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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, indicating a safe, read-only, idempotent operation. The description adds extra context: it advises passing async:true to avoid timeout, implying the tool can be long-running. It also specifies the return includes a job_id when async is true. The description does not contradict annotations and provides useful behavioral guidance beyond the annotations.

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 three concise sentences. The first sentence defines purpose and outputs. The second lists inputs and outputs. The third gives use case and a crucial tip about async. Every sentence earns its place with no fluff or repetition of schema content.

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?

The description, combined with the full input schema and annotations (readOnlyHint, openWorldHint, idempotentHint), provides a complete picture. The tool has an output schema (not shown but confirmed), so return values are documented. The description covers inputs, outputs, use case, and async behavior. It comprehensively sets expectations for a pre-deployment audit 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?

The input schema already provides descriptions for all four parameters (dataset_id, risk_threshold, include_bias_metrics, async). The tool description adds minimal value: it specifies that dataset_id is a Hugging Face ID or URL (already in schema) and that risk_threshold is optional (also implied by default). The async parameter is mentioned in context, but the schema already explains its behavior. With high schema coverage, the description's contribution is limited.

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 tool audits AI training datasets for EU AI Act compliance with bias detection and risk assessment. It specifies exact inputs (dataset identifier and optional risk thresholds) and outputs (compliance score, bias metrics, etc.). This distinguishes it from sibling tools like ai_act_incident_response or ai_act_sandbox_regulatory_sandbox, which focus on other aspects of AI Act compliance.

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 says 'Ideal for pre-deployment risk evaluation' and advises using async:true to avoid timeout. It provides context on when to use (before deployment) but does not explicitly say when not to use or compare to alternatives like ai_governance_pilot or the full report tools. A clear use case is given, but exclusions are missing.

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