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ncosic

Webotee Amazon Product Research

unauthorized_sellers

Read-only

Retrieve buy-box-winning sellers for a brand, classified as authorized, arbitrage, or reseller. Flag unauthorized sellers when an authorized list is supplied.

Instructions

List the sellers winning a brand's buy box — the resellers and arbitrage operators you're up against — each classified (authorized-retailer / arbitrage / Amazon / brand-direct / reseller). If you've saved an authorized list (authorized_seller_set) it instead flags the UNAUTHORIZED sellers. Use when the user asks 'which operators dominate the buy box on ', 'who else is selling my brand', 'unauthorized sellers on Nike', 'find rogue sellers', or any brand buy-box / protection question.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandYesBrand name (case-insensitive).
authorized_sellersNoOptional. Authorized seller names — sellers NOT in this list are flagged. If omitted (and none saved), all buy-box-winning sellers are returned, classified.
marketplace_idNo1 = Amazon UK, 2 = Amazon US (default)
limitNo
seller_nameNoExact seller name (case-insensitive).
seller_name_containsNo
min_observed_buybox_daysNo
max_observed_buybox_daysNo
min_asins_touchedNo
max_asins_touchedNo
min_avg_priceNo
max_avg_priceNo
first_seen_fromNoYYYY-MM-DD.
first_seen_toNo
last_seen_fromNo
last_seen_toNo
operator_type_inNoComma-separated classifications to keep (cold path only, when no authorized list is set): e.g. arbitrage, reseller, amazon, brand-direct, authorized-retailer.
Behavior4/5

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

Annotations already declare readOnlyHint=true. The description adds behavioral context: sellers are classified, and if an authorized_seller_set is saved, unauthorized sellers are flagged. It does not contradict annotations, and provides useful conditional behavior that goes beyond the structured data.

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 a single paragraph that front-loads the main action and purpose. Every sentence adds value, though it is slightly lengthy. It could be slightly more concise, but overall well-structured.

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

Completeness3/5

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

Given the tool has 17 parameters and no output schema, the description is somewhat complete in explaining what it does and when to use it. However, it lacks details about return format, pagination, or how results are presented, which would be helpful for a tool with this complexity.

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

Parameters2/5

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

Schema description coverage is only 35% (most parameters lack descriptions). The tool description does not compensate by explaining parameters beyond brand and authorized_sellers. With 17 parameters and low coverage, the agent needs more parameter guidance in the description.

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 states the tool lists sellers winning a brand's buy box, classified by type, and flags unauthorized sellers if an authorized list is saved. The verb 'list' and resource 'brand buy box sellers' are specific, and it distinguishes from siblings like authorized_seller_list and authorized_seller_set by focusing on buy-box winners and classification.

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?

Explicit usage guidance is provided: 'Use when the user asks...' with example queries covering brand buy-box protection questions. It also explains conditional behavior when an authorized list is saved, helping the agent choose between this tool and alternatives.

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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