US Mortgage Calculator

Compare renting with buying

calculate_rent_vs_buy

Read-onlyIdempotent

Compares renting and buying on net wealth rather than on monthly payment, accounting for equity, appreciation, maintenance, selling costs, and the return a renter earns on money not spent on a down payment. Returns the year buying overtakes renting. The result is highly sensitive to the appreciation and investment-return assumptions, which are returned alongside it.

Input Schema

TableJSON Schema

Name	Required	Description
`state`	No	Full US state name, for example "Texas". Used to look up the average property tax rate. Either this or propertyTaxRatePercent is required.
`homePrice`	Yes	Purchase price of the home in US dollars.
`monthlyHoa`	No	Monthly HOA dues.
`monthlyRent`	Yes	Current monthly rent for a comparable home.
`loanTermYears`	No	Loan term in years. Most US mortgages are 15 or 30.
`yearsToCompare`	No	Horizon of the comparison.
`annualInsurance`	No	Annual homeowners insurance premium.
`downPaymentPercent`	No	Down payment as a percentage of the purchase price, 0 to 100. Not a dollar amount.
`sellingCostPercent`	No	Cost of selling, including agent commission.
`propertyTaxRatePercent`	No	Annual property tax as a percentage of home value. Omit and pass `state` instead to use that state's average.
`investmentReturnPercent`	No	Return a renter earns on cash not tied up in a house.
`annualMaintenancePercent`	No	Annual upkeep as a percentage of home value. Typically 1 to 2.
`annualInterestRatePercent`	No	Annual nominal interest rate as a percentage, for example 6.5 for 6.5%.
`annualRentIncreasePercent`	No	Expected annual rent growth.
`annualHomeAppreciationPercent`	No	Expected annual home price growth. The answer is highly sensitive to this.

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds valuable behavioral context: it returns the crossover year and explicitly highlights that the result is highly sensitive to appreciation and investment-return assumptions, which are returned alongside the result. This goes beyond what annotations provide.

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

Conciseness5/5

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

Two sentences, front-loaded with the core concept, and every clause contributes meaning. No redundancy or filler.

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?

Given 15 parameters and no output schema, the description clearly explains the comparison basis, the key output (crossover year), and the sensitivity assumptions returned. This is sufficient for an agent to select and 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?

Schema covers 100% of parameters, so the baseline is 3. The description adds extra semantic value by identifying which parameters drive the net-wealth model (equity, appreciation, maintenance, selling costs, renter's return) and flagging high-sensitivity assumptions, helping the agent understand which inputs matter most.

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 compares renting versus buying based on net wealth rather than monthly payment, and specifically names the factors considered (equity, appreciation, maintenance, selling costs, renter's return) and the return value (year buying overtakes renting). This clearly distinguishes it from sibling tools like calculate_mortgage or compare_loans.

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?

It makes clear the comparison is on net wealth rather than monthly payment, which implies a decision context, but it does not explicitly state when to use this tool versus alternatives or list exclusions. The context is clear enough for an agent to select it for rent-vs-buy analyses, but explicit 'use this when...' guidance is missing.

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

A4.5/5.0

Disambiguation5/5

Each tool targets a distinct calculation or information need: affordability, amortization, full mortgage payment, PMI, property tax, refinancing, rent vs buy, loan comparison, closing costs, state rules, and state list. Even overlapping tools like calculate_mortgage and calculate_pmi are clearly separated by depth and purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern, using verbs like calculate, compare, estimate, get, and list. The pattern is uniform and predictable across the entire set.

Tool Count5/5

With 11 tools, the server is well-scoped for a US mortgage calculator domain. Each tool covers a meaningful aspect of mortgage analysis without redundancy or bloat.

Completeness5/5

The tool surface covers the full lifecycle of mortgage decision-making: affordability, payment estimation, amortization, PMI, property tax, refinancing, rent vs buy, loan comparison, closing costs, and state-specific rules. No significant gaps are apparent for the stated purpose.

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