explain_grade

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true. The description adds significant behavioral context: it explains that the tool recomputes grades using Canvas data, handles edge cases (curves, fudge points, grading periods), and provides a reconciliation check. There is no contradiction with annotations; instead, the description enriches understanding of the tool's behavior and limitations.

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 well-structured with an introductory sentence, a use-case paragraph, and a limitations section. It is front-loaded with core functionality and avoids repetition. However, it could be slightly more concise by condensing the limitations into bullet points or fewer sentences. Overall, it is organized and efficient, earning a 4.

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?

Given the tool's complexity and the absence of an output schema, the description thoroughly covers what the tool returns: assignment-group weights, drop rules, per-group breakdowns, letter grade, and reconciliation check. It also addresses important edge cases (curves, grading periods, pseudonymization). The sibling context shows that no other tool provides this detailed grade explanation, making the description complete for agent understanding.

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?

The input schema has 100% description coverage, so the schema already documents the parameters. The main description adds value through the limitations section (e.g., omitting student_id for authenticated user, resolving pseudonyms) that provides usage context beyond the schema's property descriptions. While not deeply detailed, this auxiliary guidance justifies a score above the baseline of 3.

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 starts with a specific verb 'Recomputes and explains' and clearly identifies the resource: 'weighted course grade for a student'. It enumerates what the output includes (weights, drop rules, per-group breakdowns, letter grade, reconciliation check), distinguishing it from siblings like 'project_grade' or 'explain_grading_policy' which serve different purposes. The purpose is unambiguous and well-scoped.

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 explicitly states when to use the tool: 'verify that Canvas's displayed grade matches the rules' or 'explain to a student or instructor how their grade was calculated'. It provides limitations (single student per call, curves not reflected, grading periods, pseudonymization) that guide appropriate usage. However, it does not explicitly mention when not to use it or suggest alternative tools, which keeps it from a score of 5.

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