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Numora Calculus Gradient Endpoint
x402
Numora Calculus Gradient Endpoint is a paid API for AI agents from numormor.netlify.app, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-14).
Computes the numerical gradient (partial derivatives) of a multivariable function at a specified point
Price / call$0.1
Success—
StatusUnknown
How this service works
100 pure math computation endpoints for AI agents. Statistics, financial math, linear algebra, equation solving, calculus, number theory, sequence generation, and unit conversions. Zero external dependencies. x402 micropayments on Base.
Output
Returns a JSON object with success=true, a result object containing the gradient vector (array of partial derivatives, one per dimension), and a human-readable computation string describing what was computed.
Example use cases
Optimize pricing model sensitivity analysis
“I need to understand how sensitive our profit function is to changes in unit cost and selling price. Can you compute the gradient of our profit equation at our current pricing point so we know which variable impacts revenue most?”
Machine learning loss function step guidance
“We're implementing a basic gradient descent optimizer and need to find which direction to adjust our model parameters next. Calculate the gradient of our loss function at the current parameter values [2.5, 1.8, 0.9] so we know which way to move.”
Fluid dynamics flow rate analysis
“For our simulation, I need to find the direction of steepest change in the velocity field at a specific location. Can you get me the gradient vector of our flow function at point (3, 4, 2) so we can model particle movement?”
Technical details
Endpoint
POSThttps://numormor.netlify.app/api/calculus/gradientWhen to prefer
Choose this endpoint when you need fast, serverless numerical gradient computation for multivariable functions without setting up a local math library or scientific computing environment. Ideal for AI agents performing optimization, sensitivity analysis, or calculus tasks where no external data dependencies are needed. Prefer over symbolic math engines when numerical approximation is sufficient and speed matters.
Side effects
- Invalid or unparseable function expression string returns error
- Point array dimension mismatch or empty array returns error
- Function with singularity or undefined value at the given point may return NaN or error
- Malformed request body missing required fields returns 400-level error
- Payment failure via x402 micropayment returns 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| fnrequired | string | |
| pointrequired | array |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| resultrequired | object | Computation result varies by endpoint |
| successrequired | boolean | Always true on success |
| computationrequired | string | Human-readable description of what was computed |
Tags
x402
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