Computes e^x with high precision for mathematical calculations in AI workflows. Access via MCP in Cursor or Windsurf, or REST API at /v1/math/exp. Pass any real number — exp(2) returns 7.38905609893065, exp(-1) gives 0.36787944117144233. Uses native math libraries for reliable exponential computations without floating-point errors common in LLM responses.
curl "https://tinyfn.io/v1/math/exp" \
-H "X-API-Key: YOUR_API_KEY"const response = await fetch('https://tinyfn.io/v1/math/exp', {
headers: { 'X-API-Key': 'YOUR_API_KEY' }
});
const data = await response.json();
console.log(data);import requests
response = requests.get('https://tinyfn.io/v1/math/exp',
headers={'X-API-Key': 'YOUR_API_KEY'})
data = response.json()
print(data)Connect your AI agent (Claude, Cursor, Windsurf, etc.) to TinyFn's math tools:
{
"mcpServers": {
"tinyfn-math": {
"url": "https://tinyfn.io/mcp/math",
"headers": {
"X-API-Key": "YOUR_API_KEY"
}
}
}
}Uses the same underlying math libraries as standard implementations, providing full double-precision accuracy. More reliable than AI-generated approximations for scientific computing.
Yes, handles negative inputs (exp(-5) = 0.006737947) and large positives, though very large values may return Infinity due to floating-point limits.
Call the exp tool directly with your number. The AI agent gets the precise result instead of approximating e^x, crucial for compound interest, decay models, or statistical calculations.
AI models often approximate or miscalculate exponentials. This tool provides deterministic, mathematically correct results using proven algorithms.
Yes, maintains precision for small inputs. exp(0.001) correctly returns 1.0010005001667084, useful for Taylor series approximations and rate calculations.