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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so safety is covered. The description adds real value by disclosing what the audit produces — article-by-article conformity, satisfied controls, open gaps, and remediation guidance — which is useful given no output schema exists.

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?

Two dense sentences with zero filler; the audit action and framework scope are front-loaded before the output description. Every clause earns its place.

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 annotations covering the safety profile and no output schema, the description adequately covers inputs and the shape of the result. It could be slightly more complete by clarifying how diagram_id and scan_id combine, but nothing essential is missing.

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 description coverage is 100%, so the baseline is 3. The description expands framework identifiers into human-readable names (EU AI Act 2024, DORA 2022, NIST AI RMF 1.0, ISO 42001) and implies cross-correlation via the scan_id mention, but adds no syntax or default guidance beyond the schema.

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?

Specific verb ('Audit') plus two resources (architecture diagram or code scan) and a clear target (statutory AI governance frameworks). It distinguishes itself from generic siblings like compliance_scan, but never names an alternative to route against.

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 the audit use case but gives no explicit when-to-use, prerequisites, or comparison to siblings such as compliance_scan or get_framework_coverage. The agent must infer selection from the name alone.

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