enaho_pobreza_fgt

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the description adds value beyond them. It reveals that the tool issues a warning when the welfare variable and poverty line are in inconsistent units, which is a notable behavioral trait not captured by the 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 concise at five sentences, each earning its place: a definition, a conceptual explanation of the indices, and a practical warning. It is front-loaded with the core purpose and contains no 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?

The description covers the core purpose, index interpretations, and a key common error, while the input schema documents parameters and the output schema presumably covers return values. It could mention additional data preparation steps beyond unit matching, but for a complex statistical tool, this is adequately complete.

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 88% schema description coverage, parameters are already well-documented. The description adds conceptual depth by explaining what the alphas parameter represents (0, 1, 2) and reinforces the critical unit-consistency requirement between variable and linea, complementing the schema's parameter details.

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 'Los tres indices FGT con error estandar bajo diseno complejo', clearly identifying the tool as computing FGT poverty indices with standard errors under complex design. It further distinguishes FGT0, FGT1, and FGT2, making it distinct from sibling poverty/inequality tools.

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 explains the meaning of the three indices and why they are reported together, implying use for poverty measurement. However, it never explicitly states when to prefer this tool over alternatives (e.g., enaho_desigualdad) or mentions exclusion scenarios.

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