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Unity MCP Server: AI-Driven Innovation & Scalability - MCP Implementation

Unity MCP Server: AI-Driven Innovation & Scalability

Unity MCP Server seamlessly integrates Smithery.ai, empowering next-gen real-time 3D workflows with AI-driven innovation and unmatched scalability. Elevate creativity, boost productivity.

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66% of users reported increased productivity after just one week

About Unity MCP Server

What is Unity MCP Server: AI-Driven Innovation & Scalability?

Unity MCP Server serves as a bridge between AI assistants (e.g., Cursor AI, Smithery) and Unity projects, enabling seamless interaction through the Model Context Protocol (MCP). It operates in Unity’s batch mode, eliminating the need for the Editor GUI while providing specialized tools for project analysis, code validation, and automated workflows. This server empowers developers to leverage AI-driven insights for faster, more scalable game development.

How to Use Unity MCP Server: AI-Driven Innovation & Scalability?

Getting started involves three core steps: installation, configuration, and integration. First, install Node.js 16+ and Unity 2022.3 LTS. Clone the repository, install dependencies, and build the project. Configure settings in config/default.json, specifying paths, security rules, and logging preferences. Launch the server via npm start or Docker. For AI tooling, connect to the server using platforms like Smithery.ai by following their documentation for endpoint setup.

Unity MCP Server Features

Key Features of Unity MCP Server: AI-Driven Innovation & Scalability?

  • AI-Enhanced Workflows: Tools like analyze_cs_code and run_tests let AI assistants detect errors and automate test execution without manual intervention.
  • Project Intelligence: Parse scenes, prefabs, and YAML files; search codebases for patterns; and generate scripts from templates to accelerate development.
  • Security-first Design:_restrict file access via allowedPaths and block dangerous commands to prevent accidental data loss._
  • Scalability: Docker support enables containerized deployment, making it easy to integrate into CI/CD pipelines or cloud environments.

Use Cases of Unity MCP Server: AI-Driven Innovation & Scalability?

Common scenarios include:

  • Automated Code Audits: Use AI to scan C# scripts for compliance with coding standards or detect deprecated APIs.
  • Scene Analysis: Query GameObject hierarchies or component configurations to optimize performance without opening the Unity Editor.
  • AI-Powered Script Generation: Let AI draft boilerplate code (e.g., monobehaviors) based on templates, reducing repetitive tasks.
  • Continuous Testing: Run unit tests in batch mode across multiple build variants to ensure stability before deployment.

Unity MCP Server FAQ

FAQ from Unity MCP Server: AI-Driven Innovation & Scalability?

Q: Does the server require Unity Editor installed?
Yes, Unity 2022.3 LTS or newer is mandatory. The server interacts with the Unity engine via batch mode, not the Editor GUI itself.

Q: Can I extend the available tools?
Absolutely! The modular design allows developers to add custom tools by following the MCP specification—ideal for team-specific workflows.

Q: How is security enforced?
The security config block restricts file access to Unity project directories and blocks dangerous commands like rm to prevent accidental damage.

Q: Is Docker the recommended deployment method?
For production environments, yes. The provided Dockerfile simplifies scaling and ensures consistent runtime across platforms.

Content

Unity MCP Server

A Model Context Protocol (MCP) server for Unity game development, enabling AI assistants to interact with Unity projects through a set of specialized tools.

Overview

The Unity MCP Server provides a bridge between AI assistants (like Cursor AI or Claude) and Unity projects. It offers tools for:

  • Reading and searching files in Unity projects
  • Parsing Unity scenes and prefabs
  • Analyzing C# code and detecting errors
  • Running automated tests in batch mode
  • Generating scripts from templates
  • Validating shaders
  • And more...

The server is designed to run with Unity in batch mode, allowing for operations that require the Unity engine without needing the Editor GUI.

Installation

Prerequisites

  • Node.js 16+
  • Unity 2022.3 LTS or newer (recommended)

Setup

  1. Clone this repository:

    git clone https://github.com/GrandMasterK414/unity-smithery-mcp.git

cd unity-smithery-mcp
  1. Install dependencies:

    npm install

  2. Build the project:

    npm run build

Configuration

The server uses the config package for configuration. Edit config/default.json to adjust settings:

{
  "server": {
    "port": 3000,
    "host": "0.0.0.0"
  },
  "unity": {
    "unityPath": "/path/to/Unity/Editor/Unity",
    "projectPath": "/path/to/your/unity/project",
    "timeoutMs": 60000,
    "batchMode": true
  },
  "logging": {
    "level": "info",
    "file": "logs/mcp-server.log",
    "maxSize": 5242880,
    "maxFiles": 5
  },
  "security": {
    "allowedPaths": ["Assets", "ProjectSettings", "Packages"],
    "disallowedCommands": ["rm", "del", "format", "shutdown"]
  }
}

Usage

Starting the Server

npm start

To run in Smithery mode:

npm run smithery:start

Using with AI Assistants

This server is primarily designed to work with AI assistants through tools defined in the MCP specification. For Cursor or Smithery.ai integration, follow their documentation to connect to this MCP server.

Available Tools

Tool Name Description
open_file Reads the contents of a file in the Unity project
search_files Searches for text patterns in project files
list_assets Lists project assets of a certain type or in a folder
get_project_info Gets high-level information about the Unity project
parse_scene Parses a Unity scene file to extract game objects hierarchy
parse_prefab Parses a Unity prefab file to extract components
find_in_scenes Finds scenes containing a specific GameObject or component
analyze_cs_code Compiles C# code to find compiler errors
find_references Finds references to a C# class or method
generate_script Creates a new C# script file from a template
validate_shader Compiles a shader and reports errors
inspect_yaml Inspects a YAML asset file
run_tests Runs Unity's automated tests in batch mode
simulate_playmode Experimental - runs a scene in play mode for a short duration

Docker Support

A Dockerfile is included for containerized deployment. Build the Docker image:

docker build -t unity-mcp-server .

Run the container:

docker run -p 3000:3000 -v /path/to/unity/project:/project unity-mcp-server

Smithery.ai Integration

This server can be deployed on Smithery.ai using the provided smithery.yaml configuration.

License

MIT

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

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