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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

code_template

Generate code templates for quantitative research workflows including regression, panel data, difference-in-differences, meta-analysis, and structural equation modeling in R, Stata, or Python.

Instructions

분석 전체 워크플로우 코드 템플릿

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflowYes워크플로우 유형
languageYes언어

Implementation Reference

  • The handler function that executes the 'code_template' tool logic. It returns a structured response with the requested workflow and language, providing a placeholder template message directing users to specific code generation tools.
    function handleCodeTemplate(args: Record<string, unknown>) {
      return {
        workflow: args.workflow,
        language: args.language,
        template: "Full workflow template - use specific code generation tools for detailed code"
      };
    }
  • The input schema defining the parameters for the 'code_template' tool: workflow type (regression_full, panel_full, etc.) and language (r, stata, python).
    inputSchema: {
      type: "object",
      properties: {
        workflow: {
          type: "string",
          enum: ["regression_full", "panel_full", "did_full", "meta_full", "sem_full"],
          description: "워크플로우 유형"
        },
        language: { type: "string", enum: ["r", "stata", "python"], description: "언어" },
      },
      required: ["workflow", "language"],
    },
  • The tool registration object in the exported tools array, including name, description, and inputSchema.
      name: "code_template",
      description: "분석 전체 워크플로우 코드 템플릿",
      inputSchema: {
        type: "object",
        properties: {
          workflow: {
            type: "string",
            enum: ["regression_full", "panel_full", "did_full", "meta_full", "sem_full"],
            description: "워크플로우 유형"
          },
          language: { type: "string", enum: ["r", "stata", "python"], description: "언어" },
        },
        required: ["workflow", "language"],
      },
    },
  • The switch case in handleToolCall that maps the 'code_template' tool name to its handler function.
    case "code_template":
      return handleCodeTemplate(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. However, it offers no information about the tool's behavior—such as whether it generates, modifies, or retrieves code templates, what permissions might be needed, or what the output looks like. This leaves critical behavioral traits completely undocumented.

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

Conciseness2/5

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

The description is a single, vague phrase that is under-specified rather than concise. It fails to front-load key information or structure content effectively, leaving the agent with insufficient detail to understand the tool's function. While brief, it lacks the clarity needed for efficient comprehension.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., code snippets, file paths, or structured data) or provide any behavioral context. For a tool with two required parameters and no structured output documentation, this minimal description is inadequate to guide proper usage.

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 schema description coverage is 100%, with both parameters ('workflow' and 'language') fully described in the schema via enums and descriptions. The description adds no additional meaning or context beyond what the schema already provides, so it meets the baseline score of 3 for high schema coverage without compensating value.

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 '분석 전체 워크플로우 코드 템플릿' (analysis entire workflow code template) is vague and tautological—it essentially restates the tool name 'code_template' with minimal added specificity. It mentions 'workflow' and 'code template' but doesn't clarify what the tool actually does (e.g., generates, retrieves, or applies templates). Compared to siblings like 'generate_python_code' or 'write_analysis_file', it lacks a clear verb and distinct purpose.

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 doesn't mention any context, prerequisites, or exclusions, and fails to differentiate it from sibling tools such as 'generate_python_code', 'generate_r_code', or 'meta_code'. Without any usage instructions, the agent is left guessing about its appropriate application.

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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