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EMI calculator

emi_calculate
Read-onlyIdempotent

Compute the equated monthly installment (EMI) for a loan.

Returns: JSON { emi, totalInterest, totalPayment } for the loan. Example input: {"principal":500000,"annualRatePercent":8.5,"months":240}

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "principal": "<principal>",
      "annualRatePercent": "<annualRatePercent>",
      "months": "<months>"
    },
    "name": "emi_calculate"
  }
}

Input Schema

NameRequiredDescriptionDefault
monthsYesLoan term in months.
principalYesLoan principal.
annualRatePercentYesAnnual interest rate as a percent, e.g. 7.5.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already establish readOnly, idempotent, closed-world behavior, so the safety profile needs no elaboration. The description adds genuinely new information beyond structured data by disclosing the return payload shape ({ emi, totalInterest, totalPayment }), which matters because there is no output schema. It stops short of stating rounding/precision or compounding conventions.

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?

Three short lines: purpose front-loaded, then return shape, then a concrete example. Every line carries distinct information and nothing is repeated from the title or schema verbatim.

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?

For a three-parameter pure calculator with full schema coverage, annotations, and an explicit return-field list, the description is nearly self-sufficient. The only material gap is that it does not state the amortization assumption or precision of the returned numbers, which could matter for downstream presentation.

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 description coverage is 100%, so all three parameters are already documented with units and bounds (annualRatePercent explicitly as a percent, e.g. 7.5; months exclusiveMinimum 0). The example input reinforces scale and ordering but adds little beyond what the schema already establishes, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb (compute) and resource (equated monthly installment for a loan), and spells out the EMI acronym so an agent need not infer it. It does not, however, distinguish itself from near-neighbours such as simple_interest, compound_interest, or run_calculator, so the agent must infer the boundary.

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

Usage Guidelines3/5

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

The phrase "for a loan" plus the worked example imply the intended scenario (fixed-rate amortizing loan), but there is no explicit statement of when to prefer this over simple_interest, compound_interest, or run_calculator, and no exclusions or prerequisites. Usage is inferable rather than stated.

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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