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brand_audit_batch_start

Initiate an async brand audit across up to 50 domains with configurable depth and brand aliases. Obtain an audit ID to track and retrieve results.

Instructions

Enqueue an async brand audit across up to 50 target domains with optional standard/deep discovery depth, brand aliases, and caller-supplied candidate domains. Returns { auditId, queuedAt, targetCount, etaSeconds } immediately; poll with brand_audit_status and fetch results with brand_audit_get_report once complete. Each target consumes 1 unit of the monthly BRAND_AUDIT_QUOTAS budget.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
viewNoOutput view mode. 'csc_complement' produces a CSC-tuned payload; requires enterprise tier. Default 'standard'.
depthNoDiscovery depth. standard is default; deep expands candidate seeding and enrichment fanout.
formatNoInline output mode. Defaults to "both".
domainsYesDomains to audit (max 50 per batch). Duplicates are merged.
planner_modeNoPlanner mode for staged discovery fanout. observe emits metrics; enforce applies candidate-backed signal caps.
brand_aliasesNoOptional public brand aliases to seed, such as product or legal-entity labels.
discovery_modeNoBrand-discovery pipeline mode. classic = legacy sweep; tiered = tenant/graph/evidence wrappers first (BlackVeil-internal).
min_confidenceNoDrop candidates whose combined confidence falls below this threshold (0-1, default 0.5).
candidate_domainsNoOptional candidate domains supplied by the caller for corroboration.
ownership_verifiedNoCaller attests that the target domains are owned or authorized for scanning. Required when discovery_mode is "tiered" and the caller is not an enterprise/owner/partner principal.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
scoreYes
passedYes
categoryYes
findingsYes
Behavior4/5

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

The description adds valuable behavioral context beyond annotations: async execution, immediate return of auditId/etaSeconds, quota consumption, and required ownership_verified for certain discovery modes. Annotations already indicate non-idempotent and non-destructive, which aligns. It stops short of describing error handling or failure modes.

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 concise sentences: first states the action and options, second describes the return and follow-up steps. No wasted words, front-loaded with the key purpose.

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?

Given the complexity (10 parameters, output schema present), the description covers all critical aspects: async nature, return structure, polling workflow, quota, and special conditions. The output schema explains return values, so the description need not repeat them. The context is sufficient for correct invocation.

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

Parameters4/5

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

Schema coverage is 100%, baseline is 3. The description adds context: 'up to 50 target domains', 'optional standard/deep discovery depth', 'brand aliases', 'caller-supplied candidate domains', and conditions like 'requires enterprise tier' for view and ownership_verified requirement. This goes beyond just listing parameters.

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 it 'enqueues an async brand audit' for 'up to 50 target domains', specifying the verb (enqueue), resource (brand audit), and scope (batch async). It distinguishes from siblings like brand_audit_single (single domain) and brand_audit_status/get_report (polling/retrieval).

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 explains that the tool returns immediately and users should poll with brand_audit_status and fetch results with brand_audit_get_report. It also mentions quota consumption. However, it does not explicitly state when or when not to use this tool versus alternatives, though the batch vs single distinction is clear from the name and description.

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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