"Unreal Engine" matching MCP servers:

A code understanding and generation system designed for Galacean Engine that helps LLMs comprehend the engine architecture and generate high-quality engine code based on user intent.

A TypeScript MCP server that lets AI assistants interact with the Godot 4.x game engine: not just editing files, but playing the game.

Provides coding agents with persistent, evidence-backed project memory and knowledge across sessions, using a structured memory tree and local knowledge base.

Enables agents to generate typed client code from OpenAPI, GraphQL, Protobuf, and SQL specs using MetaEngine's code-generation platform.

Turn the AI you already use into a game studio. Describe a game and Claude or Cursor builds it as a real 3D browser game: playable immediately, hosted on its own link, shareable with anyone. No engine, no build step, no code knowledge, nothing to export. Your AI ships the whole thing: it pulls free 3D assets, rigs characters, and playtests its own build until the game works. Free to start.

A local project intelligence, batch coding, and self-learning engine for Codex that enables task-level batch coding, reduces token usage, and automatically learns skills.

Enables AI agents to build games with the Cocos4 engine by providing high-level scaffolding tools and transparently proxying native MCP tools.

MCP servers that let Claude Code delegate to other coding CLIs (Copilot, Codex, Gemini) for cross-model second opinions, structured reviews, and multi-engine consultations.

AI-native open-source 2D game engine whose MCP server exposes every editor operation to coding agents as typed commands, so an agent can build, run, and verify a game inside the editor. It runs locally on macOS, Windows, and Linux with no cloud service or API key.

An autonomous MCP server for AI-powered software generation using Gemini and Groq, with features like dual-engine strategy, visual inspection, and QA sentinel.

Connects AI assistants to a local Codex engine for performing deep, project-level code reviews and automated refactoring. It enables context-aware bug fixes and multi-file analysis through a standardized bridge between modern AI clients and local development environments.

self-hosted development harness (Go engine + CLI + desktop, Linux first) · brief → requirements → spec → plan → build → review → release · verdicts are exit codes, never model opinions · local models first (llama.cpp, vLLM) beside any OpenAI-compatible or Anthropic endpoint · operable by humans or by other agents over MCP with recorded, attributed decisions