Real Time Filesystem Monitoring With Automatic Dependency Graph Updates

via “real-time code graph synchronization with file watching”

An MCP server plus a CLI tool that indexes local code into a graph database to provide context to AI assistants.

Unique: Integrates file system watching with the JobManager to provide real-time graph synchronization with debouncing and status tracking. Enables AI assistants to work with current code context through MCP without requiring manual re-indexing, bridging the gap between development and AI context freshness.

vs others: More responsive than periodic re-indexing (Sourcegraph, Tabnine) because it updates immediately on file changes; more efficient than naive per-file updates because debouncing batches rapid changes.

via “watch mode with auto-update hooks for continuous graph synchronization”

Local knowledge graph for Claude Code. Builds a persistent map of your codebase so Claude reads only what matters — 6.8× fewer tokens on reviews and up to 49× on daily coding tasks.

Unique: Implements filesystem-level watch mode with git hook integration (diagram 4) that automatically triggers incremental graph updates without manual intervention. The system uses SHA-256 change detection to identify modified files and re-parses only those files, keeping the graph synchronized in real-time.

vs others: More convenient than manual graph rebuild commands because it runs continuously in the background and integrates with git workflows, ensuring the graph is always current without developer action.

via “real-time filesystem monitoring with automatic dependency graph updates”

** - Analyzes your codebase identifying important files based on dependency relationships. Generates diagrams and importance scores per file, helping AI assistants understand the codebase. Automatically parses popular programming languages, Python, Lua, C, C++, Rust, Zig.

Unique: Integrates filesystem monitoring directly into the MCP server lifecycle, automatically updating the dependency graph on file system events rather than requiring explicit refresh calls. Uses incremental re-analysis (only affected files) rather than full re-scans.

vs others: More responsive than polling-based approaches but less precise than AST-aware change detection; suitable for AI assistants that need current codebase state without manual refresh