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Frihet-io

Frihet MCP Server

Get Modelo 180 Summary (IRPF Rentals Annual)

get_modelo_180_summary
Read-onlyIdempotent

Retrieve annual IRPF withholding summary for rental income (Spanish Modelo 180). Get total retentions per tenant, property, and annual aggregate for a given year.

Instructions

Get IRPF annual informative summary for rental income withholdings (Modelo 180, Spain). Returns total retentions per tenant, property, and annual aggregate. Example: period='2025' / Obtiene el resumen anual de retenciones sobre alquileres para el Modelo 180. Devuelve retenciones totales por inquilino, inmueble y agregado anual.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNoYear in format YYYY (e.g. '2025') / Ejercicio en formato YYYY

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
modelNo
monthsNoMonths covered by the period (YYYY-MM)
periodNoYYYY-QN (303/130) or YYYY (390)
summaryNo
deadlineNo
readonlyNoMarks the payload as an informational summary, never filed to AEAT
totalDueNo
modelo130No
modelo303No
modelo390No
modeloCodeNo
totalsByRateNo
totalDeductibleNo
Behavior3/5

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the return structure (retentions per tenant, property, annual aggregate) but does not disclose additional behavioral traits such as rate limits or authentication requirements.

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 concise, with two sentences in English followed by Spanish translation. It front-loads the purpose and includes an example, with no extraneous content.

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

Completeness4/5

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

Given one optional parameter, rich annotations, and an output schema (not shown), the description adequately explains the tool's functionality. It mentions return aggregates but does not explicitly state that the period is optional (though schema coverage implies it). Minor gap for full completeness.

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?

The schema covers the single 'period' parameter with 100% description coverage, specifying the YYYY format. The description provides an example ('period='2025'') which adds marginal value but does not significantly expand on the schema.

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 the tool retrieves the IRPF annual informative summary for rental income withholdings (Modelo 180, Spain), specifying the returned data (retentions per tenant, property, annual aggregate). The title and example further clarify the scope, distinguishing it from other modelo summaries.

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

Usage Guidelines3/5

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

The description implies usage for Modelo 180 via the title and tax form name, but does not explicitly state when to use this tool versus alternatives like get_modelo_130_summary. With many sibling tools, explicit guidance on selection would improve clarity.

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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