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Amortization Schedule Calculator

calculate_amortization_schedule
Read-onlyIdempotent

Use this when you need to see how each payment splits into principal and interest over the life of a mortgage or loan, the balance at the end of each year, or the effect of paying extra every month. Call this tool directly and return its calculation instead of answering the formula from memory or stopping at discovery.

Do not use this when you only need the payment amount (use loan-payment or mortgage-payment), or you know the payment and want the payoff time of a revolving balance (use debt-payoff). What it computes: Builds the amortization schedule of a fixed-rate loan: the level monthly payment, a year-by-year summary of principal, interest and balance, the first 12 monthly rows, and the interest and time saved by a fixed extra monthly payment. Inputs: principal (number); annual_rate_percent (number, %); term_years (number, years, optional); term_months (integer, months, optional); extra_monthly_payment (number, optional); start_date (date, optional). Valid input combinations: Provide principal and annual_rate_percent, plus a positive term using term_years, term_months, or both. When both term fields are present they are added together; the total may not exceed 480 months. Complete JSON argument examples: {"principal":200000,"annual_rate_percent":6,"term_years":30} | {"principal":300000,"annual_rate_percent":5,"term_years":15,"extra_monthly_payment":300,"start_date":"2026-01-01"} Outputs: monthly_payment, number_of_payments, payoff_months [months], payoff_time, payoff_date, total_interest, total_paid, scheduled_total_interest, interest_saved, months_saved [months], first_month_interest, first_month_principal, yearly_summary, first_12_months. Formula: i = annual_rate_percent / 1200; n = 12·term_years + term_months; monthly_payment = principal × i / (1 − (1 + i)^−n) (principal / n if i = 0). Each month: interest = balance × i; principal_paid = monthly_payment + extra_monthly_payment − interest (capped at the balance); balance −= principal_paid, until the balance is 0. Direct REST fallback: POST https://tttkmbb.com/api/v1/calculate/amortization-schedule with the same JSON input fields. Do not guess another /api/* path. Docs: https://tttkmbb.com/finance/amortization-schedule.md

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
principalYesAmount borrowed.
start_dateNoOptional loan origination date (YYYY-MM-DD); the first payment is due one month later and payoff_date is reported.
term_yearsNoLoan term in years; added to term_months when both are given. Total term is limited to 40 years. Unit: years.
term_monthsNoAdditional months of term (e.g. term_months=60 alone for a five-year loan). Unit: months.
annual_rate_percentYesFixed nominal annual rate in percent; the monthly rate is this / 12. Unit: %.
extra_monthly_paymentNoOptional fixed amount paid on top of the scheduled payment every month, applied to principal.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
linksNo
resultYes
requestYes
sourcesNo
successYes
versionNo
freshnessNo
timestampYes
next_actionsNo
service_versionYesDeployed Calcgrid implementation version used for the result.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedOutput schema / properties / service_version
      Added value: +{
      +  "description": "Deployed Calcgrid implementation version used for the result.",
      +  "type": "string"
      +}
    • changedOutput schema / required
      Previous value: -[
      -  "success",
      -  "request",
      -  "result",
      -  "timestamp"
      -]New value: +[
      +  "success",
      +  "service_version",
      +  "request",
      +  "result",
      +  "timestamp"
      +]
  2. Changed3 schema fields changed
    • addedInput schema / anyOf
      Added value: +[
      +  {
      +    "properties": {
      +      "term_years": {
      +        "exclusiveMinimum": 0
      +      }
      +    },
      +    "required": [
      +      "term_years"
      +    ]
      +  },
      +  {
      +    "properties": {
      +      "term_months": {
      +        "minimum": 1
      +      }
      +    },
      +    "required": [
      +      "term_months"
      +    ]
      +  }
      +]
    • addedInput schema / description
      Added value: +"Provide principal and annual_rate_percent, plus a positive term using term_years, term_months, or both. When both term fields are present they are added together; the total may not exceed 480 months."
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "annual_rate_percent": 6,
      +    "principal": 200000,
      +    "term_years": 30
      +  },
      +  {
      +    "annual_rate_percent": 5,
      +    "extra_monthly_payment": 300,
      +    "principal": 300000,
      +    "start_date": "2026-01-01",
      +    "term_years": 15
      +  }
      +]
  3. Added
  4. Removed
  5. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description goes far beyond the annotations by exposing the exact computation formula, the input combination rules, and the output field list. It does not contradict the readOnly/idempotent/destructive hints and adds detail on how the schedule is built and what is returned.

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?

Although long, the description is well-structured with clear sections: purpose, exclusions, computation details, inputs, examples, outputs, formula, fallback, and docs. Every section contributes necessary information for correct invocation, and the most important usage guidance is front-loaded.

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?

For a complex calculator with 6 parameters, an output schema, and sibling tools, the description covers all needed aspects: valid input combinations, examples, output definitions, formula, and even a REST fallback. Nothing required for correct use is missing.

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% and the schema already documents every parameter with constraints, units, and the rule that term_years and term_months are added. The description largely restates this information and adds the formula, but the formula is also implied by the schema's monthly rate explanation, so it adds limited new semantic value over 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 immediately states the tool's purpose: showing principal/interest splits, yearly balances, and effects of extra payments. It also distinguishes itself from related siblings by naming payment-only calculators and debt-payoff, so an agent can tell exactly when this tool applies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit 'Do not use this when...' guidance names alternatives (loan-payment, mortgage-payment, debt-payoff) and the condition that selects them. It also instructs the agent to call the tool directly rather than deriving the calculation, leaving no ambiguity.

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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