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

Annotations already declare readOnlyHint=true, so safety is covered. The description adds behavioral transparency by disclosing that the metric is monthly, percentile-ranked over 36 months, and includes specific output components (gap_type, trend, confidence, top_drivers). It also notes 'Updates: monthly', providing data freshness context beyond the annotations.

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 two sentences: the first packs the essential return value, methodology, and output fields; the second gives precise usage guidance. There is no filler, and the most important information is front-loaded for quick parsing.

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?

Given that there is no output schema, the description adequately lists the key return fields and the underlying data sources, which is sufficient for an agent to understand what the tool does. It also covers usage scenarios and update frequency. The only minor gap is that it doesn't explain the meaning of trend/confidence, but this is not critical for tool selection.

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?

The only parameter (days) has a thorough schema description that fully explains its purpose, behavior (historical series vs current snapshot), and the Gold tier requirement. With 100% schema coverage, the description doesn't need to add parameter details; the baseline of 3 applies since the schema carries the explanatory burden.

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 clearly states the tool's function with a specific verb ('Returns') and a well-defined output: a 0-100 brand sentiment-to-sales-volume gap score with detailed sub-components (gap_type, trend, confidence, top_drivers). It goes beyond generic phrasing by naming the exact data sources (UMich consumer sentiment vs FRED retail sales) and the percentile-ranking methodology, which distinguishes it from siblings.

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 provides explicit 'Call when' guidance with four concrete use cases (marketing efficiency, high-engagement low-conversion campaigns, consumer hype vs actual spending, brand buzz outpacing sales, timing shifts in ad spend). While it doesn't explicitly name alternatives or when-not-to-use, the strong context signals make routing clear.

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