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

Annotations already mark it read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds useful behavioral context: it supports percent and points modes, returns the needed percent, secured/out-of-reach status, 0/100 outcomes, and letter needs. It does not contradict annotations.

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 and dense but not padded. Each sentence contributes: use case, return values, mode support, and an explicit exclusion.

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 7-parameter calculator with two input modes and an output schema, the description covers the relevant complexity. It explains supported modes, expected outputs, and the key boundary against weighted averages; nothing essential is missing.

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 per-parameter definitions already carry most meaning. The description still adds value by grouping parameters into percent mode (current_grade_percent + final_weight_percent) and points mode (points_earned_so_far, points_possible_so_far, final_exam_points), which helps invocation.

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 gives a specific task: compute the score needed on a final exam or last assignment to reach a target grade. It clearly distinguishes this from category-weighted-average calculation, so an agent can separate it from siblings like weighted_grade.

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?

It states exactly when to use the tool (student asks what score is needed on a final) and when not to use it (weighted average of categories). It does not explicitly name the sibling tool to use instead, but the exclusion condition is clear.

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