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

Annotations declare readOnlyHint, openWorldHint, and idempotentHint. The description adds behavioral context by mentioning the async option to avoid timeout, implying the tool can be slow. It does not contradict annotations and provides useful operational guidance.

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 one paragraph, concisely covering purpose, inputs, outputs, use case, and async note. It is front-loaded with the main action and uses minimal words effectively.

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 the tool's complexity (4 parameters, async support, output schema), the description covers all essential aspects: what it does, inputs, outputs, ideal use case, and async behavior. The output schema likely provides return values, so description is complete.

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 50%. The description names dataset_id and risk_threshold, and mentions async in the last sentence, but omits include_bias_metrics. It adds meaning by explaining dataset_id as 'Hugging Face ID or URL' and risk_threshold as optional, but the coverage gap prevents a higher score.

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 explicitly states the tool audits AI training datasets for EU AI Act compliance with bias detection and regulatory risk assessment. It specifies inputs and outputs, and the 'Ideal for pre-deployment risk evaluation' distinguishes it from siblings like ai_act_incident_response. The verb 'audit' and resource 'training data' are clear and specific.

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 says 'Ideal for pre-deployment risk evaluation' and advises passing async:true to avoid timeout, giving context on when to use. It does not explicitly state when not to use or list alternatives, but the sibling set implies this is for proactive audit, not incidents.

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