Funplay MCP for Unity

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Funplay MCP for Unity is an MIT-licensed Unity Editor MCP server that lets AI assistants like Claude Code, Cursor, LM Studio, Windsurf, Codex, and VS Code Copilot operate directly inside your running Unity project.

Describe your game in one sentence — your AI assistant builds it in Unity through Funplay MCP for Unity's 91 built-in tools for scene creation, script generation, runtime validation, input simulation, performance analysis, and editor automation.

"Build a snake game with a 10x10 grid, food spawning, score UI, and game-over screen"

Your AI assistant handles it through Funplay MCP for Unity: creates the scene, generates all scripts, sets up the UI, and configures the game logic — all from a single prompt.

Quick Start

If you just want to get connected fast, do these three things:

• Install the Unity package from the Git URL

• Start Funplay > MCP Server

• Use the built-in one-click client configuration

1. Install via UPM (Git URL)

In Unity, go to Window → Package Manager → + → Add package from git URL:

https://github.com/FunplayAI/funplay-unity-mcp.git

💡 Before you clone or install, a quick ⭐ on GitHub would be greatly appreciated.

2. Start the MCP Server

Menu: Funplay → MCP Server to start the server.

The server starts on http://127.0.0.1:8765/ by default.

Open Funplay → Tool Exposure if you want to edit the exact tools exposed by core or full.

Open Funplay → Plugin Settings if you need to adjust plugin debug logging while troubleshooting.

3. Configure Your AI Client

Use the built-in One-Click MCP Configuration in the Funplay > MCP Server window first.

Select your target client, click Configure, and the package writes the recommended MCP config entry for you.

For Claude Code, Cursor, and Codex, click Configure + Skills to also install the default project MCP workflow skill.

If you want project-specific AI guidance for the current Unity project, open Funplay → Project Skills to choose supported platforms and install the default unity-mcp-workflow skill.

If you prefer to edit config files manually, use the examples below as fallback references:

{
  "mcpServers": {
    "funplay": {
      "type": "http",
      "url": "http://127.0.0.1:8765/"
    }
  }
}

{
  "mcpServers": {
    "funplay": {
      "url": "http://127.0.0.1:8765/"
    }
  }
}

LM Studio's mcp.json location can vary by version and platform. Prefer Program > Install > Edit mcp.json in LM Studio. Funplay's one-click Configure button opens LM Studio's lmstudio://add_mcp link and only updates an existing config file if one is already present, instead of creating a guessed path.

{
  "mcpServers": {
    "funplay": {
      "url": "http://127.0.0.1:8765/"
    }
  }
}

{
  "servers": {
    "funplay": {
      "type": "http",
      "url": "http://127.0.0.1:8765/"
    }
  }
}

{
  "mcpServers": {
    "funplay": {
      "url": "http://127.0.0.1:8765/"
    }
  }
}

{
  "mcpServers": {
    "funplay": {
      "type": "http",
      "url": "http://127.0.0.1:8765/"
    }
  }
}

[mcp_servers.funplay]
url = "http://127.0.0.1:8765/"

Use the same JSON structure as Cursor unless your local Windsurf version requires a different MCP config format.

4. Verify the Connection

Open your AI client and try a few safe requests first:

• "Call get_scene_info and tell me what scene is open."

• "Read unity://project/context and summarize the current editor state."

• "Use execute_code to return the active scene name."

If those work, the MCP server, resources, and primary execution tool are connected correctly.

5. Start Building

Open your AI client and try: "Create a 3D platformer level with 5 floating platforms"

Before You Start

• This package is Editor-only. It does not add runtime components to your built game.

• The MCP server starts on http://127.0.0.1:8765/ by default.

• Local MCP server settings are stored in UserSettings/FunplayMcpSettings.json.

• The package defaults to the core MCP tool profile to reduce tool-list noise for AI clients. core currently exposes 29 high-signal tools centered on execute_code, play mode control, input simulation, screenshots, performance inspection, logs, compilation checks, structured object location and component editing, editor selection / prefab-stage state, and execute_menu_item as a low-friction fallback. Switch to full in the MCP Server window if you want all 91 tools exposed.

• execute_code safety checks are enabled by default from the MCP Server window. Clients may still override the default per call with the optional safety_checks argument.

• Plugin debug logging is on by default and can be disabled from Funplay > Plugin Settings. Warnings and errors are always written to the Unity Console.

• All exposed MCP tools run directly. There is no extra approval toggle.

• Menu: Funplay > Check for Updates can refresh Git installs in place or download and import the latest unitypackage automatically.

Why This Project

• execute_code First — Optimized around one in-memory C# execution tool for rich editor/runtime orchestration. See execute_code: In-Memory C# Execution below for details.

• Default Safety Checks — execute_code now has a persistent default-on safety toggle for clients that do not expose per-call arguments clearly

• Play Mode Automation — Enter play mode, simulate keyboard/mouse input, capture screenshots, inspect logs, and validate behavior from the same MCP session

• Project Context Built In — Exposes live resources for project state, active scene, selection, compilation, console output, and MCP interaction history

• Focused by Default, Full When Needed — core exposes a compact high-signal toolset; full exposes all 91 tools

• Single Unity Package — No extra approval UI, no external daemon to click through, and no Python requirement for the Unity-side plugin itself

• Extensible — Add custom tools with attribute-based discovery, or connect Unity to external MCP services when needed

Highlights

• 91 Built-in Tools — Scene editing, assets, scripts, play mode control, screenshots, performance analysis, prompts, resources, structured object location, SerializedObject-based component editing, editor-state inspection, menu-item fallback, and editor automation across 20 modules

