Skip to main content
Glama
seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

interpret_diagnostics

Interpret statistical diagnostic test results (VIF, DW, BP tests) to assess regression model validity and identify potential issues in quantitative analysis.

Instructions

진단 결과 해석 (VIF, DW, BP test 등)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
test_nameYes진단 검정명
statisticYes검정 통계량
p_valueNop-value
contextNo분석 맥락

Implementation Reference

  • The core handler function that executes the 'interpret_diagnostics' tool. It takes test_name and p_value (among others), provides predefined interpretations for common diagnostics like Hausman, Breusch-Pagan, Durbin-Watson tests based on p-value thresholds, and returns structured interpretation.
    function handleInterpretDiagnostics(args: Record<string, unknown>) {
      const testName = args.test_name as string;
      const pValue = args.p_value as number;
    
      const interpretations: Record<string, string> = {
        "hausman": pValue < 0.05 ? "FE 사용 권장 (RE 불일치)" : "RE 사용 가능 (더 효율적)",
        "breusch_pagan": pValue < 0.05 ? "이분산 존재 - Robust SE 사용" : "등분산 가정 충족",
        "durbin_watson": "DW≈2: 자기상관 없음, DW<1.5 또는 >2.5: 자기상관 의심"
      };
    
      return {
        test: testName,
        p_value: pValue,
        interpretation: interpretations[testName.toLowerCase()] || "검정 결과 해석 필요"
      };
    }
  • Input schema definition for the 'interpret_diagnostics' tool, specifying parameters like test_name (required), statistic (required), p_value, and context.
    inputSchema: {
      type: "object",
      properties: {
        test_name: { type: "string", description: "진단 검정명" },
        statistic: { type: "number", description: "검정 통계량" },
        p_value: { type: "number", description: "p-value" },
        context: { type: "string", description: "분석 맥락" },
      },
      required: ["test_name", "statistic"],
    },
  • Tool registration in the exported tools array, including name, description, and inputSchema. This makes it available via MCP.
      name: "interpret_diagnostics",
      description: "진단 결과 해석 (VIF, DW, BP test 등)",
      inputSchema: {
        type: "object",
        properties: {
          test_name: { type: "string", description: "진단 검정명" },
          statistic: { type: "number", description: "검정 통계량" },
          p_value: { type: "number", description: "p-value" },
          context: { type: "string", description: "분석 맥락" },
        },
        required: ["test_name", "statistic"],
      },
    },
  • Dispatch case in the main handleToolCall switch statement that routes calls to 'interpret_diagnostics' to its handler function.
    case "interpret_diagnostics":
      return handleInterpretDiagnostics(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 states the tool 'interprets' results, implying a read-only or analytical operation, but doesn't specify whether it modifies data, requires authentication, has rate limits, or describes the output format. For a tool with no annotations, this leaves significant behavioral gaps, though it doesn't contradict any annotations.

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 very concise—a single phrase in Korean with examples—and front-loaded with the core purpose. It wastes no words, but it might be overly brief for clarity in some contexts. Every part earns its place by specifying the action and examples, though it could benefit from more structure for non-native speakers.

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 of interpreting statistical diagnostics and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, how results are formatted, or any behavioral nuances. For a tool with 4 parameters and no output schema, more context is needed to guide effective use, making it inadequate for full understanding.

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 already documents all parameters (test_name, statistic, p_value, context) with descriptions. The tool description adds no additional meaning beyond the schema, such as explaining how parameters interact or providing usage examples. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'interpreting diagnostic results' with specific examples (VIF, DW, BP test), which are statistical diagnostics. It uses a specific verb ('interpret') and resource ('diagnostic results'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'interpret_coefficient' or 'interpret_test', which might handle different types of interpretation.

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. It doesn't mention prerequisites, context, or exclusions, nor does it reference sibling tools such as 'interpret_coefficient' or 'interpret_test' that might be relevant for similar tasks. Usage is implied only by the tool name and examples, lacking explicit instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/seanshin0214/quantmaster-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server