When it comes to money, close enough isn't good enough. LLMs frequently make errors in financial calculations - wrong decimal places, incorrect compounding, flawed amortization. MCP tools ensure your AI agent handles money math with precision.
Why LLMs Fail at Financial Math
Large Language Models are trained to predict text, not compute numbers. When you ask an LLM to calculate compound interest, it doesn't run the formula - it predicts what the answer should look like based on similar examples in its training data.
Common Financial Errors
- Decimal precision: Rounding errors that compound over time
- Compounding periods: Confusing monthly vs. annual compounding
- Formula mistakes: Using simple interest when compound is needed
- Currency precision: Not handling cents/paise correctly
LLM vs Tool: Compound Interest
Question: "$10,000 at 5% for 10 years, compounded monthly?"
// LLM might respond:
"The final amount would be approximately $16,289"
// Wrong! Off by ~$181
// MCP Tool returns:
{
"principal": 10000,
"final_amount": 16470.09,
"total_interest": 6470.09
}
// Correct to the penny
The Stakes Are High: A 1% error in a mortgage calculation over 30 years could mean thousands of dollars. Financial AI applications must use deterministic tools.
Interest Calculations via MCP
TinyFn provides precise interest calculation tools that your AI agent can call via MCP:
Compound Interest
Calculate future value with compound interest, supporting various compounding frequencies:
MCP Tool Call: Compound Interest
Tool: finance/compound-interest
Input: {
"principal": 10000,
"rate": 0.05,
"time": 10,
"compound_frequency": 12
}
Response: {
"principal": 10000,
"rate": 0.05,
"time": 10,
"compound_frequency": 12,
"final_amount": 16470.09,
"total_interest": 6470.09
}Simple Interest
For loans and investments that don't compound:
MCP Tool Call: Simple Interest
Tool: finance/simple-interest
Input: {
"principal": 5000,
"rate": 0.08,
"time": 3
}
Response: {
"principal": 5000,
"interest": 1200,
"total": 6200
}Loan Amortization Tools
Loan amortization is particularly error-prone for LLMs because it requires iterative calculations across many payment periods. MCP tools handle this perfectly.
Monthly Payment Calculator
MCP Tool Call: Loan Payment
Tool: finance/loan-payment
Input: {
"principal": 250000,
"annual_rate": 0.065,
"term_months": 360
}
Response: {
"monthly_payment": 1580.17,
"total_paid": 568861.20,
"total_interest": 318861.20,
"principal": 250000
}Amortization Schedule
Get a complete payment breakdown showing principal vs. interest for each period:
MCP Tool Call: Amortization Schedule
Tool: finance/amortization-schedule
Input: {
"principal": 20000,
"annual_rate": 0.06,
"term_months": 24
}
Response: {
"monthly_payment": 886.41,
"schedule": [
{ "month": 1, "payment": 886.41, "principal": 786.41, "interest": 100.00, "balance": 19213.59 },
{ "month": 2, "payment": 886.41, "principal": 790.34, "interest": 96.07, "balance": 18423.25 },
// ... remaining months
],
"totals": {
"paid": 21273.84,
"interest": 1273.84
}
}Currency Conversion Accuracy
Currency conversion seems simple, but LLMs often use outdated rates or make decimal errors. For financial applications, you need real exchange rates and proper precision.
MCP Tool Call: Currency Conversion
Tool: finance/currency-convert
Input: {
"amount": 1000,
"from": "USD",
"to": "EUR"
}
Response: {
"amount": 1000,
"from": "USD",
"to": "EUR",
"converted": 921.50,
"rate": 0.9215,
"timestamp": "2025-01-31T10:30:00Z"
}
Note: Currency rates change constantly. MCP tools can provide current rates, while LLMs only know rates from their training data.
TinyFn Finance Endpoints
TinyFn provides a comprehensive set of financial tools via MCP:
| Tool | Description |
|---|---|
finance/compound-interest |
Calculate compound interest with customizable frequency |
finance/simple-interest |
Calculate simple (non-compounding) interest |
finance/loan-payment |
Calculate monthly loan/mortgage payments |
finance/amortization |
Generate full amortization schedule |
finance/margin |
Calculate profit margin from cost and price |
finance/markup |
Calculate markup percentage and selling price |
finance/discount |
Calculate discount amount and final price |
finance/vat |
Calculate VAT/tax inclusive and exclusive amounts |
finance/tip |
Calculate tip amount and split between parties |
finance/percentage |
Calculate percentages, increases, and decreases |
Implementation Examples
Python Agent with Financial Tools
import requests
class FinancialAgent:
def __init__(self, api_key):
self.api_key = api_key
self.base_url = "https://api.tinyfn.io/v1"
def calculate_mortgage(self, principal, rate, years):
"""Calculate mortgage payment using TinyFn MCP tool"""
response = requests.get(
f"{self.base_url}/finance/loan-payment",
params={
"principal": principal,
"annual_rate": rate,
"term_months": years * 12
},
headers={"X-API-Key": self.api_key}
)
return response.json()
def compare_investments(self, principal, rates, years):
"""Compare different interest rates"""
results = []
for rate in rates:
response = requests.get(
f"{self.base_url}/finance/compound-interest",
params={
"principal": principal,
"rate": rate,
"time": years,
"compound_frequency": 12
},
headers={"X-API-Key": self.api_key}
)
results.append(response.json())
return results
# Usage
agent = FinancialAgent("your-api-key")
mortgage = agent.calculate_mortgage(300000, 0.07, 30)
print(f"Monthly payment: ${mortgage['monthly_payment']:.2f}")JavaScript Agent Example
class FinancialAgent {
constructor(apiKey) {
this.apiKey = apiKey;
this.baseUrl = 'https://api.tinyfn.io/v1';
}
async calculateCompoundInterest(principal, rate, years, frequency = 12) {
const params = new URLSearchParams({
principal, rate, time: years, compound_frequency: frequency
});
const response = await fetch(
`${this.baseUrl}/finance/compound-interest?${params}`,
{ headers: { 'X-API-Key': this.apiKey } }
);
return response.json();
}
async calculateLoanPayment(principal, rate, months) {
const params = new URLSearchParams({
principal, annual_rate: rate, term_months: months
});
const response = await fetch(
`${this.baseUrl}/finance/loan-payment?${params}`,
{ headers: { 'X-API-Key': this.apiKey } }
);
return response.json();
}
}
// Usage
const agent = new FinancialAgent('your-api-key');
const result = await agent.calculateCompoundInterest(10000, 0.05, 10);
console.log(`Final amount: $${result.final_amount.toFixed(2)}`);Best Practices
- Always use tools for money: Never rely on LLM inference for financial calculations. Even simple percentages can go wrong.
- Preserve precision: Don't round intermediate results. Let the API handle decimal precision.
- Validate inputs: Ensure rates are in decimal form (0.05 not 5) before calling tools.
- Handle currency carefully: Always specify currency codes and be aware of exchange rate timestamps.
- Show your work: When presenting financial results to users, show the inputs and calculation method for transparency.
- Test edge cases: Zero interest, very long terms, and very small amounts can expose precision issues.
Add Financial Tools to Your AI Agent
Get accurate financial calculations with TinyFn MCP. Free tier includes 100 requests/month.
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