Official description10 sourcesMCP servers8d ago
hyperb1iss/lucidity-mcp
๐ ๐ - Improve AI-generated code quality with prompt-based analysis across 10 dimensions, from complexity toโฆ
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Official description๐ ๐ - Improve AI-generated code quality with prompt-based analysis across 10 dimensions, from complexity to security vulnerabilities.
Config template (adjust for your client)
claude mcp add lucidity-mcp -- npx -y lucidity-mcp
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
- Star count
- 90 โ ยท hyperb1iss/lucidity-mcp ยท read from the GitHub search API ยท (checked 2026-10-16 20:00:20)
- Source
- collected from the punkpeye/awesome-mcp-servers list ยท type: MCP server
- Harvest history
- first seen 2026-10-09 ยท last seen 2026-10-16 ยท seen 32ร ยท listed by 10 sources
- Score
- score 1,003 ยท 90 โ ยท 10 sources ยท mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP ๆๅก. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository
hyperb1iss/lucidity-mcp๏ผfirst catalogued 2026-10-09๏ผlast seen 2026-10-16ใThere is no official certification or quality rating here โ we record sources and dates only.Third-party quality signals
โจ Lucidity MCP ๐
ไปฅไธๆๅฝ่ช่ฏฅไปๅบ็ README๏ผ็ๆฎต๏ผ ยท ๅจ GitHub ่ฏปๅฎๆด README โ
**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! ๐ฑ ๐ฅ ๐ซ
๐ซ Features
- ๐ฎ **Comprehensive Issue Detection** - Covers 10 critical quality dimensions from complexity to security vulnerabilities
- ๐ **Contextual Analysis** - Compares changes against original code to identify unintended modifications
- ๐ **Language Agnostic** - Works with any programming language the AI assistant understands
- ๐ฏ **Focused Analysis** - Option to target specific issue types based on project needs
- ๐ **Structured Outputs** - Guides AI to provide actionable feedback with clear recommendations
- ๐ค **MCP Integration** - Seamless integration with Claude and other MCP-compatible AI assistants
- ๐ชถ **Lightweight Implementation** - Simple server design with minimal dependencies
- ๐งฉ **Extensible Framework** - Easy to add new issue types or refine analysis criteria
- ๐ **Flexible Transport** - Supports both stdio for terminal-based interaction and SSE for network-based communication
- ๐ **Git-Aware Analysis** - Analyzes changes directly from git diff, making it ideal for pre-commit reviews
๐ Installation
๐ Prerequisites
- Python 3.13 or higher
- Git (for analyzing code changes)
- UV package manager (recommended for dependency management)
๐ฎ 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!
๐ง 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
๐ 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! ๐ฑ ๐ฅ ๐ซ
๐ซ Features
- ๐ฎ **Comprehensive Issue Detection** - Covers 10 critical quality dimensions from complexity to security vulnerabilities
- ๐ **Contextual Analysis** - Compares changes against original code to identify unintended modifications
- ๐ **Language Agnostic** - Works with any programming language the AI assistant understands
- ๐ฏ **Focused Analysis** - Option to target specific issue types based on project needs
- ๐ **Structured Outputs** - Guides AI to provide actionable feedback with clear recommendations
- ๐ค **MCP Integration** - Seamless integration with Claude and other MCP-compatible AI assistants
- ๐ชถ **Lightweight Implementation** - Simple server design with minimal dependencies
- ๐งฉ **Extensible Framework** - Easy to add new issue types or refine analysis criteria
- ๐ **Flexible Transport** - Supports both stdio for terminal-based interaction and SSE for network-based communication
- ๐ **Git-Aware Analysis** - Analyzes changes directly from git diff, making it ideal for pre-commit reviews
๐ Installation
๐ Prerequisites
- Python 3.13 or higher
- Git (for analyzing code changes)
- UV package manager (recommended for dependency management)
๐ฎ 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!
๐ง 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
๐ Example AI Assistant Queries
With an AI assistant connected to Lucidity, try these queries:
- Source
- awesome ยท https://github.com/hyperb1iss/lucidity-mcp
- Browse by field
- MCP servers
- Traction
- 90 โ
- added
- yesterday
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