calcola_risultato_dichiarazione

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

With no annotations provided, the description carries the full burden. It discloses that the tool combines inputs to determine an outcome, which is useful, but it does not mention side-effect-free behavior, error handling, or any validation logic. The algorithmic summary is present but not deeply transparent about failure modes or limitations.

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

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

The description is well-structured: a brief purpose statement followed by a bulleted Args list. It is appropriately sized for a 9-parameter tool, front-loads the key outcome, and every line adds value. No fluff or repetition.

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

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

The output schema covers return values, and the description explains all parameters well. However, it omits explicit notes on optionality (though schema shows defaults/nullable) and contains the anno default mismatch, which reduces reliability. For a 9-parameter tool, these gaps make it slightly less complete than ideal.

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

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

The description includes a detailed Args section explaining every parameter, including units, examples, and defaults, which is essential given the schema has zero descriptions. However, there is a discrepancy: the description says anno default 2024, while the schema says 2025, which could mislead agents relying on the text. This prevents a perfect score.

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

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

The description states the tool calculates the final 730 declaration result (refund or IRPEF debt), combining gross IRPEF, deductions, and withholdings. This is a specific verb+resource that clearly distinguishes it from sibling tools like calcola_irpef or calcola_detrazione_lavoro, which feed into this final aggregation.

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

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

The description implies usage context by referencing sibling tools for input parameters ('vedi calcola_detrazioni_familiari', 'vedi calcola_oneri'), indicating this is the final computation after those are obtained. It does not explicitly state when to use vs. alternatives, but the context is clear enough for an agent to infer the appropriate workflow.

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