enaho_calidad

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false; the description adds substantial behavioral context by explaining what the diagnostics detect, why failures are dangerous, and that it returns apto_para_estimar with problems separated by severity. No contradiction with annotations.

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 front-loaded with the main purpose, then uses a structured bullet list to explain each of the five checks and their consequences. Every sentence adds value, and the markdown formatting makes it scannable; length is justified by the tool's complexity.

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 the tool's complexity, the description covers purpose, workflow timing, output content ('Devuelve apto_para_estimar y los problemas separados...'), and the key failure modes. It also complements the annotations and schema without relying on them; output schema exists so return-value detail is not required.

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 covers 100% of parameters with descriptions for dataset, salida, and formato, so the baseline is 3. The description does not add parameter-level detail; it mentions dataset only implicitly and leaves salida/formato behavior to 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?

The description clearly identifies the tool's specific action: determining whether a dataset is fit for its intended use ('Responde si un dataset sirve'). It lists five concrete diagnostic checks and explicitly distinguishes itself from enaho_perfil ('Es distinto de enaho_perfil, que describe'), so it differentiates from siblings.

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?

It explicitly names the alternative enaho_perfil and contrasts their purposes, and it gives workflow placement: 'Llamala despues de enaho_unir_modulos y antes de publicar cualquier cifra.' This is clear when-to-use guidance with an exclusion.

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