IvanMurzak/Unity-MCP

✨ AI Game Developer — Unity SKILLS, MCP

                                

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Unity MCP is an AI-powered game development assistant for Editor & Runtime. Connect Claude, Cursor, & Windsurf to Unity via MCP. Automate workflows, generate code, and enable AI within your games.

Unlike other tools, this plugin works inside your compiled game, allowing for real-time AI debugging and player-AI interaction.

💬 Join our Discord Server - Ask questions, showcase your work, and connect with other developers!

• ✔️ AI agents - Use the best agents from Anthropic, OpenAI, Microsoft, or any other provider with no vendor lock-in
• ✔️ Tools - A wide range of default MCP Tools for operating in Unity Editor
• ✔️ Skills - Generate skills for AI based on operating system, Unity version, plugins in the project
• ✔️ Code and Tests - Develop game mechanics and test them with AI agents
• ✔️ Runtime (in-game) - Use LLMs directly inside your compiled game for dynamic NPC behavior or debugging
• ✔️ Debug support - Let AI debug and fix the problems in a project
• ✔️ Natural conversation - Chat with AI like you would with a human
• ✔️ Flexible deployment - Works locally (stdio) and remotely (http) via configuration
• ✔️ Extensible - Create custom Tools in your project code

# 1. Install unity-mcp-cli
npm install -g unity-mcp-cli

# 2. Install "AI Game Developer" in Unity project
unity-mcp-cli install-plugin ./MyUnityProject

# 3. Sign in to ai-game.dev (opens your browser — OAuth device flow)
unity-mcp-cli login

# 4. Open Unity project (auto-connects and generates skills)
unity-mcp-cli open ./MyUnityProject

Get up and running in three steps:

1. Install plugin — download the .unitypackage installer or run openupm add com.ivanmurzak.unity.mcp

   Alternative: npx unity-mcp-cli install-plugin ./MyUnityProject — see CLI documentation

2. Pick an AI agent — Claude Code, Claude Desktop, GitHub Copilot, Cursor, or any other
3. Setup AI agent — open Window/AI Game Developer in Unity and click Auto-generate skills (recommended) or Configure MCP

   Alternative: npx unity-mcp-cli setup-skills claude-code ./MyUnityProject — see CLI documentation

That's it. Ask your AI "Create 3 cubes in a circle with radius 2" and watch it happen. ✨

The plugin ships with 70+ built-in tools across four categories. Each tool brings AI skill. All tools are available immediately after installation — no extra configuration required. See docs/default-mcp-tools.md for the full reference with detailed descriptions.

🧰 Browse the full MCP tools registry online: ai-game.dev/docs/tools

Install extensions when need more tools or create your own tools.

• Quick Start
• Skills and Tools Reference
  • Install Additional Skills and Tools
• Contents
  • More Documentation
• Installation
  • Step 1: Install Unity MCP Plugin
    • Option 1 - Installer
    • Option 2 - CLI (recommended)
  • Step 2: Install AI agent
  • Step 3: Configure AI agent
    • Automatic configuration
    • Manual configuration
      • Command line configuration
• AI Workflow Examples
  • Advanced Features for LLM
    • Core Capabilities
    • Reflection-Powered Features
• Customize Tools
  • Add custom Tool
  • Add custom MCP Prompt
• Runtime usage (in-game)
  • Sample: AI powered Chess game bot
  • Why runtime usage is needed?
• Unity MCP Server setup
  • Variables
  • Plugin Variables
  • Docker 📦
    • streamableHttp Transport
    • stdio Transport
    • Custom port
  • Binary executable
• How Unity MCP Architecture Works
  • What is MCP
  • What is AI agent
  • What is MCP Server
  • What is MCP Tool
    • When to use Tool
  • What is MCP Resource
    • When to use MCP Resource
  • What is MCP Prompt
    • When to use MCP Prompt
• Contribution 💙💛

• ⬇️ Download Installer
• 📂 Import installer into Unity project

  • You can double-click on the file - Unity will open it automatically
  • OR: Open Unity Editor first, then click on Assets/Import Package/Custom Package, and choose the file

Install the plugin via unity-mcp-cli — no Unity Editor needed:

