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magenie33-quality-dimension-generator

magenie33-quality-dimension-generator

Remote
github.com/magenie33AI & Agents0Updated 57w ago
Repository

About magenie33-quality-dimension-generator

An MCP server that turns task descriptions into measurable quality criteria and scoring targets. It guides software, writing, project, and research work with task analysis followed by generated evaluation standards.

Key features

  1. 01Task analysis prompt generation
  2. 02Quality dimension generation
  3. 03Measurable scoring criteria
  4. 04Expected score targets

Use cases

  1. 01Set standards for code and testing
  2. 02Define writing quality criteria
  3. 03Specify project deliverable expectations
  4. 04Establish research evaluation criteria

MCP support

Provides tools that generate a task-analysis prompt and then a quality-dimensions prompt from the analysis.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://server.smithery.ai/@magenie33/quality-dimension-generator/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://server.smithery.ai/@magenie33/quality-dimension-generator/mcpAuth required

Tools

Details

From the project README

Quality Dimension Generator

License: MIT TypeScript MCP

An MCP server that generates quality evaluation standards for any task. Transform vague requirements into precise, measurable quality criteria with AI-powered analysis, ultimately improving your final work quality.

🎯 What It Does

  • 📊 Analyzes your tasks - Understand what needs to be accomplished
  • 🎯 Creates evaluation standards - Generate specific quality dimensions with scoring criteria
  • 📈 Sets target scores - Define expected quality levels (e.g., 8/10)
  • ✅ Guides execution - Help you complete tasks with clear quality standards

🚀 Quick Start

Installation

Install from the Smithery AI Model Context Protocol Registry:

🔗 Get Quality Dimension Generator on Smithery

Basic Usage

Step 1: Generate task analysis

generate_task_analysis_prompt({
  userMessage: "Write a 1000-word article about AI"
})

Step 2: Generate quality standards

generate_quality_dimensions_prompt({
  taskAnalysisJson: "..." // JSON from step 1
})

Result: Get comprehensive quality evaluation criteria with target scores, then complete your task following those standards.

📋 Example Output

For the task "Write a technical blog post":

{
  "expectedScore": 8,
  "scoreCalculation": "Average of all 5 dimension scores",
  "dimensions": [
    {
      "name": "Technical Accuracy",
      "description": "Correctness and depth of technical content",
      "importance": "Ensures readers get reliable information",
      "scoring": {
        "10": "All technical details verified and comprehensive",
        "8": "Mostly accurate with minor gaps",
        "6": "Generally correct but lacks depth"
      }
    }
    // ... 4 more dimensions
  ]
}

💡 Use Cases

  • Software Development - Code quality, testing, documentation standards
  • Content Creation - Writing quality, SEO, engagement metrics
  • Project Management - Deliverable criteria, timeline adherence
  • Research - Methodology, accuracy, presentation standards

🤝 Contributing

Contributions welcome! This project is open source under the MIT License.

🔗 Resources


Transform your work quality today! 🚀

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