Artemisia · Aguas SB

Datos públicos de una depuradora urbana de España

consultar_depuradora

Read-onlyIdempotent

Busca una depuradora urbana de España en el Observatorio de Aguas SB (2.174 plantas del registro europeo Waterbase-UWWTD, dato declarado del año 2022) por su código oficial o por nombre, municipio o provincia, y devuelve el dato declarado (capacidad y carga en habitantes equivalentes, tratamiento, nitrógeno, fósforo, UV), la normativa que le aplica (demarcación y organismo que autoriza el vertido a cauce, norma o ordenanza de referencia de su comunidad para el alcantarillado, tramo de la Directiva (UE) 2024/3019), el escenario energético publicado (estimación, no consumo real) y la URL de su ficha. Determinista, sin modelo de lenguaje. No dice quién la gestiona ni si cumple: es un dato declarado, no una inspección.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "consultar_depuradora"
  }
}

Input Schema

Name	Required	Description
`codigo`	No	Código oficial del registro europeo (p. ej. ES12270190000000E), si se conoce
`nombre`	No	Nombre de la depuradora o del municipio (p. ej. "Foz", "Galindo", "Sur")
`municipio`	No
`provincia`	No

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 26, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the annotations (readOnly, idempotent, closed-world), it discloses that the operation is deterministic with no language model, that the figures are declared self-reported data rather than an inspection, and that the energy scenario is an estimate not real consumption. These are exactly the caveats an agent needs before presenting results as fact.

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

Conciseness4/5

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

Front-loads the verb, resource and source before the return inventory, and every clause carries information (dataset size, year, caveats). The middle sentence is long and heavily parenthetical, but nothing is genuinely redundant.

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?

No output schema exists, so the description takes on the burden and enumerates the return payload (capacidad y carga en HE, tratamiento, nitrógeno, fósforo, UV, normativa, demarcación, tramo Directiva, escenario energético, URL de ficha). Combined with the provenance and limitation notes, an agent can call it and interpret results without further context.

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

Parameters4/5

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

With 50% schema coverage, the description compensates by naming all four lookup keys and clarifying that 'nombre' can be either the plant name or a municipality (e.g. "Foz", "Galindo", "Sur"). It does not explain matching behavior when multiple filters are combined, which keeps it below a 5.

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?

States a specific verb ('Busca'), an exactly scoped resource (depuradora urbana de España) and the concrete source (Observatorio de Aguas SB, Waterbase-UWWTD, dato 2022). It also enumerates the returned fields, so an agent knows precisely what this tool yields versus a generic water query.

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?

Explains the two lookup paths (código oficial, or nombre/municipio/provincia) and explicitly bounds usage with 'No dice quién la gestiona ni si cumple'. It does not name sibling tools such as estimar_depuracion or consultar_aguas_sb as alternatives, so routing is implied rather than spelled out.

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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