CodeScene MCP Server Creates Self-Correcting Code Health Feedback Loop — Shifts AI Agent Fix Rates from ~20% to 90-100% on Maintainability Issues

CodeScene·high signal

CodeScene's CodeHealth MCP Server exposes code health analysis as local MCP tools that AI coding agents call during development. Without structural guidance, frontier LLMs only fix ~20% of code health issues; with MCP-augmented CodeHealth data, fix rates reach 90-100%. The mechanism: after each code generation iteration, the agent re-measures Code Health score, gets concrete actionable feedback (complexity drivers, readability issues, structural smells), and adjusts — creating a deterministic self-correcting loop. Target Code Health score of 9.5+ before deploying agents on features.