CodeHealth MCP Server ensures agents and AI coding assistants write maintainable, production-ready code without introducing technical debt. Using deterministic CodeHealth feedback, it guides agents to spot risks, improve unhealthy code, and refactor toward clear quality targets. Run it locally and keep full control of your workflow while making legacy systems more AI-ready. The result is more reliable AI-generated code, safer refactoring, and greater trust in real engineering workflows.
CodeHealth MCP Server by CodeScene
When we developed the CodeHealth MCP we benchmarked raw Claude Code refactoring against MCP-guided refactoring. The result: 2-5x improvement in how many code smells Claude Code could solve. And the type of work changed too, from more low level improvements like renames of variables to guided restructuring of the code.
Agents aren't lazy, they're just flying blind and have no incentive to do better.
Read the full thing here: https://codescene.com/blog/making-legacy-code-ai-ready-benchmarks-on-agentic-refactoring
CodeHealth MCP Server by CodeScene
@fredrik_ekstrand Indeed! What I like best is that the MCP takes away the pain of cleaning up poor AI generated code and allows me to do my work in a more holistic way, thus allowing me to achieve more, not only in velocity, but also in breath. I no longer think in terms of code, but in terms of architectural specs, and it's been liberating to me as a generalist.
This is clearly needed. Agents are capable of writing excellent code, but left alone they choose not to.
I try to find ways to micromanage quality less and this is the best Iâve seen so far.
CodeHealth MCP Server by CodeScene
@johan_martinssonInteresting point about micromanaging, it actually help you with that.
CodeHealth MCP Server by CodeScene
@johan_martinsson1Â Thank you! We think the CodeHealth MCP is the missing link in agentic programming. You should definitely give it a go!
CodeHealth MCP Server by CodeScene
@johan_martinsson1Â Not having to micromanage quality is exactly the goal, agents should self-correct, not wait for a human to notice the mess
Lancepilot
CodeHealth MCP Server by CodeScene
@odeth_negapatan1Â Thank you, Odeth!
It's important to have checks that verify AI created code. You could have unit tests in place and instruct AI to make sure that tests pass. You could instruct AI to always check that test coverage is a high percentage (at CodeScene we try to aim for 95%+), this way AI can deterministically check if tests cover the logic it created or not. Finally you could have our CodeHealth MCP which can check for code quality issues, degradations, do uplifting and safeguarding.
Does this help answer your question?
CodeHealth MCP Server by CodeScene
Thanks odeth_negapatan1 you should try it out!
CodeHealth MCP Server by CodeScene
@odeth_negapatan1Â
Thank you Odeth, really appreciate your kind words and looking forward to hearing your thoughts when you have tried it out :)
CodeHealth MCP Server by CodeScene
One thing we found in our research is that AI tends to struggle the most in already complex, low CodeHealth codebases, it doesnât just generate code, it amplifies existing issues.
We found that there's a 60% higher defect risk when applying AI coding tools to unhealthy Code. Here is a link to our whitepaper that is based on the research paper linked above.
Curious, how are you validating code quality when using AI tools today?
Are Claude Code or Codex not writing at a sufficient quality level right now? By the way, is there an integration with Claude Code as a skill or something similar? I would like to test it.
Really nice to see the great CodeScene tool as an mcp! But kotlin seems not supported đ