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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

meta_code

Generate statistical code for meta-analysis in R or Stata to perform basic analysis, meta-regression, subgroup analysis, or sensitivity testing.

Instructions

메타분석 코드 생성 (metafor, meta, metan)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
analysisYes분석 유형
effect_measureNo효과크기 지표
languageYes언어

Implementation Reference

  • The handler function for the 'meta_code' tool. It returns predefined R and Stata code snippets for meta-analysis based on the provided analysis type and language.
    function handleMetaCode(args: Record<string, unknown>) {
      return {
        analysis: args.analysis,
        language: args.language,
        r_code: "library(metafor)\nres <- rma(yi, vi, data = dat)\nforest(res)",
        stata_code: "metan effect se, random"
      };
    }
  • Registration of the 'meta_code' tool in the exported tools array, including name, description, and input schema definition.
    {
      name: "meta_code",
      description: "메타분석 코드 생성 (metafor, meta, metan)",
      inputSchema: {
        type: "object",
        properties: {
          analysis: {
            type: "string",
            enum: ["basic_ma", "meta_regression", "subgroup", "sensitivity"],
            description: "분석 유형"
          },
          effect_measure: { type: "string", description: "효과크기 지표" },
          language: { type: "string", enum: ["r", "stata"], description: "언어" },
        },
        required: ["analysis", "language"],
      },
    },
  • Dispatch case in the handleToolCall switch statement that routes calls to the meta_code handler.
    case "meta_code":
      return handleMetaCode(args);
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions code generation but doesn't describe output format, error handling, or any behavioral traits like whether it requires specific inputs beyond parameters. This leaves significant gaps in understanding how the tool behaves.

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 a single, efficient sentence that directly states the tool's function. It's appropriately sized and front-loaded, with no wasted words, though it could be slightly more informative given the lack of other context.

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 a code-generation tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the generated code looks like, how it's returned, or any dependencies, leaving the agent with insufficient 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%, so the schema already documents all parameters with descriptions and enums. The description doesn't add any meaning beyond what the schema provides, such as explaining parameter interactions or usage examples, resulting in a baseline score of 3.

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

Purpose3/5

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

The description states the tool generates code for meta-analysis using specific packages (metafor, meta, metan), which clarifies the verb (generate) and resource (code). However, it doesn't differentiate from sibling tools like 'generate_r_code' or 'generate_stata_code' that might also produce code, making the purpose somewhat vague in context.

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 explicit guidance is provided on when to use this tool versus alternatives. The description implies it's for meta-analysis code generation, but it doesn't specify scenarios, prerequisites, or exclusions compared to other code-related tools in the list.

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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