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nbiish
by nbiish

area

Calculate the area under a curve between two points using numerical integration. Input a mathematical expression and integration bounds to compute the enclosed region.

Instructions

Calculate the area under a curve between two points

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
expressionYes
startYes
endYes
nNoNumber of subintervals (default: 1000)

Implementation Reference

  • Handler function for the 'area' tool. It invokes the riemannSum helper with the trapezoid method to approximate the area under the curve (definite integral).
    async ({ expression, start, end, n = 1000 }) => {
      return riemannSum(expression, 'x', start, end, n, 'trapezoid');
    }
  • Input and output schema for the 'area' tool. Defines parameters: expression (string), start/end (numbers), optional n (subintervals). Outputs a number.
    inputSchema: z.object({
      expression: z.string(),
      start: z.number(),
      end: z.number(),
      n: z.number().optional().describe('Number of subintervals (default: 1000)')
    }),
    outputSchema: z.number(),
  • index.js:131-146 (registration)
    Registration of the 'area' tool via ai.defineTool, including name, description, schema, and inline handler.
    ai.defineTool(
      {
        name: 'area',
        description: 'Calculate the area under a curve between two points',
        inputSchema: z.object({
          expression: z.string(),
          start: z.number(),
          end: z.number(),
          n: z.number().optional().describe('Number of subintervals (default: 1000)')
        }),
        outputSchema: z.number(),
      },
      async ({ expression, start, end, n = 1000 }) => {
        return riemannSum(expression, 'x', start, end, n, 'trapezoid');
      }
    );
  • riemannSum helper function that performs numerical integration using left, right, midpoint, or trapezoid Riemann sums. Used by the 'area' handler with 'trapezoid' method.
    const riemannSum = (expr, variable, a, b, n, method = 'midpoint') => {
      try {
        const deltaX = (b - a) / n;
        let sum = 0;
        const node = math.parse(expr);
        const scope = {};
        
        if (method === 'left' || method === 'right') {
          const offset = method === 'right' ? 1 : 0;
          for (let i = 0; i < n; i++) {
            const x = a + (i + offset) * deltaX;
            scope[variable] = x;
            sum += math.evaluate(node, scope) * deltaX;
          }
        } else if (method === 'midpoint') {
          for (let i = 0; i < n; i++) {
            const x = a + (i + 0.5) * deltaX;
            scope[variable] = x;
            sum += math.evaluate(node, scope) * deltaX;
          }
        } else if (method === 'trapezoid') {
          for (let i = 0; i <= n; i++) {
            const x = a + i * deltaX;
            scope[variable] = x;
            const coef = (i === 0 || i === n) ? 0.5 : 1;
            sum += coef * math.evaluate(node, scope) * deltaX;
          }
        }
        
        return sum;
      } catch (e) {
        return `Error: ${e.message}`;
      }
    };
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions calculation but doesn't disclose computational method (e.g., numerical integration), accuracy considerations, performance characteristics, error handling, or what happens with invalid inputs. For a mathematical tool with 4 parameters, this is inadequate.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a straightforward mathematical operation and front-loaded with the core functionality.

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?

For a mathematical calculation tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain the return value format, error conditions, numerical method used, or how it relates to similar sibling tools. The context signals indicate significant gaps in documentation.

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 only 25% (only 'n' has a description), so the description must compensate but doesn't. It implies 'expression', 'start', and 'end' parameters through 'curve between two points' but provides no details about format, units, or constraints. The description adds marginal context but doesn't fully address the coverage gap.

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 tool's purpose with a specific verb ('calculate') and resource ('area under a curve between two points'). It distinguishes from some siblings like 'derivative' or 'limit' by focusing on area calculation, though it doesn't explicitly differentiate from 'integral' which might serve a similar purpose.

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 'integral', 'riemann_sum', or 'darboux_sum' which appear to be related mathematical operations. There's no mention of prerequisites, limitations, or comparative context with sibling tools.

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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