# 1.1 Install unity-mcp-cli                                #  ┌────────────────────┐
npm install -g unity-mcp-cli                               #  │ Available AI agent │
                                                           #  ├────────────────────┤
# 1.2 (Optional) Install Unity                             #  │ antigravity        │
unity-mcp-cli install-unity                                #  │ claude-code        │
                                                           #  │ claude-desktop     │
# 1.3 (Optional) Create Unity project                      #  │ cline              │
unity-mcp-cli create-project ./MyUnityProject              #  │ codex              │
                                                           #  │ cursor             │
# 2. Install "AI Game Developer" in Unity project          #  │ gemini             │
unity-mcp-cli install-plugin ./MyUnityProject              #  │ github-copilot-cli │
                                                           #  │ kilo-code          │
# 3. Sign in to ai-game.dev (OAuth device flow)            #  │ open-code          │
unity-mcp-cli login                                        #  │ rider-junie        │
                                                           #  │ unity-ai           │
# 4. Open Unity project (auto-connects and generates skills)  │ vs-copilot         │
unity-mcp-cli open ./MyUnityProject                        #  │ vscode-copilot     │
                                                           #  └────────────────────┘
# 5. Wait for Unity Editor to be ready
unity-mcp-cli wait-for-ready ./MyUnityProject

See full CLI documentation for all available commands.

Cloud sign-in & project pinning: unity-mcp-cli login runs the browser OAuth device flow and stores the credential in the shared machine store (~/.ai-game-dev/credentials.json) — there is no personal access token to paste. By default the AI-agent config that setup-mcp writes points at a per-project pinned endpoint (https://ai-game.dev/mcp/p/<pin>); pass --no-pin to use the shared https://ai-game.dev/mcp endpoint instead. Teams can distribute access with an enrollment code: unity-mcp-cli install-plugin --enroll <code>.

Choose a single AI agent you prefer - you don't need to install all of them. This will be your main chat window to communicate with the LLM.

• Claude Code (recommended)
• Claude Desktop
• GitHub Copilot in VS Code
• Antigravity
• Cursor
• Windsurf
• Any other supported

The AI Game Developer is quite universal, which is why you may use any AI agent you prefer - it will work as smoothly as any other. The only important requirement is that the AI agent must support Skills or dynamic MCP Tool updates.

• Open Unity project
• Open Window/AI Game Developer
• Option 1: Click Auto-generate Skills (recommended)
• Option 2: Click Configure Model Context Protocol (MCP)

If your MCP client is not in the list, use the raw JSON shown in the window to inject it into your MCP client. Read the instructions for your specific MCP client on how to do this.

If automatic configuration doesn't work for you for any reason, use the JSON from the AI Game Developer (Unity-MCP) window to configure any MCP Client manually.

Gemini CLI

gemini mcp add ai-game-developer <command>

Replace <command> from the table above

Claude Code CLI

claude mcp add ai-game-developer <command>

Replace <command> from the table above

GitHub Copilot CLI

copilot

/mcp add

Server name: ai-game-developer Server type: local Command: <command>

Replace <command> from the table above

Communicate with the AI (LLM) in your AI agent. Ask it to do anything you want. The better you describe your task or idea, the better it will perform the job.

Some AI agents allow you to choose different LLM models. Pay attention to this feature, as some models may work much better than others.

Example prompts:

Explain my scene hierarchy

Create 3 spheres on top of each other

Create metallic golden material and attach it to a new sphere gameObject

Make sure Agent mode is enabled if using VS Code with Copilot

The plugin shows an update popup at Editor startup when a newer version is available on OpenUPM. By default, each team member sees this popup until they individually click "Do not show again" (which is a per-user setting stored on their machine).

For multi-person Unity projects where one engineer owns plugin versioning, you can disable the popup for the entire team by opening Edit ▸ Project Settings ▸ AI Game Developer and enabling "Disable update notifications for the entire team". The setting is persisted to ProjectSettings/AI-Game-Developer-UpdateSettings.asset and only needs to be set once per project — commit that file and every team member who pulls the commit will have the popup suppressed.

The same toggle is also reachable via Tools ▸ AI Game Developer ▸ Updates ▸ Disable Update Notifications (Team) in the menu bar.

Unity MCP provides advanced tools that enable the LLM to work faster and more effectively, avoiding mistakes and self-correcting when errors occur. Everything is designed to achieve your goals efficiently.

