AI coding assistants frequently struggle to understand complex brownfield projects, resorting to inefficient brute-force methods that lead to context overload, partial information, and poor suggestions. Ctxo solves this by acting as a Model Context Protocol (MCP) server, furnishing AI agents with dependency-aware and history-enriched code intelligence. It transforms hundreds of individual file reads into a single, comprehensive call, delivering relevant symbol graphs, blast radius, git intent, and risk scores, thereby significantly reducing token usage, speeding up responses, and enhancing the accuracy of AI-driven code analysis and generation.
Características Principales
01Logic-Slice based Context Retrieval with transitive dependencies (L1-L4 detail)
02Git Commit Intent and Anti-Pattern Detection ('why context')
03Comprehensive PR Impact Assessment in a single call
046 GitHub stars
05Blast Radius Analysis to identify impacts of code changes (confirmed/likely/potential)
06Automated Privacy Masking for sensitive data (AWS keys, JWTs, secrets)
