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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so safety is covered; the description adds substantive context beyond that: the 4.0-scale default, that A+ value, weights and percent cutoffs are adjustable because schools differ, and what is returned (unweighted and weighted GPA, cumulative, quality points table, grade distribution, highest-leverage course).

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 core sentence is front-loaded and useful, but the long parenthetical query-keyword list ('gpa calculator, gpa calculator college, gpa calculator high school, weighted gpa calculator...') is SEO padding that adds no information for an agent and inflates the definition. Size is defensible for an 8-parameter tool, but that clause does not earn its place.

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 an 8-parameter, nested-object tool with an output schema and annotations, the description covers inputs, defaults, adjustability, return contents, and scope exclusions. An agent has everything needed to invoke it correctly without consulting other sources.

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 the baseline is 3, but the description genuinely adds semantics the schema does not: it explains how grade strings are interpreted across formats, that cumulative GPA requires previous_gpa together with credits, and that the weighting/percent-cutoff defaults are configurable. It stops short of explaining interaction rules such as what happens if previous_credits is omitted with previous_gpa.

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+resource ('calculate their GPA') and enumerates the exact input forms it handles (letter grades A+ to F, percentages, P/NP/W, credits, level), so an agent can immediately tell this apart from final_grade_needed or plan_target_gpa.

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?

Opens with an explicit trigger ('Use this when the user wants to calculate their GPA') and closes with a clear when-not list: no transcript reading, no international grade conversion, no admissions prediction. It does not name the sibling tools as alternatives, so routing between calculate_gpa, final_grade_needed and plan_target_gpa still requires inference.

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