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

Annotations already establish readOnly/idempotent/non-destructive, so the description only needs to add what they cannot. It does so usefully: 'Returns a starting range in EUR, not a prediction of a real fine' sets output expectations and guards against over-reliance, and the 'step 2' scoping signals the method's boundary. No rate limits or auth caveats, but none are relevant for a closed-world calculator.

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?

Roughly four sentences, front-loaded with the trigger question and immediately followed by the method, inputs, and output caveat. Every sentence contributes, though the two quoted trigger phrasings are somewhat redundant with each other and could be compressed into one.

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?

For an 8-parameter, 2-required calculator with no output schema, the description covers triggers, method, input expectations, and the nature of the return value (a EUR starting range). It is nearly self-sufficient; the only minor gap is no explicit statement about behavior when factual inputs are missing and seriousness is also omitted.

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?

With 75% schema coverage and 5 enum-typed parameters, the schema carries most of the load. The description adds real value by explaining the inference contract: seriousness may be omitted and derived from the supplied facts (fault, damage, duration, data categories, people affected). It also frames the Art. 83(4)/83(5) tier and turnover adjustment as the calculation inputs, reinforcing what each drives.

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?

Names a specific verb+resource (GDPR fine estimation) and frames it with the exact user questions it answers ('how big could a GDPR fine be?', 'how are GDPR fines calculated?'). It also states the method scope (step 2 of EDPB Guidelines 04/2022), which cleanly distinguishes it from siblings like gdpr_breach_decision or dora_checklist that handle obligations rather than quantification.

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?

Explicit trigger phrasings plus concrete guidance on what to supply ('Give the seriousness, or the facts to let ClevSec suggest it') make the intended invocation context clear. It does not, however, name an alternative or state when NOT to use it (e.g., that it computes a range only, not a decision on whether a breach occurred, which is gdpr_breach_decision's job).

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