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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

interpret_coefficient

Interpret regression coefficients from OLS, Logit, and interaction models to understand variable effects in statistical analysis.

Instructions

회귀계수 해석 가이드 (OLS, Logit, 상호작용 등)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_typeYes모형 유형
coefficientYes계수값
seNo표준오차
variable_typeNo변수 유형
transformationNo변환

Implementation Reference

  • Handler function that executes the tool logic: interprets the coefficient based on model type (OLS/linear or logit/logistic), computes odds ratio if applicable, and returns a structured interpretation with caution note.
    function handleInterpretCoefficient(args: Record<string, unknown>) {
      const modelType = args.model_type as string;
      const coefficient = args.coefficient as number;
      const variableType = args.variable_type as string;
    
      let interpretation = "";
    
      if (modelType === "ols" || modelType === "linear") {
        interpretation = `X가 1단위 증가하면 Y가 ${coefficient.toFixed(3)} 단위 변화 (다른 변수 통제)`;
      } else if (modelType === "logit" || modelType === "logistic") {
        const or = Math.exp(coefficient);
        interpretation = `Odds Ratio = ${or.toFixed(3)}. X가 1단위 증가하면 odds가 ${((or - 1) * 100).toFixed(1)}% 변화`;
      }
    
      return {
        model_type: modelType,
        coefficient,
        variable_type: variableType,
        interpretation,
        caution: "인과적 해석은 연구설계에 따라 결정"
      };
    }
  • Input schema definition for the interpret_coefficient tool, specifying parameters like model_type, coefficient (required), se, variable_type, and transformation.
    {
      name: "interpret_coefficient",
      description: "회귀계수 해석 가이드 (OLS, Logit, 상호작용 등)",
      inputSchema: {
        type: "object",
        properties: {
          model_type: { type: "string", description: "모형 유형" },
          coefficient: { type: "number", description: "계수값" },
          se: { type: "number", description: "표준오차" },
          variable_type: { type: "string", enum: ["continuous", "binary", "categorical", "interaction"], description: "변수 유형" },
          transformation: { type: "string", enum: ["none", "log", "standardized"], description: "변환" },
        },
        required: ["model_type", "coefficient"],
      },
  • Registration of the interpret_coefficient tool in the handleToolCall switch statement, mapping the tool name to its handler function.
    case "interpret_coefficient":
      return handleInterpretCoefficient(args);
Behavior1/5

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. The description only states it's a 'guide' for interpretation, but doesn't reveal what the tool actually does behaviorally—e.g., whether it generates text, calculates metrics, requires specific inputs beyond the schema, or has any limitations like handling only certain data types. This lack of detail makes it hard for an agent to predict the tool's behavior or outcomes.

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 very concise—a single phrase in Korean that efficiently conveys the core purpose. It's front-loaded with key terms like '회귀계수 해석 가이드' (regression coefficient interpretation guide). However, it could be more structured by explicitly stating the output or action, but its brevity avoids unnecessary fluff, making it easy 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 complexity of statistical interpretation and the lack of annotations and output schema, the description is incomplete. It doesn't specify what the tool returns (e.g., textual explanation, confidence intervals, or visual aids), which is critical for an agent to understand the tool's utility. With 5 parameters and no behavioral context, the description fails to provide enough information for effective tool selection and invocation in a statistical workflow.

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%, with all parameters well-documented in the schema (e.g., model_type, coefficient, se, variable_type with enums, transformation with enums). The description adds no additional meaning beyond the schema, as it doesn't explain how parameters interact or affect interpretation. Given the high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract from the schema's completeness.

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 '회귀계수 해석 가이드 (OLS, Logit, 상호작용 등)' states the general purpose as a guide for interpreting regression coefficients, mentioning specific model types (OLS, Logit) and interactions. However, it's somewhat vague about what the tool actually produces (e.g., textual explanation, visualization, or statistical output) and doesn't clearly differentiate from sibling tools like 'interpret_model_fit' or 'interpret_diagnostics', which could also involve interpretation tasks.

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 explicit guidance on when to use this tool versus alternatives. It mentions model types (OLS, Logit) and interactions, implying usage for regression analysis, but offers no context on prerequisites, when not to use it, or how it compares to siblings like 'interpret_model_fit'. Without such guidance, users may struggle to select the appropriate tool from the many statistical options available.

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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