• ✔️ Agent-ready tools - Find anything you need in 1-2 steps
• ✔️ Instant compilation - C# code compilation & execution using Roslyn for faster iteration
• ✔️ Full asset access - Read/write access to assets and C# scripts
• ✔️ Intelligent feedback - Well-described positive and negative feedback for proper issue understanding

• ✔️ Object references - Provide references to existing objects for instant C# code
• ✔️ Project data access - Get full access to entire project data in a readable format
• ✔️ Granular modifications - Populate & modify any piece of data in the project
• ✔️ Method discovery - Find any method in the entire codebase, including compiled DLL files
• ✔️ Method execution - Call any method in the entire codebase
• ✔️ Advanced parameters - Provide any property for method calls, even references to existing objects in memory
• ✔️ Live Unity API - Unity API instantly available - even when Unity changes, you get the fresh API
• ✔️ Self-documenting - Access human-readable descriptions of any class, method, or property via Description attributes

Unity MCP supports custom MCP Tool, MCP Resource, and MCP Prompt development by project owners. The MCP server takes data from the Unity MCP Plugin and exposes it to a client. Anyone in the MCP communication chain will receive information about new MCP features, which the LLM may decide to use at some point.

To add a custom Tool, you need:

1. A class with the AiToolType attribute
2. A method in the class with the AiTool attribute
3. Optional: Add a Description attribute to each method argument to help the LLM understand it
4. Optional: Use string? optional = null properties with ? and default values to mark them as optional for the LLM

Note that the line MainThread.Instance.Run(() => allows you to run code on the main thread, which is required for interacting with Unity's API. If you don't need this and running the tool in a background thread is acceptable, avoid using the main thread for efficiency purposes.

[AiToolType]
public class Tool_GameObject
{
    [AiTool
    (
        "MyCustomTask",
        Title = "Create a new GameObject"
    )]
    [Description("Explain here to LLM what is this, when it should be called.")]
    public string CustomTask
    (
        [Description("Explain to LLM what is this.")]
        string inputData
    )
    {
        // do anything in background thread

        return MainThread.Instance.Run(() =>
        {
            // do something in main thread if needed

            return $"[Success] Operation completed.";
        });
    }
}

MCP Prompt allows you to inject custom prompts into the conversation with the LLM. It supports two sender roles: User and Assistant. This is a quick way to instruct the LLM to perform specific tasks. You can generate prompts using custom data, providing lists or any other relevant information.

[AiPromptType]
public static class Prompt_ScriptingCode
{
    [AiPrompt(Name = "add-event-system", Role = Role.User)]
    [Description("Implement UnityEvent-based communication system between GameObjects.")]
    public string AddEventSystem()
    {
        return "Create event system using UnityEvents, UnityActions, or custom event delegates for decoupled communication between game systems and components.";
    }
}

Use Unity MCP in your game/app. Use Tools, Resources or Prompts. By default there are no tools, you would need to implement your custom.

// Build MCP plugin
var mcpPlugin = UnityMcpPluginRuntime.Initialize(builder =>
    {
        builder.WithConfig(config =>
        {
            config.Host = "http://localhost:8080";
            config.Token = "your-token";
        });
        // Automatically register all tools from the current assembly
        builder.WithToolsFromAssembly(Assembly.GetExecutingAssembly());
    })
    .Build();

await mcpPlugin.Connect(); // Start active connection with retry to the MCP server

await mcpPlugin.Disconnect(); // Stop active connection and close existed connection

There is a classic Chess game. Lets outsource to LLM the bot logic. Bot should do the turn using game rules.

[AiToolType]
public static class ChessGameAI
{
    [AiTool("chess-do-turn", Title = "Do the turn")]
    [Description("Do the turn in the chess game. Returns true if the turn was accepted, false otherwise.")]
    public static Task<bool> DoTurn(int figureId, Vector2Int position)
    {
        return MainThread.Instance.RunAsync(() => ChessGameController.Instance.DoTurn(figureId, position));
    }

    [AiTool("chess-get-board", Title = "Get the board")]
    [Description("Get the current state of the chess board.")]
    public static Task<BoardData> GetBoard()
    {
        return MainThread.Instance.RunAsync(() => ChessGameController.Instance.GetBoardData());
    }
}

There are many use cases, lets imagine you are working on a Chess game with bot. You may outsource the bot decision making to LLM by writing few lines of code.

