A zero-dependency, in-memory graph database providing a memory layer for local AI agents with AST-based dependency tracking and Token-Level Provenance via Source Maps.
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# MCP Context Graph A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP). ## Why This Tool? AI coding assistants often struggle with large codebases. They either: - Read entire files (expensive, hits context limits) - Grep for text (misses semantic relationships) - Lose track of where functions are called from **MCP Context Graph solves this by building a semantic graph of your codebase** that the AI can query efficiently. ### Key Differentiators | Feature | Benefit | |---------|---------| | **Token-Level Source Maps** | Stores minified signatures but can expand to exact original source with character-accurate mapping | | **Semantic Call Graph** | "Who calls this function?" answered in milliseconds, not by reading every file | | **Polyglot Engine** | Python, TypeScript, JavaScript parsed with tree-sitter grammars | | **Zero Install** | Run instantly with `uvx` - no pip install, no dependencies to manage | | **MCP Native** | Built for AI agents - exposes tools through Model Context Protocol | | **Smart Exclusions** | Respects `.gitignore`, skips `node_modules`, `.venv`, `__pycache__` automatically | | **Lazy Ingestion** | Files indexed on-demand, auto-refreshed when modified | ### How It Works ``` Your Codebase MCP Context Graph AI Agent | | | | ──── tree-sitter parse ────> | | | | | | <── minified signatures ──── | | | + source maps | | | | | | | <── find_callers("fn") ── | | | ──> [caller1, caller2] ───> | | …
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