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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

meta_effect_size

Calculate and convert effect sizes for meta-analysis using various input formats like means, t-values, correlations, or odds ratios to standardize research findings.

Instructions

메타분석용 효과크기 계산 및 변환

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_typeYes입력 유형
valuesYes입력값
target_metricNo변환 목표 지표

Implementation Reference

  • Registration of the 'meta_effect_size' tool, including its name, description, and input schema for meta-analysis effect size calculation and conversion.
      name: "meta_effect_size",
      description: "메타분석용 효과크기 계산 및 변환",
      inputSchema: {
        type: "object",
        properties: {
          input_type: {
            type: "string",
            enum: ["means_sd", "t_value", "f_value", "correlation", "odds_ratio", "proportion"],
            description: "입력 유형"
          },
          values: { type: "object", description: "입력값" },
          target_metric: { type: "string", description: "변환 목표 지표" },
        },
        required: ["input_type", "values"],
      },
    },
  • Input schema defining parameters for effect size input type, values, and optional target metric.
    inputSchema: {
      type: "object",
      properties: {
        input_type: {
          type: "string",
          enum: ["means_sd", "t_value", "f_value", "correlation", "odds_ratio", "proportion"],
          description: "입력 유형"
        },
        values: { type: "object", description: "입력값" },
        target_metric: { type: "string", description: "변환 목표 지표" },
      },
      required: ["input_type", "values"],
    },
  • Switch case registration mapping the tool name to its handler function.
    case "meta_effect_size":
      return handleMetaEffectSize(args);
  • Handler function for 'meta_effect_size' tool. Currently a placeholder that echoes input parameters and provides example R code using escale for standardized mean difference calculation from the metafor package.
    function handleMetaEffectSize(args: Record<string, unknown>) {
      return {
        input_type: args.input_type,
        values: args.values,
        r_code: "escalc(measure = 'SMD', m1i, sd1i, n1i, m2i, sd2i, n2i, data)"
      };
    }
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 the tool performs 'calculation and conversion', implying a read-only operation, but doesn't clarify if it's deterministic, requires specific inputs, handles errors, or produces outputs in a particular format. For a tool with 3 parameters and no annotations, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient phrase in Korean that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple tool, though it could be more structured (e.g., by clarifying the scope). Every word earns its place, but it lacks front-loading of key details like differentiation from siblings.

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 complexity (3 parameters, nested objects, no output schema) and lack of annotations, the description is incomplete. It doesn't explain the output format, error handling, or how parameters interact, leaving the agent to guess. For a calculation tool with multiple input types and no output schema, more context is needed to ensure correct usage.

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 100%, so the schema already documents all parameters. The description adds no additional meaning about parameters beyond what's in the schema—it doesn't explain the 'values' object structure, 'target_metric' usage, or provide examples. With high schema coverage, the baseline is 3, as the description doesn't compensate but doesn't detract either.

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

Purpose3/5

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

The description '메타분석용 효과크기 계산 및 변환' (Meta-analysis effect size calculation and conversion) states the general purpose but lacks specificity. It mentions 'effect size' as the resource and 'calculation and conversion' as the verb, but doesn't distinguish it from sibling tools like 'calc_effect_size' or specify what makes it unique for meta-analysis. The purpose is clear but not differentiated from alternatives.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or exclusions, and it fails to differentiate from similar tools like 'calc_effect_size' or 'meta_heterogeneity'. Without any usage context, the agent must infer when this tool is appropriate based on the name alone.

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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