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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior. The description adds meaningful context beyond annotations by naming the source tables, distinguishing the two percentile definitions, and noting the class of 2026 mean and score-band comparison, though it does not describe the response shape or edge cases.

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 front-loaded with the core purpose and then supplies relevant percentile-table context. It is dense but not redundant; the exact test-taker count is extra detail, but it supports the national comparison context.

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 simple two-parameter lookup with full schema coverage and rich annotations, the description is nearly complete: it explains the two percentile types and the mean/band comparison. It does not spell out the response structure or routing alternatives, but no output schema is provided and the core behavior is covered.

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%, including ranges, enum values, default, and step-of-10 guidance. The description restates the score ranges and section categories but adds little semantic detail beyond what the schema already provides, so the baseline of 3 is 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?

The description states a specific operation: computing the percentile rank of a digital SAT total or section score, and identifies the College Board percentile tables and comparison outputs. It does not explicitly differentiate this tool from the inverse sibling get_score_for_sat_percentile or the state-based siblings, so it is clear but not fully sibling-routed.

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 usage for converting a known SAT score into percentiles, but gives no explicit when-to-use/when-not guidance or alternative-tool routing. It does not mention using get_score_for_sat_percentile for the inverse lookup, so an agent must infer which sibling to call.

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