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

With no annotations, the description fully carries behavioral disclosure. It reveals that the data is design intentions and self-assessment, not audited, statuses are limited to specific values, and there is deliberately no 'compliant' status. This is rich behavioral context beyond what an annotation would typically state.

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 longer than average but every sentence contributes to clarifying purpose, limitations, and usage. The final sentence about Makuri being a specific platform is slightly tangential but important for avoiding general knowledge. It is front-loaded and well-structured.

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?

Despite no output schema, the description sufficiently explains what is returned (posture across five regulations, statuses) and how to interpret the results. It covers the key caveats and usage context. It could have specified the exact return structure (e.g., object mapping), but the description is complete enough for correct invocation.

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 schema already documents the `regulation` parameter with an enum and description, achieving 100% coverage. The description adds value by expanding acronyms (GDPR-K as children data) and listing regulations, which helps interpret the enum values. This slightly exceeds the baseline of 3.

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 returns Makuri's regulatory posture across five specific regulations (EU AI Act, GDPR, GDPR-K, COPPA, ISO 42001), with the caution that it's self-assessment, not certified. This distinguishes it from sibling get_* tools by focusing on compliance posture.

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?

Explicitly says 'Use when the user asks about regulatory compliance, AI Act classification, or data protection for children' and instructs to present results as posture, not certification. This gives clear when-to-use context and implicit alternative avoidance.

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