Summary

This project provides an AI-assisted toolkit for JavaScript analysis and security, targeting engineers and researchers. It offers a comprehensive suite of tools for browser automation, debugging, network monitoring, code analysis, and more, enabling deep insights into web applications and potential vulnerabilities.

How It Works

The system operates as an MCP (Model Context Protocol) server, dynamically registering over 200 specialized tools. It employs a modular architecture with domain self-discovery and progressive tool profiles (search, minimal, workflow, full) to optimize startup performance and resource usage. Key innovations include LLM-powered code understanding, AI-generated JavaScript hooks, and advanced browser automation with human behavior simulation.

Quick Start & Requirements

• Primary install / run command: npx @jshookmcp/jshook (recommended).
• Non-default prerequisites and dependencies: Node.js >= 20, npm/pnpm. LLM API keys (OpenAI/Anthropic) are required for AI features. Building from source requires pnpm install and pnpm build.
• Links: Official documentation is available at docs/index.md and docs/guide/getting-started.md.

Highlighted Details

• Features 244 built-in tools across 16 domains, extensible via plugins and workflows.
• Integrates LLM-powered code deobfuscation, crypto detection, and semantic analysis.
• Offers advanced browser automation, including stealth injection, human behavior simulation, and CAPTCHA solving.
• Supports WebAssembly analysis, binary encoding inspection, and bridges to native security tools (Ghidra, IDA Pro, Burp Suite).
• Employs progressive discovery and tiered tool profiles for efficient loading and resource management.

Maintenance & Community

The provided README does not contain specific details regarding maintainers, community channels (e.g., Discord, Slack), or project roadmap.

Licensing & Compatibility

The license type is not explicitly stated in the provided README content. Compatibility for commercial use or closed-source linking cannot be determined without this information.

Limitations & Caveats

AI-driven features necessitate the configuration of external LLM API keys. Certain advanced integrations, such as bridges to native analysis tools, require separate installation and setup by the user. The absence of explicit licensing information poses a potential adoption blocker.