classify_ai_system_risk

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

With no annotations, the description carries the full burden of disclosing behavior. It explains that the tool classifies into four categories and returns obligations and deadlines, adding legal context. However, it does not disclose any limitations, error conditions, or side effects, which is a gap for a tool with no annotation support.

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 and concise: a clear purpose sentence, a list of output categories, a bulleted Args list, and a Returns line. Every sentence adds value, with no redundancy or fluff. The formatting is easy to scan.

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 covers the classification framework, legal basis, and return value, and the presence of an output schema obviates the need for detailed return specifications. It is complete for its complexity, though it could mention how the boolean flags relate to the classification logic or cross-reference sibling tools for a fuller context.

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?

Despite 0% schema description coverage, the description provides a brief meaning for each of the 11 parameters (e.g., 'biometric_data: Uses biometric identification/categorization'). This compensates well for the schema's lack of descriptions, though some entries are terse and could benefit from more detail (e.g., 'interacts_with_users').

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: 'Determine AI system risk level per EU AI Act classification framework.' It specifies the output categories (PROHIBITED, HIGH-RISK, LIMITED-RISK, MINIMAL-RISK) and the legal basis (Articles 5, 6, and 50), distinguishing it from sibling tools that focus on specific aspects like prohibited practices or role determination.

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 implies usage for classifying AI system risk under the EU AI Act, but it does not explicitly state when to use this tool versus alternatives like 'check_prohibited_practices' or 'determine_eu_ai_act_role'. No exclusions or alternative guidance is provided, so it relies on the user to infer the appropriate context.

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