US Brand Intelligence

US Brand Signal Policy Simulator

us-brand-signal-policy-simulator

Read-only

Compare buyer-defined threshold policies over the same opaque US brand signal scores. Return accepted, review, and rejected IDs for every policy plus differences and sensitivity summaries; it does not choose or recommend a policy. — $0.05/call, x402 (USDC on base).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`rows`	Yes	One to 100 unique opaque IDs with integer scores from 1 to 100.
`policies`	Yes	One to 20 unique policies. Review must be strictly below accept.
`schemaVersion`	Yes	The only supported closed contract version.	1.0

TDQS

A4.2/5.0

Behavior4/5

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

The description discloses behavioral context beyond the annotations: it returns per-policy accepted/review/rejected IDs, differences and sensitivity summaries, and explicitly does not choose or recommend a policy. It also adds operational details ($0.05/call, x402 USDC on base), which are useful. No contradiction with annotations is present.

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 plus a cost/protocol note. It front-loads purpose, outputs, and a key limitation. Every sentence earns its place with no fluff.

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 tool's moderate complexity (arrays of scores and policies) and absence of an output schema, the description covers the key outputs (per-policy IDs, differences, sensitivity summaries) and the non-goal (no recommendation). It does not describe error cases or exact output structure, but the schema provides validation constraints.

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 100%, so the schema already documents all parameters. The description does not add syntax or format details beyond what the schema provides, but it does reinforce that policies are threshold-based and scores are opaque, aligning with the schema. Baseline of 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 uses a specific verb 'Compare' with a defined object 'buyer-defined threshold policies' over 'the same opaque US brand signal scores'. It clearly states the output ('Return accepted, review, and rejected IDs... plus differences and sensitivity summaries') and explicitly distinguishes itself from decision tools by saying 'it does not choose or recommend a policy'.

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 provides clear context for when to use the tool: comparing multiple threshold policies on the same input scores. It includes an explicit exclusion (does not choose/recommend), but does not name alternative sibling tools or provide more granular when-to-use guidance.

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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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.

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