Xearno Tools

Saudi Arabia End-of-Service Award (EOSB) Calculator

saudi_end_of_service

Read-only

Your end-of-service award under Saudi Labor Law Arts. 84–87 — the resignation ladder and the wage base, done right for KSA (not the UAE). Computes the end-of-service award (EOSB, مكافأة نهاية الخدمة) under Saudi Labor Law: the Art. 84 base (half a month per year for the first five years, a full month per year after, on the LAST actual wage), then the branch the termination reason selects — full award for employer-side endings, the Art. 85 ladder (0 / 1/3 / 2/3 / full at exactly 2, 5, and 10 years) for resignation, and ZERO for an Art. 80 misconduct dismissal. General AI reliably gets this wrong by porting UAE rules into KSA: the UAE abolished resignation reductions and uses basic-only wage; Saudi kept the ladder and uses the actual wage including allowances.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`reason`	No	How is employment ending? The decisive input. The reason flips the answer between 0× and 1× of the same formula — the input models never ask for. Ordinary resignation is cut by the Art. 85 ladder; an Art. 80 dismissal pays nothing at all.	employerTermination
`monthlyWage`	No	Last monthly wage (actual wage, SAR) (SAR) Your LAST monthly actual wage: basic + housing + transport + fixed allowances. The Saudi base is the gross wage, NOT basic-only (that is the UAE rule). Contracts may exclude fluctuating commissions per Art. 86 — commission earners should use the last-12-months average of the commission part.
`serviceYears`	No	Years of service Total continuous service in years — decimals are fine, pro-rata applies to fractions of a year in both tiers.

TDQS

A4.5/5.0

Behavior5/5

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

Annotations indicate readOnlyHint: true, confirming safe read operation. The description goes beyond by detailing the calculation logic, including the Art. 85 ladder for resignation, zero for Art. 80 dismissal, and the wage base using actual wage including allowances. It also mentions handling of fluctuating commissions per Art. 86. This provides comprehensive behavioral insight.

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 verbose with legal citations and redundancy ('done right for KSA (not the UAE)'). While structured and front-loaded, it could be more concise without losing critical information. Every sentence adds value, but length is above optimal.

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 complexity and no output schema, the description covers the formula, variants by reason, wage base definition, and common pitfalls. It is nearly complete; missing explicit mention of return value format but implied. Adequate for agent use.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds significant meaning: 'reason' enum is explained with legal context, 'monthlyWage' clarifies it is gross wage not basic-only (UAE rule), and 'serviceYears' notes pro-rata treatment. This adds substantial value beyond the schema.

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 the end-of-service award under Saudi Labor Law, specifying articles 84-87. It distinguishes from UAE rules, relevant given sibling tools like uae_gratuity. The verb 'computes' and resource 'end-of-service award' are 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 explains when to use the tool (KSA calculations) and warns against porting UAE rules. It implies alternatives by referencing UAE but does not explicitly name sibling tools like uae_gratuity. The 'reason' parameter usage is clarified. Overall clear guidance on context but lacks explicit exclusion of alternatives.

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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