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

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

Annotations already declare this a read-only, idempotent, closed-world operation, so the safety profile is covered. The description adds genuinely useful behavioral context beyond that: it pins the calculation to the 2026 tax code and a specific 7-level progressive rate table, telling the agent the result is a deterministic statutory computation rather than an estimate. It does not discuss rounding/precision or what the output contains, but the output schema covers returns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

A single front-loaded sentence stating the domain, the legal basis, and the supported modes, with no filler or redundant restatement of the tool name. Nothing can be removed without losing scope information.

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

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

For a 13-parameter tool, the description correctly frames the domain and the three modes while leaving parameter-level detail to a fully covered schema and return values to the output schema. The only gaps are cross-tool routing and any caveat about which mode-specific parameters are ignored, both minor given the surrounding structure.

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

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

Schema coverage is 100% and the schema descriptions already explain each parameter's role per mode, including the 80%/56% inclusion rules and the annual-vs-monthly override precedence. The description only names the three modes, which the mode enum already documents. With the schema doing all the heavy lifting, baseline 3 applies.

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

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

The description gives a specific verb+resource ('精准计算中国个人所得税') and names the exact tax regime (2026 现行税法, 7 级超额累进税率表) plus the three computation modes it supports. An agent can tell this is a tax-calculation tool, not a lookup or policy-query tool. What it lacks is an explicit boundary against close siblings such as calculate_salary_take_home or compare_annual_bonus_tax.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The enumeration of the three modes (月度累计预扣 / 劳务报酬预扣 / 年度汇算清缴) implies which scenarios the tool covers, which is real usage guidance. However, it never states when to prefer this over calculate_salary_take_home or compare_annual_bonus_tax, nor any prerequisites or exclusions. Usage is therefore inferable but not directed.

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

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

Annotations already cover read-only, idempotent, non-destructive, and closed-world behavior, so the description need not repeat those properties. The description adds useful behavioral context: the calculation follows the national employee pension formula and can automatically retrieve a recorded 计发基数 from the local dataset when province/city is supplied, otherwise requiring explicit input.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single dense sentence that is front-loaded with the purpose and then explains the calculation structure and input options. It is efficient, but it repeats several input names already documented in the schema, making it slightly longer than strictly necessary.

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

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

An output schema exists, so the description does not need to explain return values. Given eight parameters and a complex pension formula, the description provides adequate context about methodology and input modes; the remaining details, such as required fields and defaults, are covered by the schema.

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

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

Schema description coverage is 100%, so the schema already documents all eight parameters in detail. The description adds conceptual meaning by framing the inputs around the two pension components and by clarifying the automatic lookup versus direct-entry pathways, though it does not add much parameter-level detail beyond what the schema provides.

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

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

The description uses a specific verb (测算) and resource (企业职工基本养老金月领金额), and explicitly identifies the two calculation components: 基础养老金 and 个人账户养老金. This clearly distinguishes it from the sibling tax, salary, and social-security-limit tools without needing to name them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies that the tool is for estimating monthly pension benefits and explains two input modes: automatic lookup via province/city, or direct entry of pensionBase and related factors. However, it does not explicitly say when to use this tool instead of the sibling calculations, nor does it state exclusions or prerequisites for either input mode.

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

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

Annotations already declare readOnly=true, idempotent=true, destructive=false, so there is no safety burden here. The description still adds real behavioral value by disclosing that bases are automatically matched to the city's 2026 social insurance/housing fund floor and ceiling and truncated, and that tax uses cumulative withholding, which explains why the month parameter matters. It does not cover rate-limit, data freshness for policy years, or error behavior for unsupported cities.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

Two sentences, front-loaded with the core outcome and followed by the computation flow; every clause maps to an actual output component or behavior. It is dense but not padded, though the long second sentence could be broken up for readability.

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

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

For a 7-parameter tool with nested special deductions, full schema coverage, an output schema, and non-destructive annotations, the description supplies the missing conceptual layer: what is computed end-to-end and how city limits are applied. An agent has everything needed to call it correctly without further explanation.

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

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

Schema description coverage is 100%, including all nested specialDeductions fields, so the schema already carries parameter meaning and the baseline is 3. The description's note about auto-matching and truncating bases to city limits largely repeats what socialBase/fundBase descriptions already say, adding only the 2026 policy-year context.

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

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

The description states a specific verb+resource pair: computing after-tax salary and employer labor cost for a given city, and it enumerates the components covered (social insurance and housing fund deductions for individual and employer, special additional deductions, cumulative withholding tax, net pay, monthly employer cost). This is far more than restating the title, and an agent can tell this is the combined take-home + employer-cost calculator rather than the narrower income-tax or limits-lookup siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: the description makes clear this is a full take-home/cost simulation for a given pre-tax monthly salary and city, which implicitly suggests using the city-limits or policy-query siblings first if raw data is needed. There is no explicit when-to-use vs when-not, no mention of when to prefer calculate_income_tax alone, and no prerequisites stated.

