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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world behavior, so the safety profile is covered. The description adds real behavioral context beyond that: brands with insufficient observed data are dropped from rankings, results are capped by limit, only brands/categories/emails-per-week are returned, and each brand carries a details_url for citation.

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?

Front-loads what the tool does and what it ranks by, then layers usage examples, coverage, exclusions and return shape. Dense but each sentence carries information; only the return-field sentence is partly redundant with the output schema.

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?

An output schema exists, yet the description still summarizes return contents and citation practice, plus ranking window, supported markets and data thresholds. Nothing needed to call the tool correctly is missing.

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 description coverage is 100%, so the schema already documents limit, market and category, including the same country codes and category examples the description repeats. The description adds no syntax or format meaning beyond the schema, making the baseline 3 appropriate.

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?

States a specific verb and resource (find ecommerce brands), the ranking metric (average marketing emails per week over 90 days), and the market scope. It is clearly distinguishable from siblings like get_brand_email_frequency and get_brand_summary, which return per-brand detail rather than a ranked list.

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?

Gives concrete usage contexts with example questions ("Which brands email most frequently in Swedish ecommerce?", finding a brand when the user names a kind of brand) and notes markets and data-sufficiency exclusions. It does not explicitly name the sibling tools or state when to prefer them, so routing guidance is contextual rather than explicit.

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