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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

publication_bias

Detect publication bias in meta-analyses using funnel plots, Egger's test, trim-and-fill, or selection models to assess research validity.

Instructions

출판편향 검정 가이드

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
methodsYes검정 방법 (funnel_plot, egger, trim_fill, selection_model)
kNo연구 수

Implementation Reference

  • Handler function implementing the 'publication_bias' tool. Returns a guide listing common publication bias tests such as funnel plot, Egger's test, trim-and-fill, and selection models based on input methods.
    function handlePublicationBias(args: Record<string, unknown>) {
      return {
        methods: args.methods,
        tests: {
          funnel_plot: "Visual asymmetry check",
          egger: "Regression test for asymmetry",
          trim_fill: "Impute missing studies",
          selection_model: "Model publication process"
        }
      };
    }
  • Registration of the 'publication_bias' tool in the tools array, including name, description, and input schema specifying methods array and optional study count k.
    name: "publication_bias",
    description: "출판편향 검정 가이드",
    inputSchema: {
      type: "object",
      properties: {
        methods: {
          type: "array",
          items: { type: "string" },
          description: "검정 방법 (funnel_plot, egger, trim_fill, selection_model)"
        },
        k: { type: "number", description: "연구 수" },
      },
      required: ["methods"],
    },
  • Switch case in handleToolCall that routes 'publication_bias' calls to the handlePublicationBias handler function.
    case "publication_bias":
      return handlePublicationBias(args);
  • Input schema for 'publication_bias' tool defining required 'methods' array of strings and optional 'k' number for number of studies.
    inputSchema: {
      type: "object",
      properties: {
        methods: {
          type: "array",
          items: { type: "string" },
          description: "검정 방법 (funnel_plot, egger, trim_fill, selection_model)"
        },
        k: { type: "number", description: "연구 수" },
      },
      required: ["methods"],
    },
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. '가이드' (guide) suggests it might provide instructions or recommendations rather than perform computations, but this is ambiguous—it could output text, code, or statistical results. The description doesn't clarify if it's read-only, requires specific inputs beyond the schema, has side effects, or details output format (e.g., textual guide vs. statistical report). For a tool with no annotations, this lack of behavioral context is a significant gap.

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 phrase '출판편향 검정 가이드', which is concise and front-loaded with the core purpose. There's no wasted text or redundancy. However, it's overly brief for a tool with parameters and potential complexity, bordering on under-specification rather than optimal conciseness, but it efficiently communicates the essence without fluff.

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 tool has 2 parameters, no annotations, and no output schema, the description is incomplete. It doesn't address what the tool outputs (e.g., a guide text, statistical results, or code), behavioral aspects like whether it performs calculations or provides instructions, or how it integrates with sibling tools. For a statistical testing tool in a server with many analytical siblings, more context is needed to understand its role and usage.

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 clear descriptions for both parameters: 'methods' as an array of strings for test methods (funnel_plot, egger, trim_fill, selection_model) and 'k' as a number for study count. The description adds no additional meaning beyond the schema, such as explaining method differences or typical 'k' values. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but there's no compensation for potential gaps like method selection guidance.

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 '출판편향 검정 가이드' (Publication bias testing guide) states the general purpose as a guide for publication bias testing, which is clear but vague. It specifies the domain (publication bias) and action (testing/guide), but lacks a specific verb-resource combination like 'perform publication bias tests' or 'guide through publication bias analysis methods'. It doesn't distinguish from siblings like 'meta_heterogeneity' or 'test_selection', which might cover related statistical concepts.

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 contexts like meta-analysis, statistical validation, or comparison with other tools (e.g., 'meta_heterogeneity' for heterogeneity testing or 'test_selection' for general testing). There's no indication of prerequisites, scenarios, or exclusions, leaving usage entirely implicit based on the tool name alone.

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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