官方说明10 个来源MCP 服务8 天前
hyperb1iss/lucidity-mcp
🐍 🏠 - Improve AI-generated code quality with prompt-based analysis across 10 dimensions, from complexity to…
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官方说明🐍 🏠 - Improve AI-generated code quality with prompt-based analysis across 10 dimensions, from complexity to security vulnerabilities.
配置模板（请按你的客户端调整）
claude mcp add lucidity-mcp -- npx -y lucidity-mcp
这是根据仓库名生成的可粘贴骨架，命令行参数与包名请以该项目 README 为准。
- ★ 数取值
- 90 ★ · hyperb1iss/lucidity-mcp · 取自 GitHub 搜索 API · （2026-10-16 20:00:20 核对）
- 来源明细
- 收录自 punkpeye/awesome-mcp-servers 榜单（我们从中收集的条目之一） · 条目类型：MCP 服务
- 收录与活跃
- 收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 10 个来源列出
- 评分构成
- 综合分 1,003 · 90 ★ · 10 个来源 · mcp
常见问题（答案都来自我们的记录）
怎么安装？
我们【没有】从该仓库取到安装命令（我们的 install 覆盖率只有约 4%）。请看它的 README 或 releases 页。
它适合谁 / 用来做什么？
我们把它归在这些标签下: MCP 服务。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
仓库
hyperb1iss/lucidity-mcp；我们收录于 2026-10-09；最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。第三方质量标记
✨ Lucidity MCP 🔍
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
**Clarity in Code, Confidence in Creation**
Lucidity is a Model Context Protocol (MCP) server designed to enhance the quality of AI-generated code through intelligent, prompt-based analysis. By providing structured guidance to AI coding assistants, Lucidity helps identify and address common quality issues, resulting in cleaner, more maintainable, and more robust code.
Before you commit, just ask Lucidity to analyze the changes instead of vibe-coding yourself into a nightmare hellscape! 😱 💥 🚫
💫 Features
- 🔮 **Comprehensive Issue Detection** - Covers 10 critical quality dimensions from complexity to security vulnerabilities
- 🔄 **Contextual Analysis** - Compares changes against original code to identify unintended modifications
- 🌐 **Language Agnostic** - Works with any programming language the AI assistant understands
- 🎯 **Focused Analysis** - Option to target specific issue types based on project needs
- 📝 **Structured Outputs** - Guides AI to provide actionable feedback with clear recommendations
- 🤖 **MCP Integration** - Seamless integration with Claude and other MCP-compatible AI assistants
- 🪶 **Lightweight Implementation** - Simple server design with minimal dependencies
- 🧩 **Extensible Framework** - Easy to add new issue types or refine analysis criteria
- 🔀 **Flexible Transport** - Supports both stdio for terminal-based interaction and SSE for network-based communication
- 🔄 **Git-Aware Analysis** - Analyzes changes directly from git diff, making it ideal for pre-commit reviews
🚀 Installation
📋 Prerequisites
- Python 3.13 or higher
- Git (for analyzing code changes)
- UV package manager (recommended for dependency management)
🔮 Quick Start
Run the Lucidity server
Using with AI Assistants
1. Start Lucidity in SSE mode:
2. Connect your AI assistant using the MCP protocol URI:
3. The AI can now invoke the analyze_changes tool to get code quality feedback!
🧠 Analysis Dimensions
Lucidity analyzes code across 10 critical quality dimensions:
1. **Unnecessary Complexity** - Identifies overly complex algorithms, excessive abstractions, and convoluted logic
2. **Poor Abstractions** - Detects leaky or inappropriate abstractions and unclear separation of concerns
3. **Unintended Code Deletion** - Catches accidental removal of critical functionality or validation
4. **Hallucinated Components** - Finds references to non-existent functions, classes, or APIs
5. **Style Inconsistencies** - Spots deviations from project coding standards and conventions
6. **Security Vulnerabilities** - Identifies potential security issues in code changes
7. **Performance Issues** - Detects inefficient algorithms or operations that could impact performance
8. **Code Duplication** - Finds repeated logic or functionality that should be refactored
9. **Incomplete Error Handling** - Spots missing or inadequate exception handling
10. **Test Coverage Gaps** - Identifies missing tests for critical functionality
📊 Example AI Assistant Queries
With an AI assistant connected to Lucidity, try these queries:
Lucidity is a Model Context Protocol (MCP) server designed to enhance the quality of AI-generated code through intelligent, prompt-based analysis. By providing structured guidance to AI coding assistants, Lucidity helps identify and address common quality issues, resulting in cleaner, more maintainable, and more robust code.
Before you commit, just ask Lucidity to analyze the changes instead of vibe-coding yourself into a nightmare hellscape! 😱 💥 🚫
💫 Features
- 🔮 **Comprehensive Issue Detection** - Covers 10 critical quality dimensions from complexity to security vulnerabilities
- 🔄 **Contextual Analysis** - Compares changes against original code to identify unintended modifications
- 🌐 **Language Agnostic** - Works with any programming language the AI assistant understands
- 🎯 **Focused Analysis** - Option to target specific issue types based on project needs
- 📝 **Structured Outputs** - Guides AI to provide actionable feedback with clear recommendations
- 🤖 **MCP Integration** - Seamless integration with Claude and other MCP-compatible AI assistants
- 🪶 **Lightweight Implementation** - Simple server design with minimal dependencies
- 🧩 **Extensible Framework** - Easy to add new issue types or refine analysis criteria
- 🔀 **Flexible Transport** - Supports both stdio for terminal-based interaction and SSE for network-based communication
- 🔄 **Git-Aware Analysis** - Analyzes changes directly from git diff, making it ideal for pre-commit reviews
🚀 Installation
📋 Prerequisites
- Python 3.13 or higher
- Git (for analyzing code changes)
- UV package manager (recommended for dependency management)
🔮 Quick Start
Run the Lucidity server
Using with AI Assistants
1. Start Lucidity in SSE mode:
2. Connect your AI assistant using the MCP protocol URI:
3. The AI can now invoke the analyze_changes tool to get code quality feedback!
🧠 Analysis Dimensions
Lucidity analyzes code across 10 critical quality dimensions:
1. **Unnecessary Complexity** - Identifies overly complex algorithms, excessive abstractions, and convoluted logic
2. **Poor Abstractions** - Detects leaky or inappropriate abstractions and unclear separation of concerns
3. **Unintended Code Deletion** - Catches accidental removal of critical functionality or validation
4. **Hallucinated Components** - Finds references to non-existent functions, classes, or APIs
5. **Style Inconsistencies** - Spots deviations from project coding standards and conventions
6. **Security Vulnerabilities** - Identifies potential security issues in code changes
7. **Performance Issues** - Detects inefficient algorithms or operations that could impact performance
8. **Code Duplication** - Finds repeated logic or functionality that should be refactored
9. **Incomplete Error Handling** - Spots missing or inadequate exception handling
10. **Test Coverage Gaps** - Identifies missing tests for critical functionality
📊 Example AI Assistant Queries
With an AI assistant connected to Lucidity, try these queries:
- 来源
- awesome · https://github.com/hyperb1iss/lucidity-mcp
- 按领域浏览
- MCP 服务
- Traction
- 90 ★
- 收录于
- 昨天
更多MCP 服务
所属领域与标签
每个标签都能点进去看同类的全部条目。数字是当前库里的真实条数。