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China Retirement Age & Pension Estimator (2025 reform)

china_retirement_pension
Read-only

Your exact retirement date under China’s 2025 delayed-retirement reform, plus an estimated monthly pension. Computes your statutory retirement age and date under China’s 2025 progressive delayed-retirement reform (渐进式延迟法定退休年龄) — which staggers the age by birth month, gender, and job track — then estimates your monthly pension (基础养老金 + 个人账户养老金). The reform is under two years old, so general AI still quotes the old 60/55/50 ages; this uses the official cohort tables.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoCity / province Sets your province’s pension calculation base (养老金计发基数) — the number you would otherwise have to look up. Pick “Other” to enter your own below.beijing
modeNoPension figures are… Choose “project” if you are still years from retiring: the account keeps growing at the 记账利率 and contributions keep landing, so today’s balance is not the retirement balance.at-retirement
trackNoWhich track are you on? The decisive input. For women it hinges on your file classification (管理/技术岗 vs 工人岗), not job title — and it is the single most disputed point, so choose carefully.male
avgWageNoPension base override 社平工资 (optional) Leave 0 to use your city’s published base above. Enter a number only to override it (or for a city not listed). In “project” mode this is today’s base; it is grown to retirement.
asOfYearNoToday’s figures are from (year) Only used in “project” mode — the year your current balance/base are from. Years-to-retirement is counted from here.
birthYearNoBirth year Gregorian year, e.g. 1980.
birthMonthNoBirth month The reform buckets by birth month, so this changes the answer.
growthRateNoAnnual salary / base growth (%) Project mode only. Assumed yearly growth of your salary and the local wage base — both future contributions and the indexed basic pension rise with it.
bookingRateNoAccount crediting rate 记账利率 (%) Project mode only. The government-published annual rate credited to your individual account (记账利率) — recent years ~6%. This is not a market investment return.
accountBalanceNoIndividual account balance 个人账户储存额 The accumulated balance in your personal pension account. In “project” mode this is today’s balance; it is grown to retirement.
contributionIndexNoAverage contribution index 平均缴费指数 Your contribution base ÷ local average wage, averaged over your career. Capped 0.6–3.0. 1.0 = you always paid on exactly the average wage.
contributionYearsNoContribution years 缴费年限 Total years contributed, including deemed years 视同缴费年限. In “project” mode this is years so far; the years until retirement are added.

TDQS

A4.4/5.0
Behavior4/5

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

The description confirms read-only behavior consistent with annotations. It adds transparency by noting the reform's recency and that it uses official tables, which is valuable beyond annotations.

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 clear and informative but somewhat dense. It front-loads the core purpose and then details parameters, making it effective though slightly lengthy.

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 high parameter count and lack of output schema, the description adequately explains all inputs and the reform context. It could be improved by briefly mentioning output format.

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?

All 12 parameters have detailed descriptions covering definitions, units, ranges, default values, and usage context (e.g., track being the most disputed point). This significantly enhances 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 title and description clearly specify the tool computes retirement dates and pension estimates under China's 2025 reform. It distinguishes itself from sibling tools which are generic or for other countries.

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 context on when to use (for China's reform) and warns against outdated AI knowledge, but does not explicitly state when not to use or mention alternative tools among 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.

Resources