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gsaccardi

Dichiarino

by gsaccardi

analizza_certificazione_unica

Analyze Italian Certificazione Unica data to detect anomalies in IRPEF withholding, work days, and internal consistency across income fields.

Instructions

Analizza i dati della Certificazione Unica (CU) e rileva eventuali anomalie.

    Verifica la coerenza interna dei dati CU: controlla se l'IRPEF trattenuta
    è plausibile rispetto al reddito dichiarato, e se i giorni di lavoro
    sono nel range corretto.

    Args:
        reddito_lordo: Reddito lordo (punto 1 CU).
        imponibile_previdenziale: Imponibile previdenziale (punto 6 CU).
        irpef_trattenuta: IRPEF trattenuta (punto 21 CU).
        addizionale_regionale_trattenuta: Addizionale regionale trattenuta (punto 22 CU).
        addizionale_comunale_trattenuta: Addizionale comunale (punti 23+24 CU).
        giorni_lavoro: Giorni di lavoro/pensione nell'anno (punto 5 CU).
        tipo_reddito: "lavoro_dipendente" | "pensione" | "lavoro_assimilato".
        anno: Anno di imposta. Default: 2024.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
annoNo
tipo_redditoNolavoro_dipendente
giorni_lavoroYes
reddito_lordoYes
irpef_trattenutaYes
imponibile_previdenzialeYes
addizionale_comunale_trattenutaYes
addizionale_regionale_trattenutaYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses specific behavioral checks (IRPEF plausibility, days range) which go beyond a generic statement. It doesn't explicitly state read-only nature or error handling, but the analysis focus and detailed validation logic provide good transparency.

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 well-structured: a two-sentence summary followed by a necessary Args list. There is no redundant text, and the Args list is essential given the 0% schema description coverage.

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?

The description covers purpose, validation logic, and all parameters comprehensively, and the output schema covers return values. The only gaps are the minor default value discrepancy and no explicit mention of side effects, but overall it is complete for an analysis tool.

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?

The description offers extensive parameter explanations with CU point references (e.g., 'reddito_lordo: Reddito lordo (punto 1 CU)'), which the schema completely lacks. However, it incorrectly states the default for 'anno' as 2024, while the schema says 2025, creating a conflicting instruction that undermines the guidance.

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 begins with a specific verb 'Analizza' and names the resource 'Certificazione Unica' followed by the action 'rileva eventuali anomalie'. It clearly distinguishes from sibling tools like calcola_irpef or calcola_detrazione by focusing on validation and anomaly detection rather than calculation.

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 clearly implies when to use this tool: when you have CU data and need to verify internal coherence and plausibility. However, it does not explicitly mention alternatives or when not to use it, stopping short of the highest score.

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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