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supra126

taiwan-payroll

計算勞保一次請領老年給付

calculate_old_age_single_payment

Compute old-age lump-sum benefits under Taiwan's Labor Insurance using the base formula. Input average insured salary and insurance years before/after age 60. For those with coverage before 2009.

Instructions

依勞保一次請領老年給付法定公式(基數制:前 15 年每年 1 個基數、第 16 年起每年 2 個基數、60 歲前最高 45 個基數、逾 60 歲後年資每年 2 個基數最多計 5 年、合併最高 50 個基數)試算給付金額。適用 98 年前已有保險年資者。結果僅供參考,以勞保局、健保署核發之繳款單為準,不構成法律或會計建議。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNo年度(西元,如 2026)。省略則使用最新可用年度;可先用 list_years 查可用年度。
avgInsuredSalaryYes平均月投保薪資(採退保前 3 年內最高 36 個月平均),新臺幣元。
preSixtyYearsYes60 歲(含)以前之保險年資:年。
preSixtyMonthsNo60 歲(含)以前之保險年資:月(0–11),預設 0。
postSixtyYearsNo逾 60 歲以後之保險年資:年。逾 60 歲後最多計入 5 年,超過部分不計。預設 0。
postSixtyMonthsNo逾 60 歲以後之保險年資:月(0–11),預設 0。
Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: the formula details (base system, limits), the condition for pre-98 years, and the disclaimer that results are for reference only and subject to official confirmation. It also notes that omitting year uses latest available.

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 paragraph that front-loads the main purpose, but it is dense and could be broken into shorter sentences or bullet points for better readability. It is not overly verbose.

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

Completeness5/5

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

Given the tool's complexity (6 parameters, legal formula, applicability conditions), the description covers all essential aspects: formula, parameter usage, applicability, disclaimer. It is complete for an AI agent to understand and invoke the tool correctly.

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 already provides 100% coverage with clear parameter descriptions. The description adds value by explaining the overall formula and the logic behind the parameters (e.g., the 45 vs 50 base limits), which enhances understanding beyond individual parameter 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 calculates the old-age lump sum payment for labor insurance using the legal formula, and distinguishes itself from sibling tools like calculate_old_age_pension by specifying the lump sum nature and applicability to those with insurance years before 98.

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 explicitly mentions applicability for those with insurance years before 98, which helps decide when to use this tool. However, it does not mention when not to use or explicitly contrast with alternative tools.

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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