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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

interpret_model_fit

Interpret statistical model fit metrics like R², AIC, BIC, and Pseudo-R² to evaluate model performance and compare models for quantitative research.

Instructions

모형적합도 해석 (R², AIC, BIC, Pseudo-R²)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metricsYes적합도 지표들
model_typeYes모형 유형
comparisonNo모형 비교 여부

Implementation Reference

  • The handler function for 'interpret_model_fit' tool. Takes metrics object and returns interpretations for R², adjusted R², AIC, BIC in Korean.
    function handleInterpretModelFit(args: Record<string, unknown>) {
      const metrics = args.metrics as Record<string, number>;
      return {
        metrics,
        interpretation: {
          r_squared: metrics.r_squared ? `설명력: ${(metrics.r_squared * 100).toFixed(1)}%` : null,
          adj_r_squared: metrics.adj_r_squared ? `조정된 설명력: ${(metrics.adj_r_squared * 100).toFixed(1)}%` : null,
          aic: metrics.aic ? "낮을수록 좋음 (모형 비교용)" : null,
          bic: metrics.bic ? "낮을수록 좋음 (복잡성 페널티 더 큼)" : null
        }
      };
    }
  • Input schema for the 'interpret_model_fit' tool defining required metrics object, model_type string, and optional comparison boolean.
    inputSchema: {
      type: "object",
      properties: {
        metrics: { type: "object", description: "적합도 지표들" },
        model_type: { type: "string", description: "모형 유형" },
        comparison: { type: "boolean", description: "모형 비교 여부" },
      },
      required: ["metrics", "model_type"],
    },
  • Tool registration in the tools array, including name, description, and inputSchema.
      name: "interpret_model_fit",
      description: "모형적합도 해석 (R², AIC, BIC, Pseudo-R²)",
      inputSchema: {
        type: "object",
        properties: {
          metrics: { type: "object", description: "적합도 지표들" },
          model_type: { type: "string", description: "모형 유형" },
          comparison: { type: "boolean", description: "모형 비교 여부" },
        },
        required: ["metrics", "model_type"],
      },
    },
  • Registration of the handler in the main handleToolCall switch statement.
    case "interpret_model_fit":
      return handleInterpretModelFit(args);
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 only lists the metrics to be interpreted without explaining what the tool actually does with them (e.g., generates explanations, compares values, provides recommendations). This leaves critical behavioral traits like output format, processing logic, or any limitations undocumented.

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 listing the metrics—which is efficient and front-loaded. However, it's arguably too brief, as it doesn't provide enough context for effective use, but within the scope of conciseness, it avoids unnecessary verbosity.

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 interpreting model fit metrics, no annotations, and no output schema, the description is incomplete. It fails to explain what the tool outputs, how it handles different model types, or any prerequisites for use. This leaves significant gaps for an AI agent to understand and invoke the tool correctly in a statistical analysis context.

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 three parameters (metrics, model_type, comparison). The description doesn't add any meaning beyond what's in the schema—it doesn't clarify what 'metrics' should include, what 'model_type' values are expected, or how 'comparison' affects interpretation. Baseline 3 is appropriate when the schema does the heavy lifting.

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 purpose as interpreting model fit metrics (R², AIC, BIC, Pseudo-R²), which is a specific verb+resource combination. However, it doesn't distinguish itself from sibling tools like 'interpret_coefficient' or 'interpret_diagnostics', which also involve interpretation tasks in statistical analysis.

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. With sibling tools like 'interpret_coefficient' and 'interpret_diagnostics', there's no indication of the specific context or scenarios where this tool is appropriate, leaving the agent to guess based on the tool 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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