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Frihet-io

Frihet MCP Server

Get Modelo 347 Summary (Operations >€3,005 Annual Recap)

get_modelo_347_summary
Read-onlyIdempotent

Retrieves the annual Modelo 347 summary for Spain, listing clients and vendors with transactions over €3,005. Returns per-party totals for the given tax year.

Instructions

Get annual informative summary of operations exceeding €3,005 per counterparty (Modelo 347, Spain). Returns per-party totals for clients and vendors above the threshold. Example: period='2025' / Obtiene el resumen anual de operaciones con terceros superiores a 3.005€ (Modelo 347). Devuelve totales por cliente/proveedor que superen el umbral.

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
Behavior4/5

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

Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the burden is lower. The description adds useful context: it returns per-party totals for clients/vendors above threshold, and includes an example. No contradictions with annotations.

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

Conciseness3/5

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

The description is bilingual (English then Spanish), making it longer than necessary. While front-loaded with key information, it could be more concise by using only one language or a shorter note. Still structured and readable.

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's simplicity (one optional parameter, output schema exists, annotations cover safety), the description is complete. It explains the purpose, threshold, return content, and provides an example. No gaps for an agent to select and invoke correctly.

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?

Schema coverage is 100% for the only parameter (period), with description 'Year in format YYYY'. The tool description adds an example (period='2025') and repeats the format in Spanish, adding marginal value beyond the schema. Baseline 3, example raises to 4.

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 gets an annual informative summary of operations exceeding €3,005 per counterparty (Modelo 347, Spain). It specifies the verb 'Get' and the resource 'Modelo 347 Summary', and distinguishes from sibling tools like get_modelo_130_summary by naming the specific form and threshold.

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 implicitly indicates when to use this tool (for the Modelo 347 annual summary), but does not explicitly state when not to use it or provide alternatives. Given the specific naming and context, it is fairly clear, but lacks explicit usage guidance.

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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