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nbiish
by nbiish

logarithm

Calculate logarithms with any base for mathematical and financial analysis. This tool computes logarithmic values to support complex calculations in AI code assistants.

Instructions

Calculate logarithm with any base

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYes
baseNo

Implementation Reference

  • The handler function computes the logarithm of the given value using the specified base (defaults to natural log if base not provided) using the change of base formula.
    async ({ value, base = Math.E }) => {
      return Math.log(value) / Math.log(base);
    }
  • Defines the input schema with 'value' (required number) and 'base' (optional number), and output schema as a number.
    inputSchema: z.object({
      value: z.number(),
      base: z.number().optional()
    }),
    outputSchema: z.number(),
  • index.js:175-188 (registration)
    Registers the 'logarithm' tool using ai.defineTool, including name, description, schemas, and inline handler.
    ai.defineTool(
      {
        name: 'logarithm',
        description: 'Calculate logarithm with any base',
        inputSchema: z.object({
          value: z.number(),
          base: z.number().optional()
        }),
        outputSchema: z.number(),
      },
      async ({ value, base = Math.E }) => {
        return Math.log(value) / Math.log(base);
      }
    );
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe error handling (e.g., for invalid inputs like negative values or base=1), performance characteristics, or output format. For a computational tool with zero annotation coverage, this is a significant gap in transparency.

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 with zero waste. It's appropriately sized for a simple mathematical function and front-loaded with the core purpose, making it easy for an agent to parse quickly.

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 tool's computational nature, lack of annotations, and no output schema, the description is incomplete. It doesn't cover error conditions, mathematical constraints (e.g., domain restrictions), or what the return value represents, which are critical for reliable tool invocation in a mathematical context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'any base' which hints at the 'base' parameter, but doesn't explain the 'value' parameter or provide any details on valid ranges, units, or default behaviors. The description adds minimal value beyond the schema's structural information.

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 function as 'Calculate logarithm with any base', which is a specific verb ('calculate') and resource ('logarithm'). It distinguishes from most siblings (e.g., 'area', 'derivative', 'integral') by specifying the mathematical operation, though it doesn't explicitly differentiate from all possible mathematical tools.

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. It doesn't mention any prerequisites, constraints, or comparison with sibling tools like 'exponential' or 'solve', leaving the agent to infer usage context solely from the tool name and description.

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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