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

Annotations already establish readOnlyHint=true, and the description adds useful behavioral context: it explains the PWR formula, says the result includes rank and tier, and explicitly says 'Free, no key', which informs auth expectations. It does not detail every output field, but for a simple read-only rating lookup the key behavioral facts are present.

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-loads the core concept, then gives the formula and example queries. The use-case examples earn their place by helping intent matching. It is slightly dense but every sentence contributes useful information.

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 read-only, zero-required-parameter lookup tool, the description sufficiently explains what the data means, what is included, and which natural-language intents map to it. It does not have an output schema, but describing rank and tier plus the optional team/tier/limit parameters from the schema gives the agent enough to invoke and interpret results correctly.

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 parameters are already well documented with types, defaults, and constraints. The description reinforces usage patterns like team-based queries but does not add meaning beyond the schema. A baseline of 3 is appropriate because the schema does the heavy lifting.

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 NFL power ratings (PWR) for all 32 teams and defines the metric with a formula. It goes well beyond the tool name by explaining that PWR measures points per game above an average team on a neutral field, and notes that rank and tier are included. It does not explicitly compare itself to sibling tools, but the power-ratings focus is distinct enough among the listed siblings.

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 gives concrete query phrasings that should route to this tool, such as 'best NFL teams by power rating', 'is Baltimore overrated', and 'how good is Kansas City'. This gives an agent clear contextual triggers. It does not state when not to use it or name alternative tools, so it stops short of full routing guidance.

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