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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

robustness_guide

Assess model robustness by evaluating alternative specifications and conducting sensitivity analysis to validate research findings.

Instructions

강건성 검정 가이드 (대안모형, 민감도분석)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
main_analysisYes주 분석 방법
concernsNo우려사항

Implementation Reference

  • Registration of the 'robustness_guide' tool, including its name, description, and input schema definition.
      name: "robustness_guide",
      description: "강건성 검정 가이드 (대안모형, 민감도분석)",
      inputSchema: {
        type: "object",
        properties: {
          main_analysis: { type: "string", description: "주 분석 방법" },
          concerns: { type: "array", items: { type: "string" }, description: "우려사항" },
        },
        required: ["main_analysis"],
      },
    },
  • The handler function `handleRobustnessGuide` that executes the tool logic, returning recommended robustness checks based on the main analysis.
    function handleRobustnessGuide(args: Record<string, unknown>) {
      return {
        main_analysis: args.main_analysis,
        robustness_checks: [
          "Alternative specifications (different controls)",
          "Different estimation methods",
          "Sample restrictions (outliers, subgroups)",
          "Alternative measures of key variables",
          "Placebo tests"
        ]
      };
    }
  • Tool registration in the main switch statement of `handleToolCall` function, mapping the tool name to its handler.
    case "robustness_guide":
      return handleRobustnessGuide(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 states it's a guide for robustness testing with alternative models and sensitivity analysis, but doesn't reveal if it's informational, interactive, requires specific inputs beyond parameters, or has any behavioral traits like rate limits, permissions, or output format. 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 front-loads the key terms. It's appropriately sized for a guide tool, with no wasted words, though it could be more structured if it included brief functional details.

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 no annotations, no output schema, and a tool with 2 parameters, the description is incomplete. It doesn't explain what the guide produces (e.g., text, code, recommendations), how it interacts with inputs, or any behavioral aspects. For a tool that might involve complex statistical guidance, this leaves too much ambiguity for effective use.

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 parameters 'main_analysis' and 'concerns' fully described in the schema. The description doesn't add any meaning beyond this, such as examples or contextual usage of parameters. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but no extra value is provided.

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 '강건성 검정 가이드 (대안모형, 민감도분석)' translates to 'Robustness test guide (alternative models, sensitivity analysis)', which indicates a guide for robustness testing with specific techniques mentioned. However, it's vague about what the tool actually does (e.g., generates guidance, explains methods, or performs analysis) and doesn't clearly distinguish from siblings like 'check_assumptions' or 'compare_methods' that might overlap in statistical validation contexts.

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?

No explicit guidance is provided on when to use this tool versus alternatives. The description lists techniques but doesn't specify contexts, prerequisites, or exclusions. Given siblings like 'check_assumptions' or 'compare_methods', users might be uncertain about when this guide is the appropriate choice for robustness-related tasks.

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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