Grade distribution for a course

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

The annotations already establish read-only, idempotent, non-destructive behavior. The description adds meaningful behavioral context: data comes from UCI public records, recent quarters may be missing, and results can be grouped by instructor or term. This goes beyond the structured metadata without contradicting it.

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?

Three compact sentences with no filler: the first states the resource, the second gives concrete use cases, and the third provides a relevant data caveat. Each 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?

The description covers purpose, grouping options, source, and recency limitations, which is strong for a read-only lookup tool with fully documented parameters. It does not describe the exact output shape, but since there is no required input and the schema documents all filters, this is a minor gap rather than a critical 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%, so the baseline is 3. The description adds only marginal parameter context, such as highlighting the default grouping and connecting professor difficulty to the instructor/course filters. It does not substantially clarify parameters beyond what the schema already provides.

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 tool as providing historical grade distributions and average GPA for a course, with a default breakdown by instructor. It also distinguishes itself from sibling tools like enrollment_history or instructor_info by focusing on grade outcomes and professor difficulty.

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 tells the agent when to use the tool: to answer 'which professor should I take?' or 'how hard is this class?'. It does not name alternatives or exclusions, but the use-case framing gives clear selection guidance.

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