How to Set Up Automated Quality Gates for Claude Code with MCP
A tutorial details how to implement automated quality gates for AI-generated code using the Model Context Protocol (MCP) and LucidShark. The article highlights that standard AI feedback loops, such as compilation and test passing, do not measure structural code quality, leading to issues like complexity creep and style drift. By integrating LucidShark, a local-first static analysis tool, with an MCP server, developers can ensure that AI agents produce maintainable, secure, and well-structured code before it is committed. AI
IMPACT Enables developers to maintain code quality and architectural soundness when using AI coding agents.