OpenKrak

Dorchester Engine MCP Server — static analysis intelligence for AI coding assistants.

OpenKrak runs the Dorchester engine on your repository before the model reads a single file. The result is a structured brief — hotspot rankings, dependency graph, blast radius, security findings — delivered directly to the model's context. No hallucinated file structure. No wasted tokens on the wrong files.

Supported: Claude Code · Claude Desktop · Cursor · Windsurf

How It Works

Repository
    │
    ▼
DeepStrike          — File discovery, AST parse, symbol extraction, dependency resolution
    │
    ▼
Hotspot Registry    — Coupling scores, complexity, git change frequency, god_object detection
    │
    ▼
Correlation Engine  — Finding classification, noise reduction (Rule 1–4), impact chains
    │
    ▼
Blast Radius        — Cascade mapping, affected files and modules, risk scoring
    │
    ▼
Execution Gate      — Safety checks, circular dependency detection, blocker identification
    │
    ▼
Mahadata            — Structured brief with source snippets delivered to the model

All analysis runs locally. No source code leaves your machine.

Related MCP server: mcp-code-indexer

Quickstart — Claude Code

cd /your/repo
npx openkrak-init

That's it. openkrak-init drops two files into your repo root:

• .mcp.json — registers OpenKrak as a Claude Code MCP server

• CLAUDE.md — injects mandatory instructions into Claude's system prompt every session

Claude Code reads CLAUDE.md as user-level instructions. It cannot ignore them. OpenKrak is invoked automatically before any file is accessed.

Manual Setup (Claude Desktop / Cursor / Windsurf)

Add to your MCP config:

{
  "mcpServers": {
    "openkrak": {
      "command": "npx",
      "args": ["openkrak-mcp@1.3.0"]
    }
  }
}

Requires Node.js ≥ 18.

Tools

10 tools available as of v1.3.0.

Output Format

╔══ DORCHESTER ENGINE — SCAN ══════════════════════════════════════╗
║ Repo      your-project  |  42f  5840loc  TypeScript  fw:next@15.0
╠══ HOTSPOT REGISTRY (42 files ranked) ════════════════════════════╣
║  1. [CRITICAL ] auth-context.tsx  score:0.812  god_object,high_coupling
║  2. [HIGH     ] api-router.ts     score:0.641  high_coupling
╠══ SOURCE PREVIEW — top 3 critical files (first 60–80 lines each) ╣
║  ── auth-context.tsx  [score:0.812]
...

The model receives source snippets of the top 3 critical files inline. It does not need to open those files separately.

Benchmark

Tested on a 26-file TypeScript / Next.js repo (ChesterMath):

Without OpenKrak: a model analyzing the same repo by reading files sequentially consumes 60,000+ tokens before forming a structural understanding. OpenKrak delivers equivalent context in ~13,000 tokens — approximately 4–5× reduction.

Token budget is proportional to repo size. Larger repos produce proportionally larger briefs, not arbitrarily capped output.

Language Support

License

Free tier is active by default — no account required.

To activate Pro, set OPENKRAK_KEY in your environment or MCP config:

{
  "mcpServers": {
    "openkrak": {
      "command": "npx",
      "args": ["openkrak-mcp@1.3.0"],
      "env": {
        "OPENKRAK_KEY": "your-license-key"
      }
    }
  }
}

License keys: openkrak-web.vercel.app

Notes

• Static analysis only. No AI inference in the pipeline.

• Anonymous telemetry: query count, tool name, error events. No source code or file contents transmitted.

• License validation requires a network call on each invocation.

MIT License — © 2026 Faiz Hamizan / Challanger Absolute Advance
github.com/FrnzJulianBergmann/openkrak