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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

sem_guide

Generate structural equation modeling guides for CFA, path, full SEM, and multigroup analyses to support quantitative research design and interpretation.

Instructions

구조방정식모형 가이드 (측정모형, 구조모형)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_typeYes모형 유형
fit_indicesNo적합도 지수

Implementation Reference

  • The primary handler function implementing the sem_guide tool. It returns guidelines on SEM model fit indices (acceptable and good thresholds) and an example R code snippet using the lavaan package for structural equation modeling.
    function handleSemGuide(args: Record<string, unknown>) {
      return {
        model_type: args.model_type,
        fit_indices: {
          acceptable: "CFI > .90, TLI > .90, RMSEA < .08, SRMR < .08",
          good: "CFI > .95, TLI > .95, RMSEA < .06, SRMR < .05"
        },
        r_code: "library(lavaan)\nfit <- sem(model, data = df)\nsummary(fit, fit.measures = TRUE)"
      };
    }
  • The input schema defining parameters for the sem_guide tool: model_type (enum: cfa, path, full_sem, multigroup) as required, and optional fit_indices object.
    inputSchema: {
      type: "object",
      properties: {
        model_type: { type: "string", enum: ["cfa", "path", "full_sem", "multigroup"], description: "모형 유형" },
        fit_indices: { type: "object", description: "적합도 지수" },
      },
      required: ["model_type"],
    },
  • Registration of the sem_guide tool in the exported tools array, including name, description, and full input schema.
    {
      name: "sem_guide",
      description: "구조방정식모형 가이드 (측정모형, 구조모형)",
      inputSchema: {
        type: "object",
        properties: {
          model_type: { type: "string", enum: ["cfa", "path", "full_sem", "multigroup"], description: "모형 유형" },
          fit_indices: { type: "object", description: "적합도 지수" },
        },
        required: ["model_type"],
      },
    },
  • Handler dispatch for sem_guide in the main handleToolCall switch statement, routing calls to the handleSemGuide function.
    case "sem_guide":
      return handleSemGuide(args);
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavioral traits. However, it only names the tool and its components without explaining what the tool does (e.g., whether it generates code, provides explanations, or performs calculations), its output format, any side effects, or operational constraints like rate limits. This lack of information makes it impossible for an agent to understand the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with a single phrase, but it is not front-loaded with actionable information. It wastes space by repeating the tool name and adding vague parenthetical terms without clarifying the tool's function. While brief, it fails to convey essential details efficiently, making it under-specified rather than optimally concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of SEM tools, no annotations, no output schema, and a vague description, this is highly incomplete. The description does not explain what the tool produces (e.g., a guide document, code, analysis), how to interpret results, or any dependencies. It leaves critical gaps for an agent to use the tool effectively, especially compared to more detailed sibling tools.

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?

The input schema has 100% description coverage, with parameters 'model_type' (with enum values) and 'fit_indices' documented in the schema. The description adds no meaning beyond this, as it does not mention parameters at all. According to the rules, with high schema coverage (>80%), the baseline score is 3 when no parameter info is provided in the description.

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 '구조방정식모형 가이드 (측정모형, 구조모형)' translates to 'Structural Equation Modeling Guide (Measurement Model, Structural Model)', which restates the tool name 'sem_guide' in Korean and adds parenthetical terms that are essentially synonyms or components of SEM. It does not specify a clear action (e.g., 'generate', 'explain', 'analyze') or distinguish this tool from siblings like 'mlm_guide' or 'timeseries_guide'. This is a tautology with minimal added value.

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 comparisons to sibling tools such as 'get_method_guide' or 'suggest_method', which might offer overlapping functionality. There is no explicit or implied usage advice, leaving the agent without direction.

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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