US Brand Intelligence

US Brand Action Queue

us-brand-action-queue

Read-only

Turn verified US brand signals into one deterministic prioritized action queue with evidence, urgency, ownership status, and rule traces for downstream operators and agents. The Actor does not send messages or write to destinations. — $0.05/call, x402 (USDC on base).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`items`	No	Choose this or datasetId, never both; the runtime enforces XOR because the Apify schema dialect cannot express cross-property relations.
`options`	No	Deterministic caps and optional explanation mode.
`scoredAt`	Yes	Explicit UTC timestamp bound into request identity and used by all time-based scoring rules.	2026-08-05T00:00:00Z
`datasetId`	No	Choose this or items, never both; the runtime enforces XOR because the Apify schema dialect cannot express cross-property relations.
`requestId`	No	Bounded correlation value only; it does not deduplicate separate Actor runs.
`schemaVersion`	Yes	Closed contract version.	1.0
`openrouterApiKey`	No	Optional caller-supplied value for one bounded explanation only; runtime enforces the BYOK conditional because the Apify schema dialect cannot express it.

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already indicate read-only and non-destructive, and the description adds that the Actor 'does not send messages or write to destinations,' reinforcing no side effects. It also adds determinism, inclusion of urgency/ownership/rule traces, and cost ($0.05/call) beyond annotation coverage, with no contradiction.

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?

Two sentences plus a cost note; every sentence carries useful information: the core transformation, the no-side-effect boundary, and the pricing/currency. It is front-loaded with the main verb and resource, with no filler.

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?

The tool is complex with nested objects, XOR input constraints, and no output schema, yet the description gives only a high-level output summary without detailing the queue structure, prioritization criteria, or return format. While the input schema's `items` prefill hints at the shape, an agent selecting this tool would benefit from more explicit output details, making it minimally viable but not fully complete.

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 baseline is 3. The description mentions evidence, urgency, ownership status, and rule traces, which map to nested fields in the `items` parameter, but it does not add syntax, format, or precedence details beyond the schema's own per-parameter descriptions.

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 phrase 'Turn verified US brand signals into one deterministic prioritized action queue' with an explicit resource and output artifact. It clearly distinguishes from sibling tools focused on signals, evidence, or chronology by naming the action-queue output and its contents (evidence, urgency, ownership status, rule traces).

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 states it is for 'downstream operators and agents' and explicitly excludes sending messages/writing to destinations, giving a clear boundary. It does not explicitly name alternative tools, but the context and exclusion provide practical guidance on when to use it.

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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