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

Annotations already mark the tool as read-only, open-world, and idempotent. The description adds valuable context about compliance (GDPR-compliant tracking), industry standards (IAB Tech Lab), and output structure (risk categorization, compliance warnings), going beyond what annotations convey. No contradiction detected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with the core purpose, followed by audience/usage context, and then inputs/outputs. No redundant or filler content; every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With a fully documented schema, rich annotations (readOnly, idempotent, openWorld), and an output schema, the description adds the remaining context: standards used, compliance approach, target users, and decision-timing. Nothing significant is missing for the tool's complexity.

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

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

Schema coverage is 100%, so the baseline is 3. The description mentions 'domain, page URL, and optional contextual signals' which loosely maps to the schema's domain, url, categories, and gdprConsent, but does not add meaning beyond what the schema already documents. It adds no syntax, format, or behavioral specifics for parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Evaluates'), a specific resource ('programmatic ad inventory'), and a clear objective ('brand safety risks using IAB Tech Lab's standards'). It clearly distinguishes itself from siblings like programmatic_attribution_calibrator, which handles attribution, not safety.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indicates a clear context for use ('before bidding') and identifies the intended audience ('ad revenue operations teams'). It does not explicitly mention alternatives or exclusions, but the context is sufficient to understand when to apply this tool.

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