get_course_grades

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

Annotations already cover read-only, idempotent, open-world, and non-destructive behavior. The description adds value by specifying that results are historical, separated into OVERALL and individual sections, and exclude predictions or current seat counts. It still omits auth, rate-limit, and pagination details, but it meaningfully exceeds the annotation baseline.

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 tightly written sentences, front-loaded with the tool's scope and followed by a useful exclusion. 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?

With 4 parameters, 0% schema description coverage, and no output schema, the description is materially incomplete. It gives high-level scope but does not explain parameter meaning or enough return structure for an agent to invoke the tool confidently.

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 for undocumented parameters. It only implies that 'course' is involved and does not explain subject, course patterns, campus, or yearSession semantics, leaving most parameter meaning to the bare schema.

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 clearly identifies the resource and scope: historical UBCGrades reports for a course, split into OVERALL and section-level data. It distinguishes the tool from predictive or live-seat tools via the second sentence, though it does not explicitly differentiate from siblings like get_course or compare_courses.

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?

Usage is implied by 'historical UBCGrades reports for a course,' and the description adds a useful exclusion ('No prediction of grades or current seat counts'). However, it never names alternatives or states when another sibling tool should be preferred.

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