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Singapore Platform Worker CPF Calculator (Platform Workers Act)

singapore_platform_worker_cpf
Read-only

Your monthly CPF deduction and operator top-up as a ride-hail or delivery platform worker — by birth cohort, vehicle, and the 2025–2029 rate ramp. Computes platform-worker CPF under Singapore’s Platform Workers Act (in force 1 Jan 2025) — a regime new enough that general AI either doesn’t know it or garbles it. Three inputs users never think to volunteer decide everything: your BIRTH DATE (born on/after 1 Jan 1995 → increased contributions are mandatory; born before → voluntary via an irrevocable opt-in, otherwise MediSave-only), your VEHICLE (the 60/35/20% fixed expense deduction moves the CPF base by 3× for the same gross), and the YEAR (rates ramp every January to full employee parity in 2029). It also gets right what models confidently invert: no monthly ceiling — unlike employees — but a $102,000/year net-earnings cap per platform operator.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ageNoAge Sets the rate band: 35 & below, >35–45, >45–50, >50–55, >55–60, >60–65, >65–70, >70. Each band has its own worker/operator split.
yearNoContribution year Rates ramp every January until full employee parity in 2029. 2027+ figures for ages 55–70 are subject to the senior-worker contribution schedule.2026
optedInNoBorn before 1995 — have you opted in? Only matters if you were born before 1 Jan 1995. The opt-in is irreversible — once made, you are treated exactly like the mandatory cohort (worker share + operator share), forever.no
vehicleNoHow do you work? CPF applies to NET earnings = gross minus a fixed expense deduction (FEDA) set by your mode of work: 60% for cars/vans/lorries, 35% for motorcycles/PABs/PMDs, 20% otherwise. A car driver’s CPF base is only 40% of gross.bicycle
birthYearNoBirth year The hard line: born on or after 1 Jan 1995 → increased CPF contributions are MANDATORY. Born before → voluntary, by an IRREVOCABLE opt-in (otherwise MediSave-only). Two riders doing identical work, born days apart, live under different regimes.
grossMonthlyNoGross platform earnings per month (S$) Fares and fees as paid out by the platform, before the fixed expense deduction. Per operator.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations provide readOnlyHint: true, and the description adds significant behavioral context: regime effective from 1 Jan 2025, no monthly ceiling but a $102k annual cap, irrevocable opt-in for pre-1995 cohort, rate ramp to 2029. No contradiction with 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 front-loaded with the main purpose and then provides detailed context. It is somewhat verbose but every sentence adds value by explaining nuances. The structure is logical: purpose, regime context, input explanations. Could be slightly more concise, but effective.

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?

With 6 parameters and no output schema, the description fully covers the inputs, their implications, and the regime's unique rules. It explains how inputs interact (e.g., birthYear and optedIn, vehicle and FEDA, year and rate ramp). The agent has enough information to 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?

All 6 parameters have schema descriptions (100% coverage). The tool description adds extra meaning beyond schema, e.g., explaining the 'hard line' for birthYear, FEDA percentages for vehicle, and rate ramp for year. This enriches understanding beyond what the schema alone provides.

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 'monthly CPF deduction and operator top-up' for Singapore platform workers under the Platform Workers Act. It specifies the exact use case (ride-hail or delivery platform worker) and distinguishes itself from sibling tools like singapore_property_stamp_duty or other nation-specific calculators.

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 implicitly guides when to use by explaining the three critical inputs (birth date, vehicle, year) and their effects. It does not explicitly list alternatives or when not to use, but the context from sibling-tools and the detailed regime explanation provides sufficient guidance for an AI agent.

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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