lore-mcp

LORE is a plugin-based code archaeology engine for TypeScript projects. It parses your AST, maps dependencies, detects circular deps, tracks async chains, scores type safety, and feeds architectural intelligence to AI coding assistants through the Model Context Protocol (MCP).

Built by EliotShift · Battle-tested on 16 real-world projects · 100% pass rate.

Why LORE?

AI coding assistants write fast, but they lack architectural memory. They can't remember:

• Which files are tightly coupled

• Where circular dependencies live

• Which types are spreading across boundaries

• Which files change too often (hotspots)

• What the architectural layers are

LORE solves this by giving your AI assistant a deep understanding of your codebase architecture — through CLI analysis and MCP server integration.

Features

13 Analyzers

MCP Integration (8 Tools + 3 Resources)

Expose LORE to Claude Desktop, VS Code, Cursor, or any MCP client:

CLI Commands

lore [path]                   Analyze project (default: cwd)
lore analyze [path]           Explicit analysis
lore init                     Extract architectural decisions
lore status                   View decisions by category
lore diff                     Diff against saved baseline
lore doctor                   Environment + tooling check
lore doctor --fix             Auto-fix project setup
lore ignore                   List/manage ignore patterns
lore watch                   Watch + re-analyze on change
lore mcp inspect             Inspect MCP server setup
lore mcp config              Claude Desktop config snippet
lore version                  Show version

Documentation

Website: eliotshift.github.io/lore-mcp

Quick Start

Install

npm install -g lore-mcp

CLI Usage

# Analyze current project
lore

# Analyze a specific project
lore ./my-typescript-project

# Check environment and auto-fix
lore doctor --fix

# Watch for changes
lore watch --filter src/

# Diff from last baseline
lore diff

MCP Integration (Claude Desktop)

Add this to your Claude Desktop config:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "lore": {
      "command": "npx",
      "args": ["-y", "lore-mcp"]
    }
  }
}

Or if installed globally:

{
  "mcpServers": {
    "lore": {
      "command": "lore",
      "args": ["mcp"]
    }
  }
}

Restart Claude Desktop, then ask:

"Analyze my project architecture" → LORE runs analyze_architecture "What are the circular dependencies?" → LORE runs get-violations "Which files are hotspots?" → LORE runs get-hotspots "What are the AI recommendations?" → LORE runs get-recommendations

Architecture

lore-mcp/
├── src/
│   ├── algorithm/          # LoreGraph, AST parser, async chain builder, type tracker
│   ├── algorithms/         # AI recommendations, hotspot analysis, entropy, scoring
│   ├── analyzer/           # Dependency parsers, circular deps, direction, imports
│   ├── commands/           # CLI: doctor, diff, ignore, init, status, watch
│   ├── core/               # Plugin system, pipeline runner, graph engine
│   ├── lib/                # Hidden coupling, gaps, middleware chain, ontology
│   ├── mcp/                # MCP server + architecture bridge
│   ├── output/             # Formatter, markdown, SARIF, logger
│   ├── plugins/built-in/   # 13 built-in analysis plugins
│   ├── storage/            # Cache and decision store
│   ├── types/              # TypeScript type definitions
│   ├── cli.ts              # CLI entry point
│   └── index.ts            # MCP server entry point
├── package.json
└── tsconfig.json

Plugin System

LORE uses a plugin-based architecture — every analyzer is a plugin:

interface LorePlugin {
  name: string;
  version: string;
  analyze(context: AnalysisContext): Promise<PluginResult>;
}

Built-in plugins include: circular-deps, coupling-matrix, dep-direction, entropy, gaps, hidden-coupling, hotspot, import-impact, middleware-chain, breaking-changes, type-safety, tooling-config, ai-recommendations.

How It Works

1. Parse — LORE parses all .ts/.tsx files using a hybrid ts-morph + regex parser

2. Build Graph — Constructs a dependency graph with typed edges (import, type-ref, decorator, async-chain, implements, extends)

3. Run Plugins — 13 analyzers run in parallel through the plugin pipeline

4. Score — Computes health scores (type safety, tooling, architecture, overall 0–100)

5. Recommend — AI engine generates prioritized suggestions (P0 critical → P3 nice-to-have)

6. Serve — Results available via CLI output, MCP tools, or SARIF format

Validation

All 16 test projects: 100% pass rate, zero crashes.

Requirements

• Node.js >= 18.0.0

• TypeScript project (analyzes .ts and .tsx files)

• Git (optional, for hotspot analysis)

• ripgrep (optional, for faster file discovery)

Tech Stack

License

MIT © 2025 EliotShift