detect_bias

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

With no annotations provided, the description fully covers behavioral traits: it states the tool generates structured output without side effects, is deterministic, has rate limits (free: 10/day, pro: unlimited), and requires no authentication for basic usage. No contradictions.

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 clear sections (Args, Behavior, When to use, When NOT to use) and each sentence adds value. It is appropriately sized and front-loaded with the main purpose.

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?

While the description covers many aspects, it lacks details on the output format (e.g., what 'structured output' contains) and error handling. Given no output schema, the agent may need more context about return values.

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?

Input schema has 0% description coverage, so the description compensates by explaining each parameter: model_output as 'the AI-generated text,' protected_attributes with example and auto-detection note, and api_key as optional for pro tier. This adds significant meaning beyond the bare schema.

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 it 'Analyze text for demographic bias patterns, stereotyping, and unfair language.' The verb 'Analyze' and specific resource 'text for bias' make the purpose explicit, and it distinguishes itself from siblings like 'fairness_metrics' or 'mitigation_recommendations.'

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 includes dedicated 'When to use' and 'When NOT to use' sections, providing context such as compliance needs and legal disclaimer. However, it does not explicitly compare to sibling tools or provide exclusions for alternative use cases.

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