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Logimu

brand

Read-onlyIdempotent

Amazon BRAND dossier from Logimu's own buy-box observations (derived intelligence, not estimates): catalog size, how many sellers win the buy box, control score (share of buy-box days won by FBA offers), winner diversity, Amazon's own retail share, FBA penetration, seller concentration (HHI) and churn, seller-pressure trend grades, since when we have observed the brand, its dominant category with p25/p50/p75 benchmarks, and the top-10 sellers by observed buy-box days with their feedback records. USE WHEN the user asks who sells a brand, how contested / Amazon-dominated / FBA-heavy a brand is, whether a brand is gaining or losing sellers, or wants to vet a brand for wholesale / arbitrage / brand protection. OPTIONS: sellers=true adds the full seller list (25 per page, page=N); network=true adds the brand's SELLER MAP - the brands connected to it because the same third-party sellers win the buy box on both (edges weighted by shared sellers, one hop, max 50); history=true adds up to 24 calendar months (ASINs, sellers, buy-box days, price, rating). All 11 Amazon marketplaces, no Walmart. HONESTY: 'observed since' is our data horizon, not the brand's age; a 404 means we have not observed the brand (names are matched as Amazon prints them). COST: paid plan required (the tool is listed only for keys on a paid plan). 1 credit for the dossier, +0.5 once when history comes back non-empty, +1 per page of sellers, +1 for the network. Unknown brand: 0.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoSeller-list page (with sellers=true).
brandYesBrand name as Amazon prints it (case-insensitive).
countryNoAmazon marketplace.us
historyNoAdd history.monthly (up to 24 months). +0.5 credit when non-empty.
networkNoAdd the seller map: connected brands + weighted edges. +1 credit.
sellersNoAdd the full seller list (25 per page). +1 credit per page.
network_limitNoEdges to return with network=true (1-50).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the annotations' read-only/idempotent safety profile, the description discloses paid-plan requirements, per-option credit costs, the data horizon caveat ('observed since' is not brand age), 404 behavior for unobserved brands, and marketplace scope. These are exactly the operational details an agent needs before invoking the tool.

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?

Despite being dense, the description is front-loaded: it defines the dossier, then states when to use it, then options, then scope, then honesty caveats, then cost. Every section earns its place for a tool with seven parameters, multiple optional expansions, usage-dependent costs, and no output schema.

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?

With no output schema, the description carries the burden of explaining what is returned and does so thoroughly, including field families, optional expansions, pagination, marketplace coverage, data freshness caveats, and cost model. No material gap remains for correct invocation.

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

Parameters5/5

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

Schema description coverage is 100%, so the baseline would be 3, but the description adds material meaning: sellers=true returns 25 per page and supports page=N; network=true returns a one-hop seller map with up to 50 weighted edges; history=true returns up to 24 calendar months with specific fields; and each option carries its own credit cost.

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 names a specific resource ('Amazon BRAND dossier') and enumerates its contents in detail, making clear this is brand-level buy-box intelligence rather than a generic product or seller lookup. It also distinguishes the data source ('Logimu's own buy-box observations, derived intelligence, not estimates'), which separates it from sibling tools that may surface estimates or different entities.

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?

It gives an explicit 'USE WHEN' list covering who sells a brand, how contested or Amazon-dominated it is, seller trends, and vetting for wholesale/arbitrage/brand protection. It does not name sibling alternatives or state when not to use this tool, so it stops short of the highest tier.

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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