AI has no objective way to assess the quality of the code it generates or modifies.
With MCP Server we’re trying to bridge that gap. It allows AI assistants to request code health insights directly from a codebase.
This means that AI can now identify the most urgent design problems, propose targeted refactorings, and verify whether those changes actually improve code health.
By giving the AI-coding assistant a way to understand the code health of a codebase, to understand the existing code, we enable the AI and dev to collaborate on improving code health and make the AI accountable for the code it produces.
The MCP server safeguards the quality of the code, making sure no technical debt or declined quality is introduced to production environment.
That way, the human is in the lo…
AI has no objective way to assess the quality of the code it generates or modifies.
With MCP Server we’re trying to bridge that gap. It allows AI assistants to request code health insights directly from a codebase.
This means that AI can now identify the most urgent design problems, propose targeted refactorings, and verify whether those changes actually improve code health.
By giving the AI-coding assistant a way to understand the code health of a codebase, to understand the existing code, we enable the AI and dev to collaborate on improving code health and make the AI accountable for the code it produces.
The MCP server safeguards the quality of the code, making sure no technical debt or declined quality is introduced to production environment.
That way, the human is in the loop and we can control the quality of the output.
In practice, this means the AI no longer works in isolation. It collaborates with code health analysis to understand architectural issues, reason about complexity, and make measurable improvements.
These three use cases really resonate:
- Safeguard new code: Prevent AI from introducing technical debt by flagging code health issues like excess complexity, low cohesion, etc.
- Targeted Refactoring: Yes, AI tools can refactor code, but they lack direction on what to fix and how to measure if it helped. The MCP Server largely solves the problem by giving the AI precise insight into design problems.
- Understand existing code: LLMs have their flaws, but they are generally great at summarizing information. We can use this to create reports and diagnostics from all the detailed code health reviews.
Join our live session, we’re taking our MCP Server for a spin and we’ll also explore code health metric in depth and other practical tools and techniques for crafting healthy code to stay ahead of technical debt.
Short demo What are your thoughts?