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中国社保与薪税计算器 (China Shebao & Payroll Tax MCP)

by io.github.ddter
OverviewDiscussionsAdmin

灵活就业人员社保缴费与退休养老金测算

calculate_flexible_employment_social
Read-onlyIdempotent

测算灵活就业/自由职业人员每月自缴社保费用（养老20%、医保）与未来退休待遇：支持60%~300%档次对比、测算每月自缴金额（个账8% vs 统筹12%）、预计退休月领养老金（基础养老金+个人账户养老金）及回本周期年数。

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "calculate_flexible_employment_social"
  }
}

Input Schema

NameRequiredDescriptionDefault
cityNo参保城市名称或代码（未填默认上海，与网页一致），自动带出该城市社平工资与基数上下限。
tierNo缴费档次百分比（如 60、80、100、150、200、300，默认 60 为最低档）。
yearsNo预计累计缴费年限（年，如 15、20、25 年，默认 15）。
retireAgeNo预计退休年龄（如 60、55、50、65，默认 60）。
customBaseNo自定义缴费基数（元/月，可选；若输入则覆盖档次设定）。
hasMedicalNo是否同时缴纳灵活就业职工医保（默认 true）。
medicalRateNo灵活就业医保缴费比例（如 0.08 或 8，默认 8%）。

Output Schema

NameRequiredDescriptionDefault
canonicalUrlYes
paybackYearsYes
totalMonthlyYes
monthlyPensionYes
pensionMonthlyYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / city / description
      Previous value: -"参保城市名称（如\"北京\"、\"上海\"、\"广州\"、\"成都\"等），自动带出该城市社平工资与基数上下限。"New value: +"参保城市名称或代码（未填默认上海，与网页一致），自动带出该城市社平工资与基数上下限。"
  2. Added

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds useful domain behavior — the 20% pension rate, the 8% personal-account vs 12% pooled split, and that a payback-period figure is returned — but says nothing about where the 社平工资 data comes from, whether rates are city-specific or how current they are, which matters for a calculator whose output drives financial decisions.

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?

It is a single front-loaded sentence that opens with the verb and resource before piling on the output breakdown, and every clause carries substantive content rather than filler. It is dense — one long colon-chained sentence — but not padded.

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?

With an output schema present, the description need not explain return values, and it still sketches what is computed (monthly contribution, projected pension, payback years). Combined with 100% parameter documentation and full annotations, the only real gap is the absence of any data-source or accuracy caveat for the assumptions it uses.

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

Parameters3/5

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

Schema description coverage is 100% and every parameter already carries a default and an example in the schema (city defaults to Shanghai, tier defaults to 60, years 15, retireAge 60). The description's mention of 60%–300% tiers and the 20% pension / medical split restates what the schema documents, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb (测算/calculate) and a precisely scoped resource: contributions and retirement benefits for 灵活就业/自由职业 people, including the 20% pension split, 8%/12% account allocation and payback period. The target-population framing implicitly separates it from the generic sibling calculate_retirement_pension, but it never names an alternative explicitly, so it stops short of a 5.

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?

Usage is implied by the population it serves (freelancers/flexible-employment workers planning contributions), and the 60%–300% tier comparison hints at a comparison use case. There is no explicit when-to-use / when-not-to-use statement and no routing to calculate_retirement_pension or get_city_social_fund_limits, leaving the agent to infer the boundary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All 中国社保与薪税计算器 (China Shebao & Payroll Tax MCP) toolsInspect
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