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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

test_selection

Select appropriate statistical tests by specifying analysis purpose and variable types for quantitative research projects.

Instructions

적절한 통계 검정 선택 가이드

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
purposeYes분석 목적
variable_typesYes변수 유형 정보
sample_characteristicsNo표본 특성

Implementation Reference

  • The handler function for the 'test_selection' tool. It is a placeholder implementation that echoes the input arguments and provides a suggestion message in Korean indicating that detailed implementation for test recommendation based on variable types is needed.
    function handleTestSelection(args: Record<string, unknown>) {
      return { args, suggestion: "Variable types에 따른 검정 추천 - 상세 구현 필요" };
    }
  • The input schema for the 'test_selection' tool, defining parameters for analysis purpose, variable types, and sample characteristics.
    inputSchema: {
      type: "object",
      properties: {
        purpose: {
          type: "string",
          enum: ["comparison", "relationship", "prediction", "model_fit"],
          description: "분석 목적"
        },
        variable_types: { type: "object", description: "변수 유형 정보" },
        sample_characteristics: { type: "object", description: "표본 특성" },
      },
      required: ["purpose", "variable_types"],
    },
  • Registration of the 'test_selection' tool in the exported tools array.
      name: "test_selection",
      description: "적절한 통계 검정 선택 가이드",
      inputSchema: {
        type: "object",
        properties: {
          purpose: {
            type: "string",
            enum: ["comparison", "relationship", "prediction", "model_fit"],
            description: "분석 목적"
          },
          variable_types: { type: "object", description: "변수 유형 정보" },
          sample_characteristics: { type: "object", description: "표본 특성" },
        },
        required: ["purpose", "variable_types"],
      },
    },
  • Registration of the 'test_selection' handler in the main tool dispatch switch statement within handleToolCall function.
    case "test_selection":
      return handleTestSelection(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 reveals nothing about what the tool actually does behaviorally—whether it generates recommendations, evaluates inputs, returns explanations, or performs calculations. It doesn't mention permissions, rate limits, or output format, leaving the agent completely in the dark about the tool's operation.

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 extremely concise—a single Korean phrase—with zero wasted words. However, this brevity comes at the cost of being under-specified rather than efficiently informative, slightly reducing its effectiveness as a standalone description.

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 implied by 3 parameters (including nested objects) and no annotations or output schema, the description is incomplete. It doesn't explain what the tool outputs (recommendations, explanations, code?) or how it behaves, making it inadequate for an agent to understand the tool's role among many statistical siblings.

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 documented in the schema itself (purpose with enum values, variable_types, sample_characteristics). The description adds no additional meaning about parameters beyond what's already in the structured schema, so it meets the baseline of 3 where the schema does the heavy lifting.

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 '적절한 통계 검정 선택 가이드' (appropriate statistical test selection guide) is a tautology that essentially restates the tool name 'test_selection' in Korean. It doesn't specify what action the tool performs (does it recommend, evaluate, or generate something?) or what resource it acts upon. Compared to siblings like 'suggest_method' or 'compare_methods', it lacks differentiation.

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. Given siblings like 'suggest_method', 'compare_methods', and 'get_method_guide' that might overlap in function, there's no indication of this tool's specific context, prerequisites, or exclusions. The agent must infer usage from parameters 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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