miscusi-peek/cheatengine-mcp-bridge

Connect your favorite AI agents directly to Cheat Engine via MCP. Automate reverse engineering, pointer scanning, and memory analysis using natural language.

What it solves

Manual memory analysis, reverse engineering, and game modding typically require days of tedious work scanning hex dumps and tracing pointer chains. This project eliminates that manual effort by allowing users to interact with a program's memory using natural language via an AI agent.

How it works

It implements a bridge between an AI agent (like Claude, Cursor, or Copilot) and Cheat Engine using the Model Context Protocol (MCP).

  1. AI Agent: Sends requests via JSON-RPC over stdio.
  2. MCP Server: A Python server (mcp_cheatengine.py) that translates AI requests into commands.
  3. Communication: The server communicates with Cheat Engine via Windows Named Pipes (or a TCP relay for remote/WSL access).
  4. Cheat Engine: A Lua script (ce_mcp_bridge.lua) runs inside Cheat Engine to execute the memory operations and return results to the AI.

Who it’s for

Reverse engineers, security auditors, modders, and developers who want to accelerate the process of analyzing program memory, finding specific opcodes, or creating game bots and trainers.

Highlights

  • Extensive Toolset: Provides ~180 MCP tools covering memory reading/writing, pointer chain resolution, AOB scanning, and code disassembly.
  • Advanced Analysis: Supports C++ object identification via RTTI and automatic structure dissection.
  • Deep Debugging: Integrates hardware breakpoints and Ring -1 invisible tracing via DBVM.
  • Process Control: Ability to open, create, pause, and resume target processes.
  • Code Injection: Supports DLL injection and compiling C code into injected shellcode.
  • TCP Relay: Allows the MCP server to run on a different machine or environment (like Linux/WSL) while Cheat Engine runs on Windows.

Related

  • Project
    IvanMurzak/Unity-MCP

    Unity‑MCP is a Unity package that connects LLM agents (Claude, Codex, etc.) to the Unity Editor and to compiled games. It provides 70+ built‑in “tools” (asset management, scene manipulation, scripting, testing, profiling, etc.) that the AI can call, enabling natural‑language creation, debugging, and runtime AI behaviour. Install via a UnityPackage, OpenUPM, or the `unity-mcp-cli`; configure an agent, auto‑generate a skill set, and start issuing commands like “create three cubes in a circle”. Extensible with optional skill packs for Animation, Cinemachine, Navigation, and more. MIT‑licensed.

  • Project
    zhizhuodemao/js-reverse-mcp

    js‑reverse‑mcp is a Node.js MCP server that lets LLM agents debug and reverse‑engineer JavaScript in Chrome. It wraps DevTools data (scripts, breakpoints, network, WebSocket, storage) into 24 concise tools designed for AI‑driven reasoning, and includes optional stealth layers (Patchright fork and CloakBrowser) for anti‑bot sites.

  • Project
    CoplayDev/unity-mcp

    MCP for Unity is an MIT‑licensed Unity package that connects LLMs (Claude, Codex, Gemini, local models, etc.) to the Unity Editor via the Model Context Protocol. It lets you create scenes, edit C# scripts, manage assets, run tests, and build games using natural‑language prompts from any MCP client. Install via Unity’s Package Manager, configure detected AI clients, and start issuing commands like “add a cube with a Rigidbody.” The project includes 50 ready‑made tool actions, multi‑instance routing, remote server support, and extensive documentation, with community channels on Discord and GitHub.

  • Project
    AnkleBreaker-Studio/unity-mcp-server

    AnkleBreaker Unity MCP is a Model Context Protocol server that lets AI assistants control multiple Unity editors simultaneously — building scenes, running tests, managing multiplayer Play Mode, and inspecting performance — with per-editor queues, action history, and undo support. It's built for teams coordinating several agents across projects and demanding Unity workflows.

  • Project
    apify/mcpc

    mcpc is a universal command‑line client for the Model Context Protocol (MCP). It maps every MCP operation—tools, prompts, resources, tasks, skills, OAuth authentication, and even experimental x402 payments—into intuitive shell commands. Designed for AI agents and developers, it lets you inspect, debug, and script MCP workflows using standard Unix tools (e.g., jq, xargs). Features include persistent named sessions, progressive tool discovery via `grep`, JSON‑only output mode, secure OAuth 2.1 storage (OS keychain or protected file), AI sandboxing, and lightweight cross‑platform support.