Lucidity MCP is an AI-powered code quality tool. It uses the Model Context Protocol (MCP) to help AI assistants review code. The tool analyzes git changes for problems like complexity and security issues through structured prompts.
To get started, clone the repository and set up a virtual environment. After installing the dependencies, run the server. You can then connect your AI assistant using the MCP protocol URI to begin receiving code quality feedback.
Its key features include comprehensive issue detection across 10 quality dimensions and contextual analysis that compares new changes to the original code. It is language agnostic, supports focused analysis for specific issues, and provides structured, actionable feedback. The tool integrates seamlessly with MCP-compatible AI assistants, has a lightweight and extensible framework, and performs git-aware analysis for pre-commit reviews.
Common use cases are analyzing code quality in git changes before committing, checking for security vulnerabilities, and ensuring adherence to coding standards. It is also used for identifying performance issues, code duplication, and for improving code abstractions and error handling.
For frequently asked questions: Lucidity MCP can analyze any programming language the connected AI assistant understands. It is open-source and free for everyone to use. It ensures code quality by analyzing code across multiple dimensions and providing detailed feedback and recommendations.