Skip to main content
Glama
seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

reviewer_response

Generate structured responses to reviewer critiques on statistical methods, addressing issues like endogeneity, measurement, and robustness with data constraints.

Instructions

리뷰어 통계 지적 대응 가이드

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
critique_typeYes지적 유형
current_methodYes현재 사용 방법
data_constraintsNo데이터 제약

Implementation Reference

  • Registration of the 'reviewer_response' tool including name, description, and input schema.
    name: "reviewer_response",
    description: "리뷰어 통계 지적 대응 가이드",
    inputSchema: {
      type: "object",
      properties: {
        critique_type: {
          type: "string",
          enum: ["endogeneity", "selection", "measurement", "robustness", "sample_size", "identification"],
          description: "지적 유형"
        },
        current_method: { type: "string", description: "현재 사용 방법" },
        data_constraints: { type: "string", description: "데이터 제약" },
      },
      required: ["critique_type", "current_method"],
    },
  • The main handler function that executes the 'reviewer_response' tool, providing predefined response templates for reviewer critiques based on critique type.
    function handleReviewerResponse(args: Record<string, unknown>) {
      const critiqueType = args.critique_type as string;
      const responses: Record<string, any> = {
        endogeneity: {
          options: ["IV/2SLS", "Control function", "Selection model", "Bounds analysis"],
          template: "We address endogeneity concerns by [method]. Results remain robust..."
        },
        selection: {
          options: ["Heckman selection", "PSM", "Bounds", "Placebo tests"],
          template: "Selection concerns are addressed through [method]..."
        }
      };
    
      return {
        critique_type: critiqueType,
        response_guide: responses[critiqueType] || { message: "Response template not found" }
      };
    }
  • Tool registration in the main handleToolCall switch statement, dispatching to the handler.
    case "reviewer_response":
      return handleReviewerResponse(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. However, it offers no information on what the tool does (e.g., generates text, analyzes data, provides recommendations), its output format, permissions needed, or any side effects. This is inadequate for a tool with parameters and no output schema.

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 phrase that is under-specified rather than concise—it fails to convey essential information. While it is brief, it lacks structure and front-loading of key details, making it inefficient for agent understanding.

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 implied by parameters (e.g., statistical critique types) and the lack of annotations and output schema, the description is incomplete. It does not explain what the tool produces, how it behaves, or its role among sibling tools, leaving significant gaps for agent invocation.

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 (critique_type, current_method, data_constraints) with descriptions and enums. The description adds no additional meaning, syntax, or examples beyond what the schema provides, meeting the baseline score of 3 for high schema coverage.

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 '리뷰어 통계 지적 대응 가이드' (Reviewer Statistical Critique Response Guide) is vague and tautological—it restates the tool's name 'reviewer_response' without specifying what it actually does. It lacks a clear verb (e.g., 'generate', 'provide', 'analyze') and resource (e.g., 'responses', 'guidance', 'solutions'), making it unclear how this differs from siblings like 'robustness_guide' or 'suggest_method'.

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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention context, prerequisites, or exclusions, and it fails to differentiate from sibling tools such as 'robustness_guide' or 'check_assumptions', which might handle similar statistical critiques. This leaves the agent with no basis for selection.

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