ai.smithery/magenie33-quality-dimension-generator

AI 与智能体

by magenie33

根据任务描述生成定制化质量标准与评分指南,帮助细化目标并提升评估一致性。

什么是 ai.smithery/magenie33-quality-dimension-generator

根据任务描述生成定制化质量标准与评分指南,帮助细化目标并提升评估一致性。

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

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

Step 2: Generate quality standards

javascript
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":

json
{
  "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! 🚀

常见问题

ai.smithery/magenie33-quality-dimension-generator 是什么?

根据任务描述生成定制化质量标准与评分指南,帮助细化目标并提升评估一致性。

相关 Skills

Claude接口

by anthropics

Universal
热门

面向接入 Claude API、Anthropic SDK 或 Agent SDK 的开发场景,自动识别项目语言并给出对应示例与默认配置,快速搭建 LLM 应用。

想把Claude能力接进应用或智能体,用claude-api上手快、兼容Anthropic与Agent SDK,集成路径清晰又省心

AI 与智能体
未扫描171.4k

RAG架构师

by alirezarezvani

Universal
热门

聚焦生产级RAG系统设计与优化,覆盖文档切块、检索链路、索引构建、召回评估等关键环节,适合搭建可扩展、高准确率的知识库问答与检索增强应用。

面向RAG落地,把知识库、向量检索和生成链路系统串联起来,做架构设计时更清晰,也更少踩坑。

AI 与智能体
未扫描24.9k

多智能体架构

by alirezarezvani

Universal
热门

聚焦多智能体系统架构设计,梳理 Supervisor、Swarm、分层和 Pipeline 等模式,覆盖角色定义、通信协作与性能评估,适合规划稳健可扩展的 AI agent 编排方案。

帮你系统解决多智能体应用的架构设计与协同编排难题,适合构建复杂 AI 工作流,成熟度高、社区认可也很亮眼。

AI 与智能体
未扫描24.9k

相关 MCP Server

知识图谱记忆

编辑精选

by Anthropic

热门

Memory 是一个基于本地知识图谱的持久化记忆系统,让 AI 记住长期上下文。

帮 AI 和智能体补上“记不住”的短板,用本地知识图谱沉淀长期上下文,连续对话更聪明,数据也更可控。

AI 与智能体
89.7k

顺序思维

编辑精选

by Anthropic

热门

Sequential Thinking 是让 AI 通过动态思维链解决复杂问题的参考服务器。

这个服务器展示了如何让 Claude 像人类一样逐步推理,适合开发者学习 MCP 的思维链实现。但注意它只是个参考示例,别指望直接用在生产环境里。

AI 与智能体
89.2k

by deusdata

热门

持久化的代码库知识图谱,可跨会话保留上下文,在 session 重启或上下文压缩后仍能继续使用。

专治 AI 编程助手“会话失忆”,把代码库沉淀为持久知识图谱,重启或压缩上下文后也能无缝续上开发状态。

AI 与智能体
37.3k

评论