Skip to main content
Glama
seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

meta_heterogeneity

Analyzes meta-analysis heterogeneity using Q, I², and τ² statistics to assess study variability and guide interpretation of pooled results.

Instructions

메타분석 이질성 해석 (Q, I², τ²)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
q_statNoQ 통계량
i_squaredYesI² 값
tau_squaredNoτ² 값
kYes연구 수

Implementation Reference

  • The handler function for the 'meta_heterogeneity' tool. It takes input arguments, extracts the I² value, and returns an interpretation of heterogeneity level (low, moderate, high) along with implications for fixed vs random effects models in meta-analysis.
    function handleMetaHeterogeneity(args: Record<string, unknown>) {
      const iSquared = args.i_squared as number;
      return {
        i_squared: iSquared,
        interpretation: iSquared < 25 ? "Low heterogeneity" : iSquared < 75 ? "Moderate heterogeneity" : "High heterogeneity",
        implications: iSquared > 50 ? "Random effects model 권장, 이질성 원인 탐색" : "Fixed effect 가능"
      };
    }
  • The tool registration object including name, description, and input schema definition for validating parameters like Q statistic, I², τ², and number of studies.
    {
      name: "meta_heterogeneity",
      description: "메타분석 이질성 해석 (Q, I², τ²)",
      inputSchema: {
        type: "object",
        properties: {
          q_stat: { type: "number", description: "Q 통계량" },
          i_squared: { type: "number", description: "I² 값" },
          tau_squared: { type: "number", description: "τ² 값" },
          k: { type: "number", description: "연구 수" },
        },
        required: ["i_squared", "k"],
      },
    },
  • The switch case in handleToolCall that routes calls to the meta_heterogeneity tool to its handler function.
    case "meta_heterogeneity":
      return handleMetaHeterogeneity(args);
    case "publication_bias":
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. The description only states the tool's purpose without revealing any behavioral traits: it doesn't specify whether this is a read-only operation, what the output format might be (e.g., textual interpretation, numerical results), error conditions, or computational characteristics (e.g., fast/slow, resource-intensive). For a tool with no annotation coverage, 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, efficient phrase that front-loads the core purpose without unnecessary words. It uses technical terminology appropriately for its domain (meta-analysis). However, it could be slightly more informative by clarifying the output or usage context, but as-is, it avoids redundancy and waste.

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 statistical interpretation and the lack of annotations and output schema, the description is incomplete. It states what the tool does but omits critical context: what the output looks like (e.g., a textual summary, classification of heterogeneity), how results should be used, or any limitations. For a tool with 4 parameters and no structured output information, this leaves too much ambiguity for an agent to invoke it effectively.

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 all parameters (q_stat, i_squared, tau_squared, k) clearly documented in the schema. The description adds no additional parameter semantics beyond what's in the schema—it lists Q, I², τ² in parentheses but doesn't explain their roles, relationships, or interpretation context. Since the schema already fully describes parameters, 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 function as '메타분석 이질성 해석 (Q, I², τ²)', which translates to 'Meta-analysis heterogeneity interpretation (Q, I², τ²)'. This specifies the verb 'interpret' and the resource 'heterogeneity' with specific statistical measures. It distinguishes from siblings like 'meta_effect_size' or 'meta_code' by focusing on heterogeneity metrics rather than effect sizes or code generation. However, it doesn't explicitly mention what 'interpretation' entails (e.g., providing textual explanations vs. calculations).

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 (e.g., needing Q, I², τ² values from prior analysis), when it's appropriate (e.g., after meta-analysis calculations), or what alternatives exist among siblings (e.g., 'meta_effect_size' for different aspects of meta-analysis). The tool name and description imply a niche statistical function, but without explicit usage context, an agent might struggle to apply it correctly.

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