Helpful for diffs, but not enough when you need a full repository health picture.
Direct answer
AI code review usually evaluates a pull request, file, or snippet. Repository-level analysis goes further by mapping architecture, technical debt, security exposure, and maintainability risks across the entire codebase.
It is useful for reviewing diffs, suggesting cleaner implementations, spotting common mistakes, and explaining local code sections. It works best when the change is small and context is narrow.
It does not reliably show systemic risk across a mature repository. It can miss architectural drift, dependency concentration, outdated libraries, and modules that create long-term delivery friction.
LegacyCode MRI uses repository-level signals to make AI outputs more grounded. That helps the user understand not just whether code is clean, but whether the system is safe to evolve.