Dr. QuantMaster MCP Server

Instructions

Implementation Reference

• src/tools/index.ts:1255-1282 (handler)

  Implements the core logic for mde_calculator tool: calculates Minimum Detectable Effect (MDE) for proportion or mean tests using standard normal approximations for alpha=0.05 and power=0.80.

  function handleMdeCalculator(args: Record<string, unknown>) {
    const nPerGroup = args.n_per_group as number;
    const baseline = args.baseline as number;
    const alpha = (args.alpha as number) || 0.05;
    const power = (args.power as number) || 0.80;
    const testType = (args.test_type as string) || "proportion";
  
    const zAlpha = 1.96;
    const zBeta = 0.84;
  
    let mde: number;
    if (testType === "proportion") {
      const se = Math.sqrt(2 * baseline * (1 - baseline) / nPerGroup);
      mde = (zAlpha + zBeta) * se;
    } else {
      mde = (zAlpha + zBeta) / Math.sqrt(nPerGroup / 2);
    }
  
    return {
      n_per_group: nPerGroup,
      baseline,
      alpha,
      power,
      mde: mde.toFixed(4),
      mde_percentage: (mde / baseline * 100).toFixed(2) + "%",
      interpretation: `현재 표본으로 baseline 대비 ${(mde / baseline * 100).toFixed(1)}% 차이 탐지 가능`
    };
  }

• src/tools/index.ts:157-170 (schema)

  Defines the input schema and metadata for the mde_calculator tool, including parameters for sample size per group, baseline value, significance level, power, and test type.

  {
    name: "mde_calculator",
    description: "최소탐지효과크기(MDE) 계산 - A/B 테스트, 실험설계",
    inputSchema: {
      type: "object",
      properties: {
        n_per_group: { type: "number", description: "그룹당 표본크기" },
        baseline: { type: "number", description: "기준값 (비율 또는 평균)" },
        alpha: { type: "number", description: "유의수준" },
        power: { type: "number", description: "검정력" },
        test_type: { type: "string", enum: ["proportion", "mean"], description: "검정 유형" },
      },
      required: ["n_per_group", "baseline"],
    },

• src/tools/index.ts:9-170 (registration)

  Registers the mde_calculator tool in the exported tools array used by the MCP server.

  export const tools: Tool[] = [
    // === CATEGORY 1: Knowledge Search (5 tools) ===
    {
      name: "search_stats_knowledge",
      description: "통계/계량경제학 지식베이스 RAG 검색. 방법론, 가정, 해석 가이드 제공",
      inputSchema: {
        type: "object",
        properties: {
          query: { type: "string", description: "검색 쿼리" },
          category: {
            type: "string",
            enum: ["foundations", "regression", "econometrics", "advanced", "meta", "all"],
            description: "검색 카테고리"
          },
          n_results: { type: "number", description: "결과 수 (기본: 5)" },
        },
        required: ["query"],
      },
    },
    {
      name: "get_method_guide",
      description: "특정 통계 방법의 상세 가이드 (가정, 절차, 해석, 보고)",
      inputSchema: {
        type: "object",
        properties: {
          method: {
            type: "string",
            description: "방법론 (예: ols, panel_fe, did, sem, meta)"
          },
          aspect: {
            type: "string",
            enum: ["assumptions", "procedure", "interpretation", "reporting", "all"],
            description: "가이드 측면"
          },
        },
        required: ["method"],
      },
    },
    {
      name: "suggest_method",
      description: "연구질문과 데이터 특성에 맞는 통계 방법 추천",
      inputSchema: {
        type: "object",
        properties: {
          research_question: { type: "string", description: "연구 질문" },
          dv_type: {
            type: "string",
            enum: ["continuous", "binary", "ordinal", "count", "time_to_event", "proportion"],
            description: "종속변수 유형"
          },
          data_structure: {
            type: "string",
            enum: ["cross_sectional", "panel", "time_series", "clustered", "multilevel"],
            description: "데이터 구조"
          },
          causal_design: {
            type: "string",
            enum: ["none", "experimental", "quasi_experimental", "observational"],
            description: "인과추론 설계"
          },
        },
        required: ["research_question", "dv_type"],
      },
    },
    {
      name: "compare_methods",
      description: "여러 통계 방법 비교 (장단점, 적용조건)",
      inputSchema: {
        type: "object",
        properties: {
          methods: {
            type: "array",
            items: { type: "string" },
            description: "비교할 방법들"
          },
          criteria: {
            type: "array",
            items: { type: "string" },
            description: "비교 기준 (예: assumptions, efficiency, robustness)"
          },
        },
        required: ["methods"],
      },
    },
    {
      name: "get_formula",
      description: "통계 수식 및 LaTeX 표기법 제공",
      inputSchema: {
        type: "object",
        properties: {
          concept: { type: "string", description: "개념 (예: ols_estimator, did_att, hausman_test)" },
          format: { type: "string", enum: ["latex", "text", "both"], description: "출력 형식" },
        },
        required: ["concept"],
      },
    },
  
    // === CATEGORY 2: Sample Size & Power (5 tools) ===
    {
      name: "calc_sample_size",
      description: "필요 표본크기 계산 (t-test, ANOVA, regression, proportion)",
      inputSchema: {
        type: "object",
        properties: {
          test_type: {
            type: "string",
            enum: ["t_test_two", "t_test_paired", "anova", "regression", "proportion", "chi_square"],
            description: "검정 유형"
          },
          effect_size: { type: "number", description: "효과크기 (Cohen's d, f, f²)" },
          alpha: { type: "number", description: "유의수준 (기본: 0.05)" },
          power: { type: "number", description: "검정력 (기본: 0.80)" },
          groups: { type: "number", description: "집단 수 (ANOVA)" },
          predictors: { type: "number", description: "예측변수 수 (회귀)" },
        },
        required: ["test_type", "effect_size"],
      },
    },
    {
      name: "calc_power",
      description: "주어진 표본크기에서 검정력 계산",
      inputSchema: {
        type: "object",
        properties: {
          test_type: { type: "string", description: "검정 유형" },
          n: { type: "number", description: "표본크기" },
          effect_size: { type: "number", description: "효과크기" },
          alpha: { type: "number", description: "유의수준" },
        },
        required: ["test_type", "n", "effect_size"],
      },
    },
    {
      name: "calc_effect_size",
      description: "효과크기 계산 및 해석 (Cohen's d, η², f², OR, RR)",
      inputSchema: {
        type: "object",
        properties: {
          type: {
            type: "string",
            enum: ["cohens_d", "eta_squared", "f_squared", "odds_ratio", "correlation"],
            description: "효과크기 유형"
          },
          values: { type: "object", description: "계산에 필요한 값들" },
        },
        required: ["type", "values"],
      },
    },
    {
      name: "mde_calculator",
      description: "최소탐지효과크기(MDE) 계산 - A/B 테스트, 실험설계",
      inputSchema: {
        type: "object",
        properties: {
          n_per_group: { type: "number", description: "그룹당 표본크기" },
          baseline: { type: "number", description: "기준값 (비율 또는 평균)" },
          alpha: { type: "number", description: "유의수준" },
          power: { type: "number", description: "검정력" },
          test_type: { type: "string", enum: ["proportion", "mean"], description: "검정 유형" },
        },
        required: ["n_per_group", "baseline"],
      },

• src/tools/index.ts:800-801 (registration)

  Registers the handler function in the main tool dispatcher switch statement.

  case "mde_calculator":
    return handleMdeCalculator(args);