US Brand Intelligence

US Brand Offer Evidence Normalizer

us-brand-offer-evidence-normalizer

Read-only

Turn supplied US brand offer, coupon, ad, and landing-page evidence into closed deterministic JSON. Submit bounded inline rows or one authorized read-only Dataset; the Actor validates source proof and terms without browsing or inferring ownership. — $0.05/call, x402 (USDC on base).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`rows`	No	One to 100 closed evidence-row objects. Leave empty when selecting a Dataset.
`options`	No	Labels are off unless an explicit model and caller key are supplied in BYOK mode.
`datasetId`	No	One resource selected with READ permission. Dataset V1 accepts at most 10 rows and probes 11 in one SDK call. Leave empty when using inline rows.
`requestId`	Yes	Correlation ID for this run. Reusing it in another run does not deduplicate purchases.
`schemaVersion`	Yes	Required V1 contract version.	1.0
`openrouterApiKey`	No	Optional caller-owned key for BYOK labels. The runtime must redact and never persist it.

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: it validates source proof and terms, does not browse or infer ownership, produces deterministic output, and costs $0.05/call. This goes beyond the structured annotations.

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 two sentences, front-loaded with purpose in the first sentence and constraints/modes in the second. Every word earns its place: there is no fluff or repetition of schema details.

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 the complex input schema (6 params, nested objects) and no output schema, the description covers the essential context: what the tool does, input modes, constraints (bounded rows, authorized read-only Dataset), and behavioral boundaries (no browsing, no ownership inference). It does not describe output structure, but the phrase 'closed deterministic JSON' gives a reasonable expectation.

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?

The schema description coverage is 100%, so the schema itself thoroughly documents every parameter. The description adds only a high-level mention of 'bounded inline rows or one authorized read-only Dataset,' which maps to the rows/datasetId choice but does not add syntax or detailed semantics beyond the schema. Baseline 3 is appropriate.

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 states a clear verb ('Turn'), specifies the resource ('US brand offer, coupon, ad, and landing-page evidence'), and gives the output ('closed deterministic JSON'). It distinguishes itself from sibling evidence tools by noting it validates source proof and terms without browsing or inferring ownership.

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?

The description explicitly instructs how to submit input: 'bounded inline rows or one authorized read-only Dataset.' It also provides a boundary by stating it does 'not browsing or inferring ownership,' which implies when this tool is not appropriate. It does not name explicit alternative tools, but the guidance is clear enough for selection.

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

All US Brand Intelligence tools Inspect

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TDQS

A3.8/5.0

Disambiguation2/5

Many tools have overlapping purposes, e.g., us-brand-signal-metrics-aggregator, us-brand-signal-cross-tabulator, and us-brand-signal-brand-summary all produce counts or summaries of the same type of data. The descriptions are detailed, but the boundaries between analyzers, summarizers, and aggregators are unclear enough that an agent may struggle to pick the right one.

Naming Consistency4/5

Most tools follow a consistent 'us-brand-' prefix with descriptive nouns separated by hyphens (e.g., us-brand-signal-batch-splitter, us-brand-evidence-chronology-builder). The exception is 'pricing_info', which uses an underscore and lacks the prefix, creating a minor inconsistency.

Tool Count3/5

At 21 tools, the set is on the heavier side but still manageable for a complex domain like brand intelligence. Many tools are variations on deterministic signal processing, which could be streamlined, but the count itself is not extreme.

Completeness3/5

The tool set covers a broad pipeline for processing buyer-supplied signals and evidence, including splitting, summarizing, routing, and building payloads. However, it lacks any tools for ingesting or fetching data from external sources (except one federal award snapshot), and there is no end-to-end controller that orchestrates the workflow. This leaves notable gaps for a complete 'brand intelligence' lifecycle.

Resources

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