sportsdataio_nfl_teams

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

Beyond the annotations (readOnlyHint, idempotentHint), the description adds valuable context: the return shape is unverified from vendor docs and should be treated as approximate, and authentication requires a provider key. This honesty about uncertainty and auth requirements exceeds the baseline annotation coverage.

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 and information-dense. Every sentence adds value: the summary line, return shape example, verification caveat, usage example, and auth note. No unnecessary fluff.

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 no output schema, the description provides a detailed return shape sample and auth requirements. It acknowledges the shape is unverified, which is honest but also means the agent must inspect actual payloads. For a zero-parameter static data tool, this is sufficient and transparent.

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?

There are no parameters, so the baseline is 4. The description compensates by documenting the return shape with specific field names and PascalCase convention, adding semantic meaning even though no parameters need explanation.

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 resource ('NFL franchises') and the specific data included (conference, division, stadium, coach). The example 'Every NFL team' reinforces the intent. This distinguishes it from sibling NFL tools like sportsdataio_nfl_scores.

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 implies the tool is for retrieving NFL team metadata but does not explicitly state when to use it versus alternative tools or provide exclusions. The 'Example: Every NFL team' gives a use case but no guidance on alternatives.

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