Unity MCP Server supports many different launch options and Docker deployment. Both transport protocols are supported: streamableHttp and stdio. If you need to customize or deploy Unity MCP Server to a cloud, this section is for you. Read more...

Doesn't matter what launch option you choose, all of them support custom configuration using both Environment Variables and Command Line Arguments. It would work with default values, if you just need to launch it, don't waste your time for the variables. Just make sure Unity Plugin also has default values, especially the --port, they should be equal.

Command line args support also the option with a single - prefix (-port) and an option without prefix at all (port).

For analytics and authorization webhook variables (MCP_PLUGIN_WEBHOOK_*), see docs/mcp-server.md.

Choosing a transport: Use stdio when the MCP client launches the server binary directly (local use — this is the most common setup). Use streamableHttp when running the server as a standalone process or in Docker/cloud, and connecting over HTTP.

The Unity MCP Plugin reads the following environment variables (and command-line arguments) on startup to override values from the saved config file. Overrides are applied at runtime; on first run or when a new authentication token is generated, the overridden values are written to the config file. On subsequent runs, overrides are applied in memory but are not automatically saved. The exception is UNITY_MCP_TOOLS, which uses [JsonIgnore] and is never persisted — it is runtime-only.

Command-line args take precedence over environment variables. Both override the saved config file value. The short flags -url, -token, and -auth are aliases; the full -UNITY_MCP_* arg names are also accepted.

Example (CI/CD batch mode):

Unity.exe -batchmode -nographics \
  -UNITY_MCP_CLOUD_URL=http://localhost:8080 \
  -UNITY_MCP_KEEP_CONNECTED=true \
  -UNITY_MCP_AUTH_OPTION=token \
  -UNITY_MCP_TOKEN=my-secret-token

Make sure Docker is installed. And please make sure Docker Desktop is launched if you are at Windows operation system.

Read advanced Docker configuration instructions.

docker run -p 8080:8080 aigamedeveloper/mcp-server

{
  "mcpServers": {
    "ai-game-developer": {
      "url": "http://localhost:8080"
    }
  }
}

For using this variant, MCP Client should launch the MCP Server in the docker. It is achievable through the modified MCP Client configuration.

docker run -t -e MCP_PLUGIN_CLIENT_TRANSPORT=stdio -p 8080:8080 aigamedeveloper/mcp-server

{
  "mcpServers": {
    "ai-game-developer": {
      "command": "docker",
      "args": [
        "run",
        "-t",
        "-e",
        "MCP_PLUGIN_CLIENT_TRANSPORT=stdio",
        "-p",
        "8080:8080",
        "aigamedeveloper/mcp-server"
      ]
    }
  }
}

docker run -e MCP_PLUGIN_PORT=123 -p 123:123 aigamedeveloper/mcp-server

{
  "mcpServers": {
    "ai-game-developer": {
      "url": "http://localhost:123"
    }
  }
}

You may launch Unity MCP Server directly from a binary file. You would need to have a binary compiled specifically for your CPU architecture. Check GitHub Release Page, it contains pre-compiled binaries for all CPU architectures.

./gamedev-mcp-server --port 8080 --plugin-timeout 10000 --client-transport stdio

{
  "mcpServers": {
    "ai-game-developer": {
      "command": "<project>/Library/mcp-server/win-x64/gamedev-mcp-server.exe",
      "args": [
        "--port=8080",
        "--plugin-timeout=10000",
        "--client-transport=stdio"
      ]
    }
  }
}

Unity MCP serves as a bridge between LLMs and Unity. It exposes and explains Unity's tools to the LLM, which then understands the interface and utilizes the tools according to user requests.

Connect Unity MCP to LLM clients such as Claude or Cursor using the integrated AI Connector window. Custom clients are also supported.

The system is highly extensible - you can define custom MCP Tools, MCP Resource or MCP Prompt directly in your Unity project codebase, exposing new capabilities to AI or automation clients. This makes Unity MCP a flexible foundation for building advanced workflows, rapid prototyping, and integrating AI-driven features into your development process.

MCP - Model Context Protocol. In a few words, that is USB Type-C for AI, specifically for LLM (Large Language Model). It teaches LLM how to use external features. Such as Unity Engine in this case, or even your custom C# method in your code. Official documentation.

