ncaa_rankings

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

Annotations already declare read-only and idempotent hints, and the description adds the exact return shape, uppercase key warning, an example, and auth requirement. This gives useful behavioral context beyond the structured fields without contradicting them.

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 compact and front-loaded with the tool's purpose. Every line contributes value: the summary, return structure note, example, and auth mention—nothing wasted.

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?

Despite having no output schema, the description explicitly lists the return object structure. With simple parameters that have defaults and good annotations, the description covers the needed context (purpose, example, auth, key casing) fully for this tool's complexity.

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 each parameter (poll, sport, division) is already documented. The description adds an example showing valid values and defaults, but it does not provide additional semantics beyond what the schema already supplies, so baseline 3 is appropriate.

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 states the tool returns poll rankings (AP, coaches, etc.) for a college sport and division, with a concrete example. It does not explicitly name sibling tools like cfbd_rankings or ncaa_standings to differentiate, but the return format note about UPPERCASE keys distinguishes it from standings.

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 the description and example (e.g., for AP football poll), but there is no explicit 'when to use this vs. alternatives' or 'when not to use' guidance. The 'unlike standings' remark is more about key casing than usage context.

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