klax_simulate_grade

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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses that this is a simulation rather than an official prediction, that current_scores must align with klax_get_syllabus's evaluation_ratio keys, and that ungraded items should be omitted. This gives an agent a realistic sense of the tool's assumptions and limits, though it does not detail output structure or error behavior.

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 three focused sentences with no filler. It front-loads the main purpose, then adds the key-mapping detail and the important caveat that this is not an official prediction. Every sentence earns 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 a calculation tool, the description covers the essential context: the inputs, the relationship to the syllabus, the handling of ungraded items, and the assumption-based nature of the result. An output schema exists, so return-value details are not required. The only minor gap is the unexplained optional yearhakgi parameter, but it is not critical to understanding the tool's core behavior.

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 0%, so the description must compensate. It adds meaningful semantics for current_scores by specifying the key alignment with klax_get_syllabus and the omission rule for ungraded items. However, it leaves yearhakgi unexplained and does not clarify the numeric scale of current_scores or target_score beyond the general 'total score' context.

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 names a specific operation: back-calculate the remaining average score needed to hit a target total, using syllabus evaluation ratios and current scores. It also distinguishes itself from official grade prediction by explicitly labeling the result an assumption-based simulation, which separates it from sibling tools.

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 clearly states the context: use when you have syllabus evaluation ratios and current secured scores and want to know what remaining average is needed. It also warns that this is not an official grade prediction, providing a clear exclusion. It does not name alternative tools, but no sibling appears to offer the same simulation capability.

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