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Glama

Xearno Tools

Car Loan Calculator

car_loan
Read-only

What a car actually costs you a month — priced for your market, where tax may or may not already be in the sticker. A car payment depends on something most calculators quietly assume: whether tax is added at purchase or already sitting in the advertised price. In the US, Canada and Japan it is added on top — and in most US states a trade-in is credited before tax is worked out. In the UK, the EU, Australia, Singapore and India, VAT or GST is already inside the sticker, so adding it again overstates the loan by the full tax rate. Pick the market and the rest follows, together with the negative-equity warning the dealership finance office will not give you.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
downNoDown payment
rateNoInterest rate (APR) (%)
priceNoVehicle price
marketNoWhere are you buying? This is the input that decides whether the answer is right. Where tax is added at purchase (US, Canada, Japan) it is charged on top of the advertised price. Almost everywhere else — the UK, the EU, Australia, Singapore, India — VAT/GST is already inside the price, so adding it again overstates the loan.US
monthsNoTerm (mo)
tradeInNoTrade-in / part-exchange value In most US states a trade-in reduces the amount you are taxed on. Where tax is already in the price it simply lowers what you borrow.
salesTaxNoSales tax / GST added at purchase (%) Only applies in tax-added markets. Ignored where the advertised price already includes VAT/GST.
vehicleTypeNoPetrol or new-energy vehicle? Only changes the answer in China, where new-energy vehicles pay half the purchase tax for 2026–2027, capped at a ¥15,000 reduction. The full exemption ended on 31 December 2025.ice

TDQS

A4.2/5.0
Behavior5/5

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

The description discloses significant behavioral details beyond the read-only annotation: tax is added on top in US/Canada/Japan, already included in UK/EU/AU/SG/IN, trade-in credits before tax in most US states, salesTax ignored in inclusive markets, and vehicleType only matters in China. This gives a clear picture of how the calculator handles inputs and market variations.

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 considerably longer than typical, but each sentence contributes to explaining the critical tax logic and market differences. It is front-loaded with the purpose and uses a logical structure, though some details could be tightened without losing meaning.

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?

With no output schema, the description partially covers outputs by mentioning monthly cost and negative-equity warning, but it does not fully specify what the tool returns (e.g., total interest, total cost). The complex market logic and parameter interactions are well covered, but the output side is underexplained, leaving a gap for a calculator tool.

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 coverage is 100% with detailed parameter descriptions, so the baseline is 3. The description adds a bit of context about market-driven behavior and negative-equity warning, but it largely restates what the schema already explains (e.g., trade-in tax treatment). It does not materially enhance parameter understanding beyond 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 description clearly states the tool computes monthly car loan cost ('What a car actually costs you a month') and emphasizes market-specific tax treatment, distinguishing it from generic loan or mortgage calculators. It also hints at a unique negative-equity warning, further separating it from siblings.

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 strong contextual guidance on when to use the tool—specifically for car loans in markets with tax-inclusive vs tax-exclusive pricing—and explains how market selection drives the calculation. It does not explicitly name alternative tools, but the focus on car-loan-specific tax logic implies when this is the right choice over generic tools like loan_payment or mortgage.

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