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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

debug_code

Debug statistical code in R, Stata, or Python by analyzing error messages and problematic code segments to identify and resolve programming issues.

Instructions

통계 코드 디버깅 도움

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes문제 코드
error_messageYes에러 메시지
languageYes언어

Implementation Reference

  • The handler function for 'debug_code' tool. It takes code, error_message, and language as input and returns generic debugging suggestions.
    function handleDebugCode(args: Record<string, unknown>) {
      return {
        error: args.error_message,
        language: args.language,
        suggestions: [
          "Check variable names and data types",
          "Verify data structure (missing values, duplicates)",
          "Check package/library installation",
          "Review function syntax and arguments"
        ]
      };
    }
  • Input schema definition for the 'debug_code' tool, specifying parameters: code (string), error_message (string), language (enum: r, stata, python). All required.
    inputSchema: {
      type: "object",
      properties: {
        code: { type: "string", description: "문제 코드" },
        error_message: { type: "string", description: "에러 메시지" },
        language: { type: "string", enum: ["r", "stata", "python"], description: "언어" },
      },
      required: ["code", "error_message", "language"],
    },
  • Registration of the 'debug_code' tool in the exported tools array, including name, description, and input schema.
    {
      name: "debug_code",
      description: "통계 코드 디버깅 도움",
      inputSchema: {
        type: "object",
        properties: {
          code: { type: "string", description: "문제 코드" },
          error_message: { type: "string", description: "에러 메시지" },
          language: { type: "string", enum: ["r", "stata", "python"], description: "언어" },
        },
        required: ["code", "error_message", "language"],
      },
    },
  • Dispatch/registration of 'debug_code' handler in the handleToolCall switch statement.
    case "debug_code":
      return handleDebugCode(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. It only states it provides 'help' without explaining what the tool actually does (e.g., analyzes code, suggests fixes, returns explanations), its limitations, or any behavioral traits like error handling or output format.

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 overly concise to the point of under-specification—a single phrase in Korean that lacks structure or front-loading of key information. It fails to earn its place by not providing actionable details, making it inefficient rather than succinct.

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 tool's complexity (3 parameters, no annotations, no output schema) and rich sibling context, the description is severely incomplete. It does not explain what the tool returns, how it behaves, or its scope, leaving critical gaps for an agent to understand and use it effectively.

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%, so the schema fully documents the three parameters (code, error_message, language). The description adds no meaning beyond this, such as examples or constraints, but the high coverage justifies a baseline score of 3 as adequate.

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 '통계 코드 디버깅 도움' (statistical code debugging help) restates the tool name 'debug_code' in Korean with minimal elaboration, making it tautological. It specifies the domain (statistical code) but lacks a clear verb or distinct differentiation from sibling tools like 'optimize_code' or 'diagnose_regression', leaving the exact action vague.

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?

No guidance is provided on when to use this tool versus alternatives. With many sibling tools for code-related tasks (e.g., 'optimize_code', 'generate_python_code'), the description offers no context, exclusions, or prerequisites, failing to help an agent choose appropriately.

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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