declaracion_aplicabilidad

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

Annotations already declare readOnlyHint=true, openWorldHint=false, and idempotentHint=true, and the description's 'Devuelve' is consistent with those — no contradiction. Beyond annotations, the description adds valuable behavioral context: accepted dimension values ('bajo'/'medio'/'alto', with 'basico' tolerated for compatibility), the rule that at least one dimension must be valued, omission when a dimension is not valued, the category being the highest level, and the semantics of `alternative: true` reinforcements (only one needed). This meaningfully exceeds what the annotations signal.

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 front-loads the purpose in the first line and uses line breaks to separate the recurrence of the input rules, output contract, and alternative-reinforcement semantics. Each sentence earns its place; there is minimal filler. The phrasing 'se omite si el sistema no la valora' is slightly unwieldy but not bloated. It is efficient for the amount of rule context it conveys.

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 a tool with 10 parameters and 0% schema coverage, the description covers the core dimension semantics and the output shape (category, required measures, reinforcement levels, alternative behavior), and an output schema exists to reduce the return-value burden. However, it does not address pagination (limit/cursor), the behavior or filtering effect of measure_codes, the meaning of compact, or what include_norm_text toggles. These gaps leave an agent uncertain about controlling output scope for a complex tool.

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 0%, so the description carries the burden. It usefully explains the five dimension parameters (integridad, autenticidad, trazabilidad, disponibilidad, confidencialidad) by specifying their value vocabulary and rules. However, the remaining five parameters — limit, cursor, compact, measure_codes, and include_norm_text — are left unexplained beyond their names; measure_codes as an output filter and compact/include_norm_text as format controls are not described. The compensation for the schema gap is partial.

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 clear, specific function: compute measures and reinforcements required of a system for its DdA (Declaración de Aplicabilidad), returning the system category and per-measure required levels. The verb 'Devuelve' plus the resource (system applicability) makes the purpose understandable. It does not explicitly name a sibling to distinguish from, but the computation-based nature is evidently distinct from listing tools like list_measures and search_measures.

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 use case — querying required measures for a system given its dimension security levels — but gives no explicit when-to-use vs alternatives, no exclusions, and no mention of sibling tools (list_measures, get_measure, search_measures, snapshot_status). An agent must infer that this tool aggregates applicability from dimension levels rather than listing static measures. There is no when-not-to-use guidance.

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