Unity-AI Bridge MCP server for AI agents

Unity-MCP is a bridge that enables AI assistants to interact with Unity game environments through the Model Context Protocol (MCP). It allows for AI-assisted game development, automated testing, scene analysis, and runtime debugging by providing a standardized interface between AI systems and Unity.

Installation

Prerequisites

Before installing Unity-MCP, ensure you have:

Node.js installed
A running AILogger instance (default: http://localhost:3030)

Basic Installation

Follow these steps to install Unity-MCP:

Clone the repository:

git clone https://github.com/TSavo/Unity-MCP.git
cd Unity-MCP

Install dependencies:
```
npm install
```
Build the project:
```
npm run build
```
Start the MCP STDIO client:
```
npm start
```

Testing the Installation

You can verify your installation by running the tests:

# Run all tests
npm test

# Run only unit tests
npm run test:unit

# Run only e2e tests
npm run test:e2e

# Run tests with a specific pattern
npm test -- --testNamePattern="should return the server manifest"

Usage

Architecture Overview

Unity-MCP uses a simplified architecture:

AI Assistant ↔ Unity-MCP STDIO Client ↔ Unity Client ↔ AILogger

The system uses AILogger for persistence, allowing commands to be sent to Unity and results to be stored for later retrieval.

Connecting to AI Assistants

To connect Unity-MCP to an AI assistant, create an MCP configuration file:

{
  "mcpServers": {
    "unity-ai-bridge": {
      "url": "http://localhost:8080/sse"
    }
  }
}

Place this file in the appropriate location for your AI assistant (e.g., the Claude Desktop app's configuration directory).

Available Tools

Unity-MCP provides several tools for interacting with Unity:

execute_code: Execute C# code directly in Unity
query: Access objects, properties, and methods using dot notation
get_logs: Retrieve logs from AILogger
get_log_by_name: Retrieve a specific log from AILogger

Executing Code in Unity

You can execute C# code in Unity using the execute_code tool:

JSON-RPC Request

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "execute_code",
    "arguments": {
      "code": "Debug.Log(\"Hello from Unity!\"); return GameObject.FindObjectsOfType<GameObject>().Length;",
      "timeout": 5000
    }
  }
}

JSON-RPC Response

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "{\"status\":\"success\",\"logName\":\"unity-execute-1712534400000\",\"result\":{\"success\":true,\"result\":42,\"logs\":[\"Hello from Unity!\"],\"executionTime\":123}}"
      }
    ]
  }
}

Querying Unity Objects

The query tool allows accessing Unity objects using dot notation:

JSON-RPC Request

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "query",
    "arguments": {
      "query": "Camera.main.transform.position",
      "timeout": 5000
    }
  }
}

JSON-RPC Response

{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "{\"status\":\"success\",\"logName\":\"unity-query-1712534400000\",\"result\":{\"success\":true,\"result\":{\"x\":0,\"y\":1,\"z\":-10},\"executionTime\":45}}"
      }
    ]
  }
}

Common Usage Examples

Once set up, you can instruct your AI assistant to perform Unity tasks such as:

Execute C# code: GameObject.Find("Player").transform.position = new Vector3(0, 1, 0);
Inspect game objects and their properties
Analyze scene hierarchies
Modify game state during runtime
Run and retrieve test results

Deployment Options

Unity-MCP offers several deployment options:

Unity Editor Extension: Persists beyond game execution cycles
Docker Container: A containerized version communicating with Unity over the network
NPX Package: Can be installed and run via NPX

How to install this MCP server

For Claude Code

To add this MCP server to Claude Code, run this command in your terminal:

claude mcp add-json "unity-ai-bridge" '{"url":"http://localhost:8080/sse"}'

See the official Claude Code MCP documentation for more details.

For Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > Tools & Integrations and click "New MCP Server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "unity-ai-bridge": {
            "url": "http://localhost:8080/sse"
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explicitly ask the agent to use the tool by mentioning the tool name and describing what the function does.

For Claude Desktop

To add this MCP server to Claude Desktop:

1. Find your configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

2. Add this to your configuration file:

{
    "mcpServers": {
        "unity-ai-bridge": {
            "url": "http://localhost:8080/sse"
        }
    }
}

3. Restart Claude Desktop for the changes to take effect