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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is known. The description adds valuable behavioral context: it reveals data provenance (state licensing exam outcomes), the exclusion rule (schools with <5 test-takers), and the coverage boundary (not from Google/school websites). This exceeds the baseline and adds meaningful transparency.

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 concise (two sentences) and front-loads the core purpose. Each clause adds value: the data source, the metrics, the optional filter, and the data caveat. No redundant or filler content.

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 the tool has no output schema, the description adequately explains what results to expect (rankings by pass rates and tuition). It covers the data source, the exclusion logic, and the optional filter. The enumeration of metrics (written/practical pass rates, etc.) prepares the agent for the response shape. With 3 parameters and no nested objects, this is complete for effective invocation.

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 parameters are well-documented. The description adds nuance: it explains the 'license' parameter's significance (separate exams) and implies what 'city' filters (optional). It doesn't repeat the schema but provides context on how the parameters affect results (e.g., ranking by exam outcomes). Justification for 4: the description enhances understanding beyond the schema by connecting parameters to the ranking logic, though not exhaustively.

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 ranks schools by exam outcomes and supports filtering by city. It names the specific resource (barber/cosmetology schools) and the action (compare/rank). However, it doesn't explicitly distinguish itself from the sibling 'compare_barbershops_salons', which might be confused as similar, though it does specify the data source differentiates it.

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 implies when to use this tool: when you need objective exam pass rates and tuition data, and explicitly states the data is not available from other sources, which signals its unique value. It doesn't explicitly exclude alternatives, but the context is clear. It would benefit from explicit 'when not to use' guidance, but it's adequate.

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