• Structured Returns + instanceId Chaining — Tools return {success, message, data} JSON with stable instanceId fields so agents can chain by_id calls reliably instead of re-resolving by name

• IFunplayCommand for execute_code — New snippet template with auto-Undo (ctx.RegisterObjectCreation/Modification/DestroyObject), structured logs (ctx.Log/LogWarning/LogError), and a tracked changelog returned to the agent

• Resources & Prompts — Live project context, scene/selection/error resources, resource templates, and reusable workflow prompts

• Input Simulation + Screenshots — Drive play mode with keyboard/mouse simulation and verify results with game/scene captures

• Built-in Updating — Check for updates from the Unity menu and either re-pull the Git package or auto-import the latest unitypackage

• One-Click Client Configuration — Generate MCP config entries for Claude Code, Cursor, LM Studio, VS Code, Kiro, Trae, Codex, and similar clients directly from the Unity window

• Tool Exposure Control — Edit the exact tools exposed by core and full

• Project Skills Manager — Configure project-level skills for supported AI clients, currently installing the default unity-mcp-workflow skill

• Plugin Settings — Toggle verbose plugin debug logging when troubleshooting MCP connections or tool execution

• Vendor Agnostic — Works with any AI client that supports MCP: Claude Code, Cursor, LM Studio, Windsurf, Codex, VS Code Copilot, etc.

execute_code: In-Memory C# Execution

execute_code is the heart of Funplay MCP for Unity. It lets an AI write a C# snippet, compile it in memory, and run it on the editor thread — the agent gets the full Unity Editor and runtime API surface without writing any files to disk.

• Zero-footprint compilation — Snippets are compiled via CodeDom into an in-memory assembly and executed by reflection. No .cs files are written under Assets/, no domain reload is triggered, no project state is touched beyond what the snippet itself does.

• Editor-ready before it runs — Each call refreshes the AssetDatabase and waits for any pending compilation to settle before compiling the snippet, so external file edits are picked up automatically without a separate request_recompile.

• Auto-Undo + structured logs (recommended template) — Implement IFunplayCommand and use the injected ExecutionContext so every created / modified / destroyed object participates in editor Undo, and the changelog is returned to the agent.

using UnityEngine;
using UnityEditor;
using Funplay.Editor.Tools.Helpers;
using Funplay.Editor.Tools.Scripting;

public class CommandScript : IFunplayCommand
{
    public void Execute(ExecutionContext ctx)
    {
        var go = GameObject.CreatePrimitive(PrimitiveType.Cube);
        ctx.RegisterObjectCreation(go);          // auto-Undo + tracked
        ctx.Log("Created {0}", go.name);
        ctx.ReturnValue = GameObjectSerializer.Describe(go, includeComponents: false);
    }
}

The response carries { logs, created, modified, destroyed, returnValue }, so the agent can verify exactly what changed without re-querying the scene.

The legacy template (public static string Run()) is still supported — useful for one-off inspection snippets where structured tracking is overkill.

When to reach for execute_code vs a specialized tool — execute_code shines for multi-step orchestration, novel reads, and situations where chaining 5–10 narrow tool calls would be noisier than one snippet. For single-field component edits, simple selection changes, or anything covered by an existing tool, prefer the dedicated tool — it is cheaper for the LLM to call and easier to verify.

Comparison With Coplay

The table below compares this repository with the publicly documented behavior of Coplay's open-source unity-mcp repository on GitHub.

Source for Coplay column: CoplayDev/unity-mcp

Comparison With Unity AI Assistant

The table below compares this repository with Unity Technologies' official com.unity.ai.assistant package (v2.7.0-pre.2 as of 2026-05).

For a long-form comparison of the two approaches see Funplay Unity MCP vs Unity AI Assistant detailed comparison (Chinese).

MCP Capabilities

The current open-source package exposes four high-value capability layers:

• Tools — 91 total tools in full, 29 focused tools in core

• Primary execution — execute_code for rich editor/runtime orchestration

• Prompts — workflow prompts like fix_compile_errors, runtime_validation, and create_playable_prototype

• Resources — project context, scene summaries, selection state, compile errors, console errors, MCP interaction history, plus resource templates for scene objects, components, and asset paths

Built-in Tools

Funplay MCP for Unity currently ships with 91 tool functions across 20 modules:

Adding Custom Tools

Create your own tools with simple attribute annotations:

using System.ComponentModel;

[ToolProvider("MyTools")]
public static class MyCustomTools
{
    [Description("Spawns enemies at random positions in the scene")]
    public static string SpawnEnemies(
        [ToolParam("Number of enemies to spawn", Required = true)] int count,
        [ToolParam("Prefab path in Assets")] string prefabPath)
    {
        // Your implementation here
        return $"Spawned {count} enemies";
    }
}

Methods are automatically discovered, converted to snake_case (spawn_enemies), and exposed via MCP with JSON Schema definitions.

Architecture

MCP Server (HTTP JSON-RPC 2.0)
    └─ MCPRequestHandler (protocol handling)
        └─ MCPExecutionBridge
            └─ FunctionInvokerController (reflection-based invocation)
                └─ Tool Functions (91 built-in tools across 20 modules)

External AI Client → HTTP Request → MCPRequestHandler → MCPExecutionBridge → FunctionInvokerController → tool method

Requirements

• Unity 2022.3 or later

• .NET / Mono with Newtonsoft.Json

Contributing

Contributions are welcome! Please read the Contributing Guide before submitting a PR.

License

MIT — Free to use, modify, distribute, and integrate into commercial or open-source projects.