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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

iv_strength_check

Check instrumental variable strength and validity for causal inference. Analyze F-statistics and overidentification tests to ensure reliable econometric models.

Instructions

도구변수 강도/유효성 검정 가이드

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
n_instrumentsYes도구변수 수
first_stage_fNo1단계 F-통계량
overid_testNo과대식별 검정 필요 여부

Implementation Reference

  • Handler function that implements the iv_strength_check tool logic: interprets first-stage F-statistic for instrument strength and suggests additional tests.
    function handleIvStrengthCheck(args: Record<string, unknown>) {
      const firstStageF = args.first_stage_f as number;
      return {
        first_stage_f: firstStageF,
        interpretation: firstStageF >= 10 ? "Strong instrument" : "Weak instrument concern",
        additional_tests: [
          "Stock-Yogo critical values",
          "Anderson-Rubin confidence sets (robust to weak IV)",
          "LIML estimator (less biased with weak IV)"
        ]
      };
    }
  • Tool schema definition including input parameters for IV strength check (number of instruments, first-stage F, overid test flag).
    {
      name: "iv_strength_check",
      description: "도구변수 강도/유효성 검정 가이드",
      inputSchema: {
        type: "object",
        properties: {
          n_instruments: { type: "number", description: "도구변수 수" },
          first_stage_f: { type: "number", description: "1단계 F-통계량" },
          overid_test: { type: "boolean", description: "과대식별 검정 필요 여부" },
        },
        required: ["n_instruments"],
      },
    },
  • Registration of the iv_strength_check handler in the main tool dispatcher switch statement.
    case "iv_strength_check":
      return handleIvStrengthCheck(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 mentions it's a 'guide' for instrumental variable strength/validity testing, without detailing what the tool actually does (e.g., performs calculations, returns diagnostics, provides recommendations). Critical behavioral traits like output format, computational requirements, or error handling are missing, leaving significant gaps.

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 a single phrase, which is concise but under-specified—it doesn't front-load actionable information. While not verbose, it fails to efficiently convey the tool's function, making it less helpful than a more informative yet still brief description would be.

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 and no output schema, the description is incomplete for a tool with 3 parameters. It lacks details on what the tool returns, how it behaves, or its practical use, which is insufficient for an AI agent to effectively invoke it. The high schema coverage doesn't compensate for missing behavioral and output context.

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 (n_instruments, first_stage_f, overid_test). The description adds no additional meaning or context beyond what's in the schema, such as explaining typical values or interactions. Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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 '도구변수 강도/유효성 검정 가이드' (Instrumental variable strength/validity test guide) is vague—it states the topic but not the specific action (e.g., 'check', 'calculate', 'evaluate'). It doesn't clearly distinguish this tool from siblings like 'check_assumptions' or 'test_selection', which could overlap in statistical testing contexts. The purpose is implied but lacks a clear verb+resource combination.

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 guidance is provided on when to use this tool versus alternatives. With siblings like 'check_assumptions' and 'test_selection' that might handle similar statistical tests, the description fails to specify context, prerequisites, or exclusions. Usage is only implied by the title, offering no explicit when/when-not instructions.

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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