eval_bias

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

With no annotations provided, the description carries full burden. It explains the QAG-graded mechanism (five yes/no questions), score range (0.0-1.0), and return fields (score, passed, reason, threshold, evaluator). It also notes that input contextualizes whether disparate treatment is justified. Minor gaps: no discussion of failure modes or rate limits.

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?

Description is well-structured: first sentence defines purpose, then mechanism, use cases, args, and returns. Every sentence adds value, and the most critical information is front-loaded.

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?

Given 3 parameters, no nested objects, and an output schema (whose structure is described), the description is thorough. It covers purpose, usage, mechanism, all parameters, and return format. No gaps for an evaluator tool.

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 0%, so description must carry all parameter meaning. It does so comprehensively: explains 'input' as the prompt for context, 'output' as the LLM response, and 'judge_model' with a default provider:model. Adds significant meaning beyond the raw 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 identifies the tool's purpose with a specific verb ('Detect') and resource ('bias'), listing multiple bias types (gender, racial, political, etc.). It distinguishes from siblings like eval_toxicity by focusing on fairness audits rather than harmful content.

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 explicitly states when to use this tool: 'fairness audits on generated content, summarisation of user-submitted text, or hiring/lending-adjacent LLM pipelines.' It provides clear context but does not explicitly state when not to use it or list alternatives.

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