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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

parallel_trends_check

Validate parallel trends assumption in difference-in-differences analysis using visual, statistical, or placebo approaches to ensure causal inference reliability.

Instructions

DID 평행추세 검정 가이드

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pre_periodsYes사전 기간 수
approachNo검정 방법

Implementation Reference

  • The handler function implementing the parallel_trends_check tool. It returns guidance on parallel trends checks for DID, including visual, statistical, and placebo approaches, along with R and Stata code examples.
    function handleParallelTrendsCheck(args: Record<string, unknown>) {
      return {
        pre_periods: args.pre_periods,
        approaches: {
          visual: "사전 기간 처치/통제 그룹 추세 비교 그래프",
          statistical: "사전 기간 시간×처치 상호작용항 유의성 검정",
          placebo: "가짜 처치 시점에서 효과 없음 확인"
        },
        r_code: "feols(y ~ i(time, treat, ref = -1) | id + time, data)",
        stata_code: "reghdfe y i.time#1.treat, absorb(id time) cluster(id)"
      };
    }
  • Registration of the parallel_trends_check tool in the tools array, including its name, description, and input schema definition.
    {
      name: "parallel_trends_check",
      description: "DID 평행추세 검정 가이드",
      inputSchema: {
        type: "object",
        properties: {
          pre_periods: { type: "number", description: "사전 기간 수" },
          approach: { type: "string", enum: ["visual", "statistical", "placebo"], description: "검정 방법" },
        },
        required: ["pre_periods"],
      },
  • Input schema for the parallel_trends_check tool, defining parameters pre_periods (required, number) and approach (string enum).
    inputSchema: {
      type: "object",
      properties: {
        pre_periods: { type: "number", description: "사전 기간 수" },
        approach: { type: "string", enum: ["visual", "statistical", "placebo"], description: "검정 방법" },
      },
      required: ["pre_periods"],
  • Switch case in handleToolCall that routes calls to parallel_trends_check to its handler function.
    case "parallel_trends_check":
      return handleParallelTrendsCheck(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. The description reveals nothing about what the tool actually does behaviorally—whether it's a read-only guide, a code generator, a statistical calculator, or something else. It doesn't mention permissions, side effects, output format, or any behavioral traits. This is inadequate for a tool with no annotation coverage.

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 Korean phrase that's under-specified rather than concise. While brief, it fails to convey essential information about the tool's purpose or behavior. Every sentence should earn its place, but this description doesn't provide enough substance to justify its existence as a helpful tool description.

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 statistical testing tools and the complete lack of annotations and output schema, the description is severely incomplete. It doesn't explain what the tool returns, how it behaves, or what users can expect. For a tool that presumably performs or guides through parallel trends testing (a non-trivial statistical concept), this minimal description fails to provide the necessary context for effective use.

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%, with both parameters ('pre_periods' and 'approach') having descriptions in the schema. The tool description adds no additional meaning about parameters beyond what's already documented in the schema. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no parameter information 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 'DID 평행추세 검정 가이드' (DID parallel trends test guide) is a tautology that essentially restates the tool name 'parallel_trends_check' in Korean with 'guide' added. It doesn't specify what the tool actually does (e.g., whether it performs the test, explains the methodology, or generates code). While it mentions DID (Difference-in-Differences), it doesn't distinguish this tool from sibling tools like 'event_study_guide' or 'check_assumptions' that might cover related concepts.

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 for using this tool. With many sibling tools related to causal inference and statistical methods (e.g., 'causal_design_guide', 'check_assumptions', 'event_study_guide'), users are left guessing about when this specific parallel trends check is appropriate.

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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