It is an application with a chat window. It may have smart agents to operate better, it may have embedded advanced MCP Tools. In general well done MCP Client is 50% of the AI success of executing a task. That is why it is very important to choose the best one for usage.

It is a bridge between MCP Client and "something else", in this particular case it is Unity Engine. This project includes MCP Server.

MCP Tool is a function or method that the LLM can call to interact with Unity. These tools act as the bridge between natural language requests and actual Unity operations. When you ask the AI to "create a cube" or "change material color," it uses MCP Tools to execute these actions.

Key characteristics:

• Executable functions that perform specific operations
• Typed parameters with descriptions to help the LLM understand what data to provide
• Return values that give feedback about the operation's success or failure
• Thread-aware - can run on main thread for Unity API calls or background thread for heavy processing

• Automate repetitive tasks - Create tools for common operations you do frequently
• Complex operations - Bundle multiple Unity API calls into a single, easy-to-use tool
• Project-specific workflows - Build tools that understand your project's specific structure and conventions
• Error-prone tasks - Create tools that include validation and error handling
• Custom game logic - Expose your game's systems to AI for dynamic content creation

Examples:

• Creating and configuring GameObjects with specific components
• Batch processing assets (textures, materials, prefabs)
• Setting up lighting and post-processing effects
• Generating level geometry or placing objects procedurally
• Configuring physics settings or collision layers

MCP Resource provides read-only access to data within your Unity project. Unlike MCP Tools that perform actions, Resources allow the LLM to inspect and understand your project's current state, assets, and configuration. Think of them as "sensors" that give the AI context about your project.

Key characteristics:

• Read-only access to project data and Unity objects
• Structured information presented in a format the LLM can understand
• Real-time data that reflects the current state of your project
• Contextual awareness helping the AI make informed decisions

• Project analysis - Let AI understand your project structure, assets, and organization
• Debugging assistance - Provide current state information for troubleshooting
• Intelligent suggestions - Give AI context to make better recommendations
• Documentation generation - Automatically create documentation based on project state
• Asset management - Help AI understand what assets are available and their properties

Examples:

• Exposing scene hierarchy and GameObject properties
• Listing available materials, textures, and their settings
• Showing script dependencies and component relationships
• Displaying current lighting setup and render pipeline configuration
• Providing information about audio sources, animations, and particle systems

MCP Prompt allows you to inject pre-defined prompts into the conversation with the LLM. These are smart templates that can provide context, instructions, or knowledge to guide the AI's behavior. Prompts can be static text or dynamically generated based on your project's current state.

Key characteristics:

• Contextual guidance that influences how the AI responds
• Role-based - can simulate different personas (User requests or Assistant knowledge)
• Dynamic content - can include real-time project data
• Reusable templates for common scenarios and workflows

• Provide domain knowledge - Share best practices and coding standards specific to your project
• Set coding conventions - Establish naming conventions, architecture patterns, and code style
• Give context about project structure - Explain how your project is organized and why
• Share workflow instructions - Provide step-by-step procedures for common tasks
• Inject specialized knowledge - Add information about specific Unity features, third-party assets, or custom systems

Examples:

• "Always use PascalCase for public methods and camelCase for private fields"
• "This project uses a custom event system located in Scripts/Events/"
• "When creating UI elements, always add them to the Canvas in Scene/UI/MainCanvas"
• "Performance is critical - prefer object pooling for frequently instantiated objects"
• "This project follows SOLID principles - explain any architecture decisions"

To completely remove the plugin from your Unity project:

1. Open Window ▸ Package Manager, select the AI Game Developer — MCP package, and click Remove.
2. Close Unity, then delete the Assets/Plugins/NuGet folder (along with its Assets/Plugins/NuGet.meta file).

Contributions are highly appreciated. Bring your ideas and let's make game development simpler than ever before! Do you have an idea for a new Tool or feature, or did you spot a bug and know how to fix it?

Please give this project a star 🌟 if you find it useful!

1. 👉 Read Development documentation
2. 👉 Fork the project
3. Clone the fork and open the ./Unity-MCP-Plugin folder in Unity
4. Implement new things in the project, commit, push it to GitHub
5. Create Pull Request targeting original Unity-MCP repository, main branch.