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Xearno Tools

China Severance Pay Calculator 经济补偿金 (N / N+1 / 2N)

china_severance
Read-only

Statutory severance under China’s Labour Contract Law — the N / N+1 / 2N branch and the 3×-average-wage cap, done right. Computes statutory economic compensation (经济补偿金) on leaving a job in China: the base N (one month per year of service, with the ≥6-month rounding), whether it becomes N+1 (pay in lieu of notice) or 2N (unlawful termination), and the two caps that switch on together for high earners — the base capped at 3× the local average wage and years capped at 12. The termination reason is the input that flips the answer, so it is the first question.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoCity Sets the local average wage whose 3× caps the severance base — a figure you would otherwise have to look up. Pick “Other” (or override below) if your city isn’t listed.beijing
reasonNoHow is employment ending? The decisive input. Resignation with no employer fault pays nothing; unlawful termination doubles it. If unsure which Art. 40 case applies, note that the “+1” is only for a non-fault dismissal given without 30 days’ written notice.n
monthlyWageNoAverage monthly wage 月均工资 (pre-tax, incl. bonuses) Average of your last 12 months’ gross pay — base salary + bonuses + allowances, before tax and before your own social-insurance/fund deductions (应得工资). Excludes expense reimbursements.
localAvgWageNoLocal average wage 社平工资 override (optional) Leave 0 to use your city’s figure above. The severance-cap caliber is legally negotiable in some cities (Hangzhou especially), so override if you have a specific figure.
serviceYearsNoYears of service
serviceMonthsNo…plus months The trailing part-year: ≥6 months counts as a full year, under 6 months as half a month’s pay.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, which the description aligns with by stating 'computes statutory economic compensation.' Beyond annotations, the description details rounding rules, pay-in-lieu of notice, and unlawful termination doubling, adding valuable 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.

Conciseness4/5

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

The description is a single, well-structured paragraph that front-loads the core purpose and key formulas. It is concise while covering essential details, though it could benefit from bullet points for clarity.

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 thoroughly covers the calculation scope and key inputs but omits details about the output format (e.g., numeric break down of N, N+1, 2N). Given no output schema, this leaves some ambiguity for the 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% with detailed parameter descriptions. The description reinforces semantics by explaining the decisive role of the 'reason' parameter and the cap logic, adding 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 statutory severance under China's Labour Contract Law, specifying the N/N+1/2N scenarios and the high-earner cap. It distinguishes itself from siblings like china_income_tax_salary and china_retirement_pension by focusing on severance.

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 that the termination reason is the key input and informs about the scenarios (N, N+1, unlawful). It implies usage when leaving a job in China but does not explicitly state when not to use or provide alternatives to siblings.

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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