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

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

The annotations already state readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. Beyond that, the description discloses what the response will report: the direction of the recommendation, the savings amount, and the cliff-point warning. It still does not state assumptions such as how default deductions of 0 affect the result, but it adds real behavioral value on top of the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is one front-loaded sentence that states the comparison first, then the outputs, then the cliff-point warning, with no filler. It is dense but readable; splitting it would slightly improve scannability, but nothing needs to be cut.

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

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

For a deterministic four-parameter calculator that has an output schema, the description covers the purpose, the comparison basis, and the key return highlights, so an agent can call it confidently. The only gap is that it does not state the default-deduction assumptions or limitations such as jurisdiction or tax year, but the schema and annotations cover most of that.

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

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

Schema coverage is 100%, and every parameter already has a Chinese description covering units and defaults, so the schema handles this dimension well. The tool description adds no parameter-level meaning beyond that; it only restates the two taxation methods at a conceptual level. A baseline 3 is appropriate.

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

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

The description names a specific verb and resource (compare the tax totals of the two statutory bonus taxation methods) and states exactly what the result contains: which method is cheaper, the yuan amount saved, and whether the bonus falls into a rate-cliff 'blind zone'. This makes it clearly distinguishable from siblings such as calculate_income_tax or calculate_salary_take_home.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It makes the usage context clear: the comparison is between the '全年一次性奖金单独计税' and the '并入综合所得计税' methods, which tells an agent the scenario this tool covers. However, it never names an alternative or an exclusion; for example, it does not say to use calculate_income_tax for non-bonus income, nor that it only handles annual bonuses.

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

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

The annotations already declare readOnly, idempotent, closed-world, and non-destructive behavior, so the safety profile needs no extra words. The description adds genuinely useful context beyond them: the data vintage (2026) and the coverage scope (392 cities nationwide), which tells an agent how fresh and how broad the dataset is. It does not address what happens for an unsupported or ambiguous city, which keeps it from a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

Two compact sentences with no filler. The resource and the returned fields are front-loaded, and the coverage claim is a short, useful trailing clause.

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

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

An output schema exists, so return values need no explanation, and the sole parameter is fully documented in the schema. What is missing is edge-case behavior, such as unknown city handling or whether 2026 figures are provisional, which matters for a data-lookup tool that claims exactly 392 cities.

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

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

Schema description coverage is 100%: the single city parameter already documents accepted formats (Chinese name, pinyin slug, or 6-digit administrative code) with examples. The description adds nothing about the parameter beyond what the schema provides, so the baseline 3 applies.

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

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

The description names a specific verb and resource: it retrieves the 2026 base limits, the contribution rates for each insurance type (individual and employer), and the official verification basis for a given city. It is clearly distinguishable from the calculate_* siblings and implicitly from query_social_security_policy. However, it never explicitly names the sibling it differs from, so it falls 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.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: call this tool when you need numeric contribution limits or rates for a city, rather than when you need a tax or pension calculation. There is no explicit when-not guidance, no prerequisites, and no mention of query_social_security_policy as the alternative for policy narrative versus numeric data.

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

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

The annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful scope context (2026年度, official document numbers, effective dates), but says nothing about behavior when an unsupported province is supplied, data freshness, or format of failure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

One dense sentence, front-loaded with the verb and scope, with no filler. Some enumeration of return fields is arguably redundant against the existing output schema, which keeps it from a 5.

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

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

For a single-parameter read-only lookup with a full output schema and clear annotations, the definition supplies enough scope (31 provinces, 2026, the specific data points) to call it correctly. It could note coverage limits (mainland provinces only, 2026 data only) but is otherwise adequate.

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

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

Schema description coverage is 100% and the single province parameter is already documented with example names, pinyin, and two-digit administrative codes. The description adds no parameter-level meaning beyond the schema, so the baseline of 3 applies.

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

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

The description states a specific verb (查询) and resource (中国31省市2026年度社保缴费基数官方政策), and enumerates the returned fields, so the agent knows exactly what it is asking for. It does not explicitly distinguish itself from siblings, but the province-level policy lookup is clearly separate from the calculate_* and get_city_social_fund_limits tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not-to-use guidance, and no mention of the sibling get_city_social_fund_limits or the calculate_* tools as alternatives. The intended use (fetching provincial base limits or official policy details) is only inferred from the enumerated content.

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