Xearno Tools

Mexico Vacation Days, Prima Vacacional & Aguinaldo (Vacaciones Dignas)

mexico_vacaciones_aguinaldo

Read-only

Your legal vacation days under the 2023 Vacaciones Dignas reform, plus the prima vacacional and year-end aguinaldo in pesos. Computes your statutory vacation days under Mexico’s Vacaciones Dignas reform (LFT Art. 76, in force 1 Jan 2023), the 25% prima vacacional (Art. 80), and the 15-day aguinaldo due by 20 December (Art. 87), pro-rated for partial years. The internet — and AI trained on it — is saturated with the pre-2023 table that gave just 6 days in year one; the reform doubled that to 12 and re-banded the rest, and the +2-days-per-five-years banding after year 5 is precisely where models and stale HR pages still get it wrong. All amounts use your base salary: the SDI (integrated wage) is for IMSS only, and using it here double-counts the benefits being calculated.

Input Schema

TableJSON Schema

Name	Required	Description
`serviceYears`	No	Completed years of service Count FULL years only — vacation entitlement vests on completing each year of service (2 years 8 months = 2). Enter 0 if you have not yet completed your first year.
`monthlySalary`	No	Monthly base salary (MX$) Your base salary (cuota diaria = monthly ÷ 30) — NOT the SDI. The integrated wage (salario diario integrado) is for IMSS contributions only; using it here double-counts the very benefits being calculated.
`daysWorkedThisYear`	No	Days worked this calendar year For the aguinaldo pro-rata: worked less than the full year (started mid-year, leaving early) and the 15 days scale by days worked ÷ 365. Leave 365 for a full year.

TDQS

A4.1/5.0

Behavior4/5

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

Annotations indicate readOnlyHint=true, consistent with a calculator. The description adds transparency about pro-rata calculations, the reform effective date, and the danger of using outdated data. 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 moderately concise but includes necessary legal references and warnings. It could be slightly tighter (e.g., removing the final sentence about SDI double-counting) without losing critical information.

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

Completeness3/5

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

The description explains inputs and purpose well but does not explicitly describe the output format (e.g., whether results are separate values or combined). Since no output schema exists, this gap reduces completeness for an agent.

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%, but the description adds meaningful context: for serviceYears it clarifies counting full years only; for monthlySalary it warns against using SDI; for daysWorkedThisYear it explains pro-rata logic. This adds value beyond the schema descriptions.

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 computes vacation days, prima vacacional, and aguinaldo under the 2023 Vacaciones Dignas reform. It specifies the relevant LFT articles and distinguishes from outdated pre-2023 tables, making the purpose very specific and unambiguous.

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 provides clear context for use: Mexican statutory benefits with reform details, and warns against using SDI base salary. However, it does not explicitly state when not to use this tool (e.g., for other countries) or mention alternative tools for different scenarios.

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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TDQS

A3.9/5.0

Disambiguation4/5

Each tool targets a distinct niche (e.g., specific country tax rules, loan types, or legal calculations), with detailed descriptions that clarify boundaries. However, the large number of tools (66) could cause some confusion for an agent trying to select the right one for a general query, especially when multiple tools relate to the same country.

Naming Consistency4/5

Tool names follow a mostly predictable pattern: lowercase words separated by underscores, often starting with a country name (e.g., 'uk_stamp_duty_sdlt') or a topic (e.g., 'compound_growth'). There are minor deviations, such as abbreviations ('npv_irr', 'sip') and varying use of verbs, but overall the naming is clear and consistent.

Tool Count3/5

At 66 tools, the server is unusually large and covers an extensive range of financial and legal calculators. While each tool justifies its existence, the count exceeds the typical well-scoped range (3–15), making the server feel bloated. A more modular design might improve coherence.

Completeness4/5

The tool set covers a wide array of domains: personal income taxes, property taxes, loan calculations, investment returns, and specific country regulations. Minor gaps exist (e.g., missing tools for corporate taxes, general retirement planning, or insurance), but the overall coverage is thorough and addresses many niche scenarios that general AI handles poorly.

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