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

Annotations already establish that this is a read-only, closed-world read. The description adds meaningful context beyond them: the data provenance (state licensing outcomes, not published by Google/school sites/review sites) and the exclusion rule that schools with fewer than 5 test-takers are dropped. It does not mention ordering direction or pagination, which keeps it short of a 5.

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-loaded with the core action and the metric list, then closes with the exclusion rule that materially affects interpretation. The sentence about the data not being published by Google or review sites is mildly promotional filler, otherwise the text is tight.

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?

For a 3-parameter read-only tool with no output schema, the description supplies the returned fields (written/practical pass rates, first-attempt rate, attempts, students tested, tuition), the filter, and the minimum-sample rule. Only sort order and pagination behavior are unspecified.

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 city, limit, and the license enum are already fully documented in the structured fields, including the note that the two licenses are separate exams. The description echoes the city filter and metric set but adds no parameter syntax or constraints beyond the schema.

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 (rank/compare) and resource (barber/cosmetology schools) scoped to 2026 state licensing exam outcomes, and enumerates the metrics ranked on. An agent can distinguish it from compare_barbershops_salons and texas_licensee_counts without opening the schema.

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 notes the optional city filter and the data's uniqueness, implying a research/benchmarking use case, but never names an alternative tool or states when to prefer this over compare_barbershops_salons or texas_licensee_counts. Usage is implied rather than prescribed.

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