fairness_metrics

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

With no annotations provided, the description fully carries the burden. It states the tool is read-only, stateless, idempotent, includes rate limits (10/day free, unlimited pro), and clarifies authentication needs (optional api_key, no auth required for basic usage).

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 with sections but somewhat verbose; it repeats the comma-separated format twice. Every sentence adds value, but could be tightened.

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?

No output schema exists, and the description does not specify the return format or structure of the fairness metrics (e.g., list, dictionary, scores). This leaves the agent guessing about what the tool actually returns.

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 coverage is 0%, so description compensates well by explaining the comma-separated 'group:prediction' format for predictions and ground_truth, and the purpose of api_key for pro tier. Could add accepted value ranges or constraints.

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 'Calculate fairness metrics from prediction data' and provides specific metrics (equalized odds, calibration), distinguishing it from sibling tools like detect_bias or regulatory_check by focusing on metric calculation rather than detection or checks.

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?

Explicit 'When to use' and 'When NOT to use' sections provide context for compliance audits and gap analysis, and warn against using as legal advice. However, no direct comparison to sibling tools is given.

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