AVnester — Indian Real Estate Intelligence

Simulate home-loan prepayment (calculator)

simulate_loan_prepayment

Read-onlyIdempotent

Simulate prepaying / repaying an Indian home loan early. Pure deterministic math — no advice, no loan-approval claim. Use for "should I prepay", "early/part/lump-sum repayment", "pay extra each month", "reduce EMI or tenure", "interest saved", or foreclosure. Modes: one-time, recurring yearly, or a fixed extra every month (recurring_monthly). Compares reduce_tenure (keep EMI, finish early) vs reduce_emi (keep tenure, lower EMI). Always surface the disclaimer field.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`strategy`	No	reduce_tenure keeps the EMI and finishes early (bigger saving); reduce_emi keeps the tenure and lowers the EMI; compare_both returns both.	compare_both
`prepaymentMode`	No	How the prepayment is made (annual anniversary vs every month).	one_time
`prepaymentAmount`	No	One-time lump-sum prepayment in INR (one_time / one_time_plus_recurring* modes).
`annualRatePercent`	No	Current annual interest rate (percent). Defaults to the indicative market rate when omitted.
`outstandingPrincipal`	Yes	Current outstanding home-loan principal in INR.
`remainingTenureYears`	Yes	Years left on the loan at the current EMI.
`recurringAnnualAmount`	No	Recurring yearly prepayment in INR (recurring_annual / one_time_plus_recurring modes).
`recurringMonthlyAmount`	No	Fixed extra paid toward principal EVERY month on top of the EMI (recurring_monthly / one_time_plus_recurring_monthly modes).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`cards`	Yes
`applied`	Yes
`baseline`	Yes
`strategy`	Yes
`reduceEmi`	No
`disclaimer`	Yes
`assumptions`	Yes
`attribution`	Yes
`reduceTenure`	No
`prepaymentMode`	Yes
`annualRatePercent`	Yes
`outstandingPrincipal`	Yes
`remainingTenureYears`	Yes

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint, idempotentHint, destructiveHint. The description adds context: 'Pure deterministic math — no advice, no loan-approval claim,' which clarifies the tool's non-advisory nature. No contradictions.

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?

The description is concise with no redundant sentences. Each part serves a purpose: stating the function, clarifying it's a calculator, listing use cases, and summarizing modes and strategies.

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 the tool's complexity (8 parameters, multiple modes), the description covers all key aspects: purpose, use cases, modes, strategies, and a usage instruction (disclaimer). An output schema exists, so return values need not be detailed.

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 described). The description adds value by summarizing modes and strategies (e.g., 'Modes: one-time, recurring yearly...') and the comparison of reduce_tenure vs reduce_emi, which goes 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 simulates prepaying an Indian home loan early, lists specific use cases (e.g., 'should I prepay'), and describes modes and strategies. It is distinct from sibling calculators like 'calculate_home_affordability'.

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 explicitly lists when to use the tool (e.g., 'use for early/part/lump-sum repayment') and instructs to 'always surface the disclaimer field.' However, it does not explicitly state when not to use or compare to alternative tools.

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

A4.6/5.0

Disambiguation5/5

Each tool targets a distinct resource and action: search, details, comparisons (localities vs. listings), and specific financial calculations (EMI, stamp duty, tax, prepayment, balance transfer, decision intelligence). Descriptions clearly differentiate between similar-sounding tools like calculate_home_affordability and get_property_decision_intelligence.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., compare_properties, get_locality_insights, simulate_loan_prepayment). The verbs are descriptive and uniform in style, with no mixed conventions or vague names.

Tool Count5/5

11 tools is well-scoped for a real estate intelligence server, covering property search, locality analytics, and financial calculators. Each tool has a clear purpose and the set feels neither sparse nor bloated.

Completeness5/5

The tool surface covers the full home-buying journey in India: searching properties, getting details, comparing localities/listings, estimating stamp duty and loan payments, optimizing tax regime, simulating prepayment, and assessing overall affordability with decision intelligence. No obvious gaps or dead ends.

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