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mean

Calculate the arithmetic mean of a list of numbers to determine their average value. This tool helps simplify statistical analysis by providing accurate results for numerical data sets.

Instructions

Calculates the arithmetic mean of a list of numbers

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
numbersYesArray of numbers to find the mean of

Implementation Reference

  • The handler function for the 'mean' MCP tool. It calls Statistics.mean on the input numbers and returns the result as a text content response.
    }, async ({ numbers }) => {
        const value = Statistics.mean(numbers)
    
        return {
            content: [{
                type: "text",
                text: `${value}`
            }]
        }
    })
  • Input schema for the 'mean' tool using Zod, requiring a non-empty array of numbers.
    numbers: z.array(z.number()).min(1).describe("Array of numbers to find the mean of")
  • src/index.ts:131-131 (registration)
    Registration of the 'mean' tool on the MCP mathServer, specifying name, description, and schema.
    mathServer.tool("mean", "Calculates the arithmetic mean of a list of numbers", {
  • The Statistics.mean static method that implements the arithmetic mean calculation logic used by the tool handler.
    static mean(numbers: number[]) {
        // Calculate sum and divide by the count of numbers
        const sum = numbers.reduce((accumulator, currentValue) => accumulator + currentValue, 0);
        const mean = sum / numbers.length;
    
        return mean
    }
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 states the calculation but does not cover error handling (e.g., for empty arrays or non-numeric inputs), performance considerations, or output format. This is a significant gap for a tool with no annotation support.

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 any wasted words. It is appropriately sized and front-loaded, making it easy to understand at a glance.

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 lack of annotations and output schema, the description is incomplete. It does not explain the return value (e.g., a single number), error conditions, or behavioral traits, which are crucial for an AI agent to use the tool correctly. This is inadequate for a tool with no structured support beyond the input schema.

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 the parameter 'numbers' fully documented in the schema. The description adds no additional semantic details beyond what the schema provides, such as examples or edge cases. Baseline 3 is appropriate as the schema handles the parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('calculates') and the resource ('arithmetic mean of a list of numbers'), distinguishing it from sibling tools like 'sum', 'median', or 'mode' by specifying the exact statistical operation. It avoids tautology by not merely repeating the tool name 'mean'.

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 'median' or 'sum', nor does it mention prerequisites or exclusions. It lacks context for selection among the many mathematical sibling tools, leaving usage implied rather than explicit.

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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