About ai.smithery/magenie33-quality-dimension-generator
ai.smithery/magenie33-quality-dimension-generator is an MCP server in the Productivity category: generate tailored quality criteria and scoring guides from your task descriptions. Refine objectiv….
Install
git clone https://github.com/magenie33/quality-dimension-generatorThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
Tools (2)
generate_quality_dimensions_promptGenerate quality dimensions prompt based on task analysis. Uses default target score from configuration or task complexity to determine evaluation strictness.
Parameters not published.
generate_task_analysis_promptGenerate task analysis prompt for LLM to analyze core tasks in user conversations
Parameters not published.
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README
Quality Dimension Generator
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! 🚀
README copy captured when this server was indexed; it may lag the repository. The original is authoritative.