enaho_distribucion

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

Beyond the annotations (readOnly, idempotent, not destructive), the description discloses key behavioral details: cuts are weighted quantiles based on population rather than rows, and results are descriptive without standard errors. It also explains the conceptual relationship to the Gini coefficient, adding context not present in structured metadata.

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 concise and front-loaded, with the purpose in the first sentence and supplemental detail in two short paragraphs. Every sentence earns its place, covering purpose, methodology, limitations, and alternative tools without redundancy.

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 moderate complexity, rich schema descriptions, and existing output schema, the description is contextually complete. It explains the core functionality, key methodological nuance, and when to use a sibling tool, while the output schema handles return-value documentation.

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?

The input schema already provides descriptions for all five parameters (100% coverage), so the description does not need to compensate. It adds methodological context about weighted quantiles but does not elaborate on individual parameter semantics beyond what the schema already offers.

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 opens with 'Reparto por deciles con participacion en el total y curva de Lorenz', which clearly and specifically states the tool computes distribution shares and Lorenz curves by weighted quantile groups. This distinguishes it from sibling tools like enaho_desigualdad, which is explicitly mentioned as the alternative for Gini with confidence intervals.

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 gives explicit guidance on when to use this tool versus enaho_desigualdad: 'para el Gini con intervalo de confianza usa enaho_desigualdad'. It also clarifies that this tool is descriptive and lacks standard errors, advising the alternative for inferential needs.

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