping 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.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
