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

Annotations already declare readOnlyHint, idempotentHint and non-destructive behavior, so safety is covered. The description still adds meaningful context beyond them: the tool returns exactly 12 questions, each with options a, b and c — useful given there is no output schema. It stops short of describing error handling or whether the question set is fixed/versioned, so not a 5.

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?

Three short sentences, zero waste. The usage trigger comes first, the return shape second, and the required next action last, which matches the order an agent needs them.

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?

With no output schema, the description carries the return-value burden and does so by stating the count (12) and answer format (options a, b, c). Combined with the follow-up instruction to score_ai_practice_risk, an agent has everything needed to invoke it correctly.

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 tool takes zero parameters, which is the baseline-4 case; the empty schema leaves nothing for the description to clarify or compensate for. No parameter confusion is possible.

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 states a specific verb and resource — retrieves the 12 AI Practice Risk Check questions — and pins the audience (U.S. lawyer or law firm). It is clearly distinguishable from sibling score_ai_practice_risk because it returns questions rather than a risk score.

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?

It gives an explicit trigger condition ('when a U.S. lawyer or law firm wants to check how safely their firm uses generative AI') and names the exact follow-up tool with the sequencing required ('ask every question, then call score_ai_practice_risk'). An agent knows both when to call this and what to do next.

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