Architecture-aware MCP for better AI code changes
BigIndexer is an MCP server that gives AI coding assistants (Claude, Cursor, etc.) real understanding of your codebase architecture — not just search. It identifies cluster boundaries, high-coupling seams, change impact, and — most importantly — BGI-TWINs: real behavioral twins (similar functions/patterns) already in your own repo, with seam guidance.
Validation I ran 100 scored evaluations across real repos (including cross-language). Results: Actionability jumped from ~4.0 → 4.75+, near-perfect boundary detection, zero hallucinations in the twin tools.
→ https://bigindexer.com/validation
Try it
pip install bigindexer
# then add via Claude Desktop / Cursor / etc.
GitHub: https://github.com/ahmedxuhri/bigindexer
Docs + full validation: https://bigindexer.com
I'm looking for feedback from people with large or multi-language repos. Does this kind of "in-repo pattern + boundary" context actually help you when making changes?