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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

marginal_effects_guide

Calculate and interpret marginal effects for statistical models including logit, probit, poisson, and tobit. Understand AME, MEM, and MER effect types with interaction considerations.

Instructions

한계효과 계산 및 해석 가이드

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_typeYes모형
effect_typeNo한계효과 유형
interactionNo상호작용항 포함

Implementation Reference

  • The handler function that implements the logic for the marginal_effects_guide tool, providing explanations of marginal effect types and code snippets for R and Stata.
    function handleMarginalEffectsGuide(args: Record<string, unknown>) {
      return {
        model_type: args.model_type,
        effect_types: {
          ame: "Average Marginal Effect - 평균적 한계효과",
          mem: "Marginal Effect at Mean - 평균값에서 한계효과",
          mer: "Marginal Effect at Representative values"
        },
        r_code: "margins::margins(model)",
        stata_code: "margins, dydx(*)"
      };
    }
  • Input schema defining parameters for the marginal_effects_guide tool: model_type (required), effect_type, and interaction.
    inputSchema: {
      type: "object",
      properties: {
        model_type: { type: "string", enum: ["logit", "probit", "poisson", "tobit"], description: "모형" },
        effect_type: { type: "string", enum: ["ame", "mem", "mer"], description: "한계효과 유형" },
        interaction: { type: "boolean", description: "상호작용항 포함" },
      },
      required: ["model_type"],
    },
  • Tool registration object in the tools array, including name, description, and inputSchema.
    {
      name: "marginal_effects_guide",
      description: "한계효과 계산 및 해석 가이드",
      inputSchema: {
        type: "object",
        properties: {
          model_type: { type: "string", enum: ["logit", "probit", "poisson", "tobit"], description: "모형" },
          effect_type: { type: "string", enum: ["ame", "mem", "mer"], description: "한계효과 유형" },
          interaction: { type: "boolean", description: "상호작용항 포함" },
        },
        required: ["model_type"],
      },
    },
  • Switch case in handleToolCall that routes calls to the marginal_effects_guide handler.
    case "marginal_effects_guide":
      return handleMarginalEffectsGuide(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. The description only states it's a 'guide' for marginal effects, implying it provides informational or explanatory content rather than performing computations. However, it fails to disclose key behavioral traits such as whether it generates code, provides textual explanations, requires specific data inputs beyond parameters, or has any side effects. For a tool with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 addresses the tool's topic. It is appropriately sized for a guide tool, with no wasted words. However, it lacks front-loading of critical information (e.g., what the guide produces), which slightly reduces its effectiveness, but it remains concise.

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 guidance tools and the lack of annotations and output schema, the description is incomplete. It does not specify what the guide outputs (e.g., text, code, examples), its format, or how it integrates with other tools. For a tool with 3 parameters and no structured output information, the description should provide more context about the tool's behavior and results to be sufficiently complete.

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 (model_type, effect_type, interaction) having descriptions in the schema. The tool description adds no additional meaning about parameters beyond what the schema already provides (e.g., it doesn't explain what 'ame', 'mem', 'mer' stand for or how interaction affects the guide). With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract.

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

Purpose2/5

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

The description '한계효과 계산 및 해석 가이드' (Marginal effects calculation and interpretation guide) is a tautology that essentially restates the tool name 'marginal_effects_guide' in Korean. It specifies the general domain (marginal effects) but lacks a clear verb indicating what the tool actually does (e.g., generates, explains, computes). It does not distinguish this tool from potential siblings like 'interpret_coefficient' or 'get_method_guide' that might also relate to statistical interpretation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

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. It does not mention any context, prerequisites, or exclusions. Given the many sibling tools related to statistical methods (e.g., 'interpret_coefficient', 'get_method_guide', 'bayesian_guide'), the absence of usage guidelines leaves the agent guessing about when this specific marginal effects guide is appropriate.

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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