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tresor4k

macalc

calculate_leave_days

Calculate French paid leave accruals: 2.5 days per month, up to 25 days annually. Input employment start date and months worked to get accrued days, max annual days, and months needed to reach cap.

Instructions

Calculate French paid leave (congés payés): 2.5 days/month, max 25 working days/year. Returns: {accrual_per_month, accrued_days, capped_at_max, max_annual_days, days_to_max, months_to_max}. See list_bundles for related 'temps-rh' calculators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_dateYesYYYY-MM-DD — Employment start date
months_workedYesMonths worked in the reference period (1-12)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoComputed result. Object whose fields depend on the tool (e.g. {tax, marginal_rate, brackets} for tax tools, {volume_l, gallons} for volume tools).
formulaNoHuman-readable formula or method used (e.g. "I=P·r·t", "Magnus formula").
sourceNoAuthoritative source for the rule or formula (e.g. "Article 197 CGI", "NF DTU 21").
reference_urlNoLink to a calcul2 page documenting the calculation in detail.
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does not disclose whether the tool is read-only or has any side effects, but for a simple calculator, the lack of such disclosure is acceptable. It does detail the output fields, adding some behavioral context.

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 a single, dense sentence that immediately states the tool's purpose, specifies the key rules, and lists the return fields. Every piece of information earns its place; there is no fluff.

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 the tool's moderate complexity (French leave calculation with rules and multiple output fields), the description covers the essential business logic and references related tools. It could include more edge-case guidance, but it is largely sufficient.

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 input schema has 100% description coverage for both parameters, so the baseline is 3. The description adds value beyond the schema by providing the business rules (accrual rate and cap) and explicitly listing the output fields, which helps the agent understand the tool's behavior.

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 calculates French paid leave, specifies the accrual rate (2.5 days/month, max 25 days/year), and lists the output fields. This is a specific verb and resource, distinguishing it from numerous sibling tools that calculate other things.

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 directs users to 'see list_bundles for related 'temps-rh' calculators,' which hints at related tools for HR calculations. However, it does not explicitly state when to use this tool versus alternatives or when not to use it, leaving the agent to infer the context.

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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