Skip to main content
Glama

Xearno Tools

Home Affordability Calculator

home_affordability
Read-only

How much house you can afford — on the rule your lender actually uses, which differs between the US and the UK. Two countries give entirely different answers on the same income. US lenders underwrite on debt-to-income ratios — 28% of gross income on housing, 36% on all debt — which cap the monthly payment and let the loan fall out of it. UK and Australian lenders cap the loan itself at a multiple of income, around 4.5x, then stress-test the payment at a rate above the one you are quoted. This applies whichever rule is yours, and names the binding limit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
downNoDown payment saved
rateNoMortgage rate (%)
yearsNoTerm (yr)
incomeNoGross annual income
methodNoHow does your lender decide? US lenders underwrite on two debt-to-income ratios. UK and Australian lenders cap the loan at a multiple of income (about 4.5×) and then stress-test the payment. The two produce materially different answers on the same income.US
monthlyDebtsNoExisting monthly debt payments Car loans, student loans, card minimums — not rent or utilities.

TDQS

A4.1/5.0
Behavior4/5

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

The readOnlyHint annotation already declares this a safe read operation, and the description adds behavioral context by disclosing the methodology: it computes the binding constraint (payment-cap for US, loan-multiple for UK/AU) and names which limit is binding. This goes beyond the annotation by explaining how the tool arrives at its answer and what it will report.

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 core question and uses about 100 words to explain the two rules. Each sentence contributes to understanding the tool's logic, but the explanation is somewhat lengthy and could be tightened. It remains appropriately sized for an educational calculator, yet it's not as concise as it could be.

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

Completeness4/5

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

With no output schema, the description does a good job explaining the tool's return: an affordable house amount and which lending rule is binding. Combined with thorough parameter descriptions, this provides strong contextual coverage. It stops short of describing the exact output format (e.g., whether a breakdown is included), so it's not a 5.

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 descriptions cover 100% of parameters, including a detailed explanation of the 'method' enum. The tool description's discussion of the US/UK rules reinforces the method parameter but does not add new parameter-specific details beyond the schema. With high schema coverage, a baseline of 3 is appropriate.

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 opens with 'How much house you can afford' – a specific, actionable outcome – and then clearly differentiates the tool from sibling calculators by explaining the US debt-to-income rule versus the UK/Australian income-multiple rule. This makes it unambiguous that the tool calculates an affordability limit, not monthly payments or loan terms.

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 clear context for when to use the tool by explaining that it applies the lender's actual rule and handles both US and UK/Australian methods. It states 'This applies whichever rule is yours, and names the binding limit,' which implies universal applicability. However, it does not explicitly mention alternative tools or exclusions, so it earns a 4 rather than a 5.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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