CodeHealth MCP Server by CodeScene

The server runs locally and monitors code produced by AI assistants, comparing each change against CodeHealth™ metrics that flag structural risk and technical‑debt buildup. When a risk is detected, the server returns feedback so the AI can adjust its output in real time, creating a self‑correcting loop that continues until the code meets predefined health thresholds. This process is model‑agnostic, allowing any AI coding agent to be guided by the same health signals.

Developers and engineering teams that integrate AI‑generated code into existing codebases can use the server to keep that code maintainable and to prepare legacy systems for AI‑driven development. By enforcing health constraints before code is accepted, the tool aims to reduce defect risk and improve the reliability of AI‑assisted changes.

The distinctive aspect is the tight feedback cycle: AI code is repeatedly evaluated and refactored until it satisfies the CodeHealth™ criteria, rather than merely passing tests. This approach is reported to raise fix rates from around 20 % to 90–100 % and lower defect risk by roughly 60 % when the code is kept healthy.