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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, so safety is largely covered. The description adds behavior the annotations do not: it returns the mean rounded to 2 decimal places (valuable since there is no output schema) and states the cost of 1 credit. Minor gap: no mention of behavior for empty or invalid grade input.

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?

Two short sentences with zero filler: purpose and scale first, then return value, precision, and cost. Nothing is duplicated or padded, and the most decision-relevant information is front-loaded.

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 one-parameter, read-only computation with no output schema, the description covers purpose, input domain, output shape (mean rounded to 2dp), and cost. The remaining omission is input handling (empty list, out-of-range values, weighting), which is a modest gap rather than a blocking one.

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% and the single parameter is already documented as a numeric list on the 0.0–4.0 scale, so the description can only restate it. The description does reinforce the 4.0 scale and the "mean of all grades" semantics, but adds no format or edge-case detail beyond the schema; baseline 3 applies.

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?

"Calculates the Grade Point Average (GPA) from a list of grade points on a 4.0 scale" names a specific verb (calculates), a specific resource (GPA), and the input domain (grade points on a 4.0 scale). An agent can distinguish it from generic siblings like average or calculate_median because the GPA/4.0-scale framing is explicit.

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 never says when to prefer this over the many neighboring arithmetic tools (average, calculate_grade, calculate_median, calculate_standard_deviation). There are no exclusions, no prerequisites, and no alternative-selection guidance; the agent must infer usage from the name alone.

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