codecompass-mcp

# CodeCompass A local code knowledge graph that gives AI agents a map of your codebase — so they navigate by structure instead of grepping blind, and know what's connected before they edit. One graph query replaces dozens of file opens: fewer tokens, less compute, and every answer comes with a verifiable `file:line`. And it compounds — descriptions, edges, and corrections agents write back survive re-indexes and persist across sessions, so each task starts from everything every previous session learned instead of from zero. No cloud. No API keys for core queries. One JSON graph per repo. Python, JavaScript/TypeScript, PHP, HTML/CSS. --- ## Why it's faster AI agents read files one at a time and grep to find their way. On a real task that means opening candidate after candidate to answer "who calls this?" or "what breaks if I change this?" CodeCompass answers those from a precomputed graph, so the agent reads *only the code it actually needs*. We benchmarked it against traditional grep/read on six standard tasks (impact, blast radius, dead code, flow trace, find-and-edit, feature scoping) across four real repos, measuring **tokens to a verified answer** — the query output *plus* the code still read to trust it.  CodeCompass wins every relational and discovery task; grep only holds even on a plain textual find of a known string. The advantage grows with codebase size and name collisions. Full breakdown, per-task numbers, and honest limitations in **[docs/benchmark-results.md](docs/benchmark-results.md)**. --- ## The workflow The graph turns navigation into a cheap, deterministic loop: **discover → trace → read → edit** 1. **Discover** — find the symbols you care about without opening files: | You have… | Use | |---|---| | a concept, name, or pattern | `grep` (regex over graph entities) | | an idea, not a name ("where does cachi…