alcance_auditoria

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

Annotations already declare readOnlyHint=true, idempotentHint=true and openWorldHint=false, so the safety profile is covered structurally. The description adds genuine behavioral context beyond that: the category-based accumulation rules (Básica applies to all, Media to MEDIA/ALTA, Alta only to ALTA, CCN-STIC 808 §5), the meaning of `nivel_madurez_requerido`, and the `essential` flag semantics. This enriches what the raw annotations convey. No contradiction with annotations — the compute/read behavior described matches readOnlyHint=true.

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 dense but every sentence earns its place: purpose, input contract, accumulation rules, maturity levels, and the `essential` flag semantics. It is front-loaded with the purpose statement and organized in logical paragraphs. It is somewhat long and technical (CCN-STIC references, CMM level names), but the complexity of the domain justifies the length.

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?

Given an output schema exists (so return values need not be spelled out) and 12 parameters, the description covers the core semantics thoroughly: dimension inputs, accumulation behavior, maturity requirements, and essential flags. The only real gap is the output-formatting/pagination parameters (limit, cursor, compact, include_*) left unexplained, but those are largely self-evident from their names. It is complete enough for an agent to call the tool correctly for its primary purpose.

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 of explaining parameters. It does map the key semantic inputs — the dimension levels (integridad, autenticidad, disponibilidad, confidencialidad, trazabilidad) and their 'omitted if not valued' behavior — and explains output fields like `nivel_madurez_requerido` and `essential`. However, it never mentions `limit`, `cursor`, `compact`, `include_evidence`, `include_norm_text`, or `include_questions`, leaving 6 of 12 parameters undocumented in both schema and description. It compensates only partially for the coverage gap.

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 opening line states a specific purpose — the audit syllabus of a system, i.e., what will be asked — and the description makes clear it returns applicable measures with their accumulated verification requirements. It provides a concrete resource and verb. However, it does not differentiate itself from close siblings like `requisitos_auditoria` or `evidencias_auditoria`, which an agent could easily confuse with this tool, so it stops short of a 5.

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?

Gives explicit calling context: takes the same arguments as `declaracion_aplicabilidad` (dimension level per dimension, or omitted if not valued), explaining the input semantics needed to invoke it correctly. It also states the accumulation logic clearly. It does not, however, state when NOT to use this tool versus its siblings, so the guidance is strong on how but weaker on selection.

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