I turned my Python code quality tool into a budget-aware MCP server for AI agents

I already wrote about why I built CodeClone and why I care about baseline-aware code health: I built a baseline-aware Python code health tool for CI and AI-assisted coding This post is about what changed in 2.0.0b3. The short version: this is the first release where CodeClone feels less like a Python structural analysis CLI and more like a serious MCP surface for AI coding agents. Not by building a second engine. Not by adding AI-specific heuristics to the core. But by exposing the same deterministic, baseline-aware pipeline through a read-only MCP layer that agents can actually use. Why MCP mattered for CodeClone Once you start using coding agents seriously, the hard part is not "can the model write code?" The harder questions are: what changed structurally? is this debt new or already accepted in baseline? is this production risk or just test noise? should this block CI? what is the safest next refactor target? That is the gap I wanted CodeClone to close. What shipped in 2.0.0b3 The