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Declan142

calcnook

calculate_income_tax

Calculate income tax for US, UK, Canada, Australia, or India based on income, filing status, and other parameters. Handles brackets, credits, and deductions for each country.

Instructions

Calculate income tax for US, UK, Canada, Australia, or India (2026 / FY 2025-26). country='us': federal tax with brackets by filing_status. country='uk': income tax + National Insurance (2025/26). country='ca': federal tax with Basic Personal Amount credit. country='au': income tax + Medicare Levy + optional HECS-HELP. country='in': India new/old regime with 87A rebate + cess. Example queries: 'US tax on $85000 income married filing jointly', 'UK income tax on £50000 salary', 'India income tax ₹12L new regime'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countryYesCountry code: us, uk, ca, au, in.
incomeYesGross annual income in local currency.
filing_statusNo[us] Filing status. Default: single.
has_hecs_debtNo[au] Whether HECS-HELP compulsory repayment applies.
regimeNo[in] Tax regime: 'new' (default, lower rates) or 'old' (with deductions).new
provinceNo[ca] Province/territory code (e.g. 'ON'). Accepted but provincial tax not yet computed.
yearNoTax year. Only 2026 is supported across all countries.
Behavior4/5

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

Without annotations, the description carries full burden. It discloses country-specific tax components (e.g., 'federal tax with brackets', 'income tax + National Insurance') and acknowledges accepted-but-uncomputed province parameter. Missing details on errors or limits.

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 slightly verbose but each sentence adds unique context. Front-loaded with purpose. Could be more concise by grouping country details.

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

Completeness3/5

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

No output schema, so description should hint at return values (e.g., breakdown). It omits what the tool returns (total tax? breakdown?). For 7 parameters, it covers input well but misses output expectations.

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?

Schema coverage is 100% (all parameters documented). Description adds value by explaining country-specific behavior (e.g., 'required if country=us') and providing defaults, going beyond basic schema 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 specifies the tool calculates income tax for five countries, with distinct per-country details (e.g., brackets, NI, Medicare Levy). It clearly distinguishes from sibling tools like calculate_vat or calculate_loan_payment by focusing on income tax.

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?

Example queries show usage patterns for different countries. However, no explicit guidance on when not to use this tool versus alternatives; context is inferred from sibling names.

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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