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seanshin0214

Dr. QuantMaster MCP Server

by seanshin0214

prereg_template

Generate pre-registration templates for research studies on OSF or AsPredicted platforms, specifying study type and hypotheses to ensure methodological transparency.

Instructions

사전등록 템플릿 생성 (OSF, AsPredicted)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformYes플랫폼
study_typeYes연구 유형
hypothesesNo가설 목록

Implementation Reference

  • The handler function implementing the logic for the 'prereg_template' tool. It returns a basic preregistration template structure with predefined sections based on the platform and study type inputs.
    function handlePreregTemplate(args: Record<string, unknown>) {
      return {
        platform: args.platform,
        sections: [
          "1. Study Information",
          "2. Design Plan",
          "3. Sampling Plan",
          "4. Variables",
          "5. Analysis Plan",
          "6. Other"
        ]
      };
    }
  • The tool definition including name, description, and input schema for validation of parameters: platform, study_type, and optional hypotheses.
    {
      name: "prereg_template",
      description: "사전등록 템플릿 생성 (OSF, AsPredicted)",
      inputSchema: {
        type: "object",
        properties: {
          platform: { type: "string", enum: ["osf", "aspredicted"], description: "플랫폼" },
          study_type: { type: "string", enum: ["experiment", "observational", "replication"], description: "연구 유형" },
          hypotheses: { type: "array", items: { type: "string" }, description: "가설 목록" },
        },
        required: ["platform", "study_type"],
      },
  • Registration of the tool handler in the main handleToolCall switch statement, mapping the tool name to its handler function.
    return handlePreregTemplate(args);
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. It states the tool creates templates but does not describe what 'create' entails—e.g., whether it generates a file, returns structured data, requires authentication, or has side effects. For a creation tool with zero annotation coverage, this is a significant gap in transparency about its behavior and implications.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—a single phrase in Korean that directly states the tool's function and target platforms. It is front-loaded with the core purpose and wastes no words, making it efficient for quick understanding without unnecessary detail.

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 template creation tool with no annotations and no output schema, the description is incomplete. It does not explain what the tool outputs (e.g., a template file, JSON structure), any behavioral traits like error handling, or usage context. For a tool that likely generates structured content, more information is needed to guide the agent 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 documented in the schema (platform, study_type, hypotheses). The description does not add any meaning beyond this, such as explaining the purpose of each parameter or providing examples. Since the schema fully covers the parameters, the baseline score of 3 is appropriate, as the description neither compensates nor adds value beyond the structured data.

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 purpose: '사전등록 템플릿 생성' (create preregistration template) and specifies the target platforms (OSF, AsPredicted). It uses a specific verb ('생성' - create) and identifies the resource (template). However, it does not explicitly differentiate from sibling tools, which include various research-related utilities like 'create_project_structure' or 'code_template', leaving some ambiguity about its unique role.

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 does not mention any prerequisites, context for use (e.g., during study planning), or exclusions. With many sibling tools for research tasks, the lack of usage context leaves the agent without clear direction on selecting this tool appropriately.

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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