This server helps you turn fuzzy task descriptions into concrete quality evaluation rubrics. It exposes two main operations: generate_task_analysis_prompt breaks down what you're trying to accomplish, then generate_quality_dimensions_prompt produces a structured JSON with 5 scoring dimensions, each with 10/8/6 point criteria and an overall target score. You'd reach for this when you need objective standards before starting work, whether that's writing documentation, building features, or creating content. The output gives you specific benchmarks to hit rather than subjective "good enough" judgments. It's essentially a quality framework generator that frontloads the evaluation criteria so you know what success looks like before you start.
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.
Install from the Smithery AI Model Context Protocol Registry:
🔗 Get Quality Dimension Generator on Smithery
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.
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
]
}
Contributions welcome! This project is open source under the MIT License.
Transform your work quality today! 🚀
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