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by nbiish

laplace_transform

Calculate the Laplace transform of a function to convert time-domain expressions into s-domain representations for solving differential equations and analyzing linear systems.

Instructions

Calculate the Laplace transform of a function

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
expressionYesFunction of time
timeVarYesTime variable
laplaceVarYesLaplace variable

Implementation Reference

  • Core handler function implementing the Laplace transform calculation using numerical integration approximating the integral from 0 to infinity.
    const laplaceTransform = (expr, t, s) => {
      try {
        const node = math.parse(expr);
        // Using numerical integration for a basic approximation
        const upperLimit = 100; // Approximation of infinity
        const steps = 1000;
        const dt = upperLimit / steps;
        let result = math.complex(0, 0);
        
        for (let i = 0; i < steps; i++) {
          const time = i * dt;
          const scope = { [t]: time };
          const ft = math.evaluate(node, scope);
          const expTerm = math.exp(math.multiply(math.complex(-s, 0), time));
          result = math.add(result, 
            math.multiply(ft, expTerm, dt));
        }
        
        return result.toString();
      } catch (e) {
        return `Error: ${e.message}`;
      }
    };
  • Tool schema definition including name, description, input parameters (expression, timeVar, laplaceVar), and string output.
    name: 'laplace_transform',
    description: 'Calculate the Laplace transform of a function',
    inputSchema: z.object({
      expression: z.string().describe('Function of time'),
      timeVar: z.string().describe('Time variable'),
      laplaceVar: z.string().describe('Laplace variable')
    }),
    outputSchema: z.string(),
  • index.js:441-455 (registration)
    Registration of the laplace_transform tool with Genkit's ai.defineTool, linking schema to the handler wrapper that calls the core laplaceTransform function.
    ai.defineTool(
      {
        name: 'laplace_transform',
        description: 'Calculate the Laplace transform of a function',
        inputSchema: z.object({
          expression: z.string().describe('Function of time'),
          timeVar: z.string().describe('Time variable'),
          laplaceVar: z.string().describe('Laplace variable')
        }),
        outputSchema: z.string(),
      },
      async ({ expression, timeVar, laplaceVar }) => {
        return laplaceTransform(expression, timeVar, laplaceVar);
      }
    );
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the calculation purpose without mentioning error handling, computational limits, output format, or whether it's a pure function. This leaves significant gaps for a mathematical tool with potential complexity.

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?

The description is a single, direct sentence with zero wasted words. It efficiently communicates the core purpose without unnecessary elaboration, making it perfectly front-loaded and appropriately sized for this mathematical tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of Laplace transforms and the lack of both annotations and an output schema, the description is insufficient. It doesn't explain what the output looks like, potential limitations, or error conditions, leaving the agent with incomplete information for proper tool invocation.

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?

The schema description coverage is 100%, with all three parameters clearly documented in the schema itself. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline for high schema coverage without compensating with extra insights.

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?

The description clearly states the action ('Calculate') and the specific mathematical operation ('Laplace transform of a function'), which is a well-defined concept. It distinguishes itself from siblings like 'fourier_transform' and 'z_transform' by specifying the transform type, though it doesn't explicitly contrast with them in the text.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'fourier_transform' or 'z_transform', nor does it mention prerequisites or typical use cases. It simply states what the tool does without contextual usage information.

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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