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enaho_describir_modulo

Read-onlyIdempotent

Inspect a survey module's metadata before joining or estimating: level, keys, expansion factor, variable and row counts, plus domain notes explaining row losses during merges.

Instructions

Ficha de un modulo: nivel, llaves, factor esperado, tamano y trampas.

Consultala antes de unir o estimar. Incluye el numero de variables y de filas segun el indice (sin descargar), si el modulo trae factor de expansion propio, y las notas de dominio que explican por que ese modulo pierde filas al unirse con otro.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
anioYes
moduloYesCodigo del modulo: '01', '05', '34' (Sumaria)...
encuestaNoEncuesta del INEI sobre la que operar. Por defecto 'enaho'. Valores: enaho, enaho-panel, endes, enapres, enut, enares, ena, epen-departamentos, epen-ciudades, epen-lima, epe-lima, enapref, enco, cenagro, mapa-pobreza. Cada encuesta tiene sus propias llaves de union y su propio factor de expansion; no se pueden mezclar entre si.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already mark it as read-only, idempotent, and non-destructive. The description adds substantial behavioral detail: it reports the number of variables and rows without downloading, indicates whether the module has its own expansion factor, and explains why rows are lost when joining. This goes well beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with a high-level summary of what the tool returns, followed by practical details. It is concise with no wasted words, effectively communicating purpose and key behavioral facets.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool is a metadata card, the description covers the main outputs, the use case (before joining or estimating), and important behaviors (no download, row-loss explanation). The presence of an output schema and rich annotations reduces the need for the description to explain return values in detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67% with some parameter descriptions (modulo and encuesta), but the tool description does not add additional parameter semantics. The description focuses on output content rather than clarifying the meaning or format of parameters like 'anio' or 'modulo'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides a 'Ficha de un modulo' with specific elements: nivel, llaves, factor esperado, tamano y trampas. This is a specific verb+resource+output summary that distinguishes it from sibling tools like listing or downloading modules.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Consúltala antes de unir o estimar', giving clear context for when to use the tool. However, it doesn't explicitly name alternative tools or provide exclusion criteria, only implies this is a preliminary step to joining or estimating.

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

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