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Webotee Amazon Product Research

operator_new_on_brand

Read-only

Identify sellers newly observed on a brand's ASINs in a recent window. Filters by brand, operator, and activity status to detect recent entrants.

Instructions

Operators newly OBSERVED on a brand in a recent window — counted at BRAND level: a seller's FIRST observation anywhere across the brand's ASINs falls in the window. This is coverage-robust (a long-present seller was almost certainly seen on some ASIN earlier, so they correctly drop out) — a trustworthy directional 'new on the brand' count, not the inflated per-ASIN number. Still first-OBSERVED, not provably first-to-market. Returns each operator with first_observed, how many of the brand's ASINs we've seen them on, and whether still active. Use for 'who's new on ', 'who's newly showing up on my brand', 'recent sellers on '. Amazon US/UK. since_days already bounds first_observed below; optional filters (all default to no filter): operator (exact, case-insensitive) + operator_contains, first_observed_from/_to, min/max n_asins_on_brand, min/max total_days_seen, min/max observed_buybox_days, still_active (true/false).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandYesThe brand to watch.
since_daysNoWindow in days (default 30, max 180).
marketplace_idNo1 = Amazon UK, 2 = Amazon US (default)
limitNo
operatorNoExact operator/seller name (case-insensitive).
operator_containsNo
first_observed_fromNoYYYY-MM-DD lower bound on first_observed.
first_observed_toNo
min_n_asins_on_brandNo
max_n_asins_on_brandNo
min_total_days_seenNo
max_total_days_seenNo
min_observed_buybox_daysNo
max_observed_buybox_daysNo
still_activeNoKeep only operators last seen within 7 days (true) or not (false).
Behavior4/5

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

The description goes beyond the readOnlyHint annotation by explaining methodology (coverage-robust, not inflated), limitations (not provably first-to-market), return fields, and the role of since_days. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, front-loading the main purpose and methodology. Each sentence adds value, and the length is appropriate for the complexity.

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?

Given no output schema, the description explains return fields (first_observed, n_asins_on_brand, still_active). It covers methodology, limitations, use cases, and filters, providing sufficient context for a complex tool with 15 parameters.

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 only 40%, so the description must compensate. It lists filter parameters and adds context like operator exact case-insensitivity and the bounding role of since_days. However, it does not provide detailed semantics for all 15 parameters, leaving gaps.

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 clearly states the tool's purpose: identifying operators newly observed on a brand in a recent window. It uses specific verbs and defines the resource (operators on a brand). While it doesn't explicitly differentiate from siblings like find_new_operators, the brand-level focus is distinct.

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

Usage Guidelines3/5

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

The description provides explicit use cases ('who's new on <brand>', 'who's newly showing up on my brand', 'recent sellers on <brand>'), but does not mention when not to use the tool or suggest alternatives. Lacks exclusion criteria.

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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