Classify AI System Under EU AI Act

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

Annotations indicate readOnly and idempotent. Description adds key behaviors: signals precedence, deterministic classification, return of follow-up questions, and that Art. 6(3) exceptions cannot be auto-applied. No contradictions with 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 concise and well-structured. It front-loads the purpose, then lists inputs and outputs, and ends with a critical note about the exception. Every sentence adds value without unnecessary detail.

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 (many signals, nested objects) and that an output schema exists, the description provides adequate context. It covers inputs, key behaviors, and exceptions. However, it could briefly mention that output schema exists for exact return fields.

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%. Description mentions main parameters (description, use_case, signals) and explains signal precedence. However, the role parameter is not mentioned, and the description does not add significant meaning beyond the schema for individual fields.

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's purpose: classify an AI system's risk level under the EU AI Act. It lists specific inputs (description, use_case, signals) and outputs, and mentions a sibling tool for Art. 6(3) exceptions.

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 specifies that signals take precedence over text matching and notes that Art. 6(3) exceptions should use a separate tool. However, it does not provide guidance on when to use this tool versus other siblings like euaiact_calculate_penalty or euaiact_get_article.

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