Repository Intelligence Engine

A TypeScript repository indexing engine that builds a structural model of a codebase — symbols, imports/exports, and references — and answers precise navigation questions about it directly, instead of re-discovering structure by grepping every session.

The problem

AI coding agents start every session with zero structural memory of a repo. Answering "how does login work?" means an iterative grep → open → grep → open cycle, repeated from scratch each time. This engine replaces that with direct queries against a pre-built index.

What it does

The engine parses a TypeScript repo with the TypeScript Compiler API and stores a structural model in SQLite. Five queries run over that index:

Query	Answers
find_module(name)	Which file(s) define this symbol?
find_related_files(file)	What does this file import, and what imports it?
find_symbol_references(symbol)	Everywhere this symbol is used
dependency_path(a, b)	Is there an import path between two symbols, and what is it?
reindex(path?)	Rebuild the index

The engine/ functions are callable directly (CLI, tests) — the engine is the product. It also supports Claude Code and any other MCP-compatible client through an integrated MCP server.

Status

Early scaffold. Core is being built in the order in the project plan: indexer → storage + basic queries → references → dependency_path/reindex → MCP server → benchmark harness → docs.

Benchmark

Before/after table lands here once the harness (step 6) runs against a real repo — median file-reads and tool-calls per task, baseline vs. MCP-assisted.

Development

npm install
npm run build           # tsc -> dist/
npm run index -- ./tsconfig.json repo-index.db   # index a repo
npm run mcp             # start the MCP server (stdio)
npm test                # vitest