unity
Enables AI assistants to control the Unity Editor via MCP, allowing scene manipulation, asset management, compilation, and testing through natural language.
README
LLM Dev Tools
Unity Editor tooling for AI-assisted development. AgentBridge exposes a
file-based command protocol so external tools (Claude Code, scripts, CI) can
drive the Unity Editor — and MCP turns every command into a tool your LLM can
call directly.
Quickstart
1. Install the package
Window → Package Manager → + → Add package from git URL
https://github.com/simonwittber/AgentBridge.git
Or for a specific version:
https://github.com/simonwittber/AgentBridge.git#v0.1.0
2. Build the CLI
Download a pre-built binary from the latest release
and place it on your PATH. Or build from source:
cd path/to/AgentBridge/Harness~/dffrnt-agent
go build -o dffrnt-agent . # macOS / Linux
go build -o dffrnt-agent.exe . # Windows
3. Wire into Claude Code
Add to .claude/settings.json inside your Unity project:
{
"mcpServers": {
"unity": {
"command": "dffrnt-agent",
"args": ["serve"]
}
}
}
Run dffrnt-agent from your Unity project root, or pass --project <path> to
point it at the project directory.
4. Open Unity and verify
Start (or focus) your Unity project. In Claude Code, the unity MCP server
will appear in /mcp with all bridge commands available as tools.
5. Send your first command
dffrnt-agent status
Expected output:
{
"cmd": "status",
"status": "ok",
"uptime_s": 42.3,
"queued": 0,
"busy": false
}
Requirements
- Unity 6000.0 or later
- Go 1.25+ (only needed to build from source)
Built-in commands
Core
| Command | Description |
|---|---|
status |
Bridge liveness, uptime, queue depth |
compile |
Request script compilation; returns structured errors and warnings |
refresh |
Trigger AssetDatabase.Refresh() and wait for completion |
commands |
List all available commands and their arguments |
Scene
| Command | Description |
|---|---|
scene_info |
Name, path, dirty flag, root count |
scene_open |
Open a scene by asset path |
scene_save |
Save the active scene |
scene_new |
Create a new empty or default scene |
Hierarchy & objects
| Command | Description |
|---|---|
hierarchy |
Scene tree as JSON (configurable depth) |
object_find |
Find a GameObject by path; returns components |
objects_find |
Find all objects with a given component type |
object_create |
Create a GameObject or primitive |
object_delete |
Delete a GameObject |
object_active |
Activate or deactivate a GameObject |
object_rename |
Rename a GameObject |
object_select |
Select one or more objects in the editor |
Components & assets
| Command | Description |
|---|---|
component_get |
Get all serialized fields of a component |
component_set |
Set a serialized field on a component |
component_add |
Add a component by type name |
prefab_open |
Open a prefab in prefab stage |
prefab_save |
Save and exit the current prefab stage |
asset_info |
GUID and importer settings for an asset |
asset_set |
Set an importer field and reimport |
asset_find |
Find assets by type / label filter |
material_get |
Get all shader properties of a material |
material_set |
Set a shader property on a material |
Editor & console
| Command | Description |
|---|---|
console_logs |
Recent Unity console messages (ring buffer, newest first) |
play_mode |
Enter, exit, or query play mode |
menu_item |
Invoke a Unity menu item by path |
run_tests |
Run edit-mode or play-mode tests; returns pass/fail/skip |
uuid |
Generate a UUID v4 |
Adding custom commands
Implement IAgentCommand in any Editor assembly:
using LLMDevTools;
using UnityEditor;
[InitializeOnLoad]
public class MyCommand : IAgentCommand
{
static MyCommand() => AgentBridge.Register(new MyCommand());
public string Cmd => "my_cmd";
public string Description => "Does something useful.";
public ArgSpec[] Args => new[]
{
new ArgSpec("message", "string", "", "Text to log"),
};
public void Execute(string uid, string requestJson)
{
AgentBridge.NewWriter(uid, Cmd)
.Set("echoed", requestJson)
.Send("ok");
}
}
Protocol
Commands are newline-delimited JSON written to Temp/agent_input:
{"uid":"a1b2c3d4","cmd":"compile"}
Responses are appended to Temp/agent_output:
{"uid":"a1b2c3d4","cmd":"compile","status":"ok","session_id":1749123456789,"errors":[],"warnings":[]}
Unity also writes Temp/agent_session every 5 seconds:
{"pid":12345,"state":"idle","active_scene":"Main","play_mode":false,"compile_errors":0,"written_at":1749123456789}
dffrnt-agent reads this file to verify Unity is alive before sending any command.
Output rotates at 2 MB; input is truncated on Unity startup.
LLM Agent Log window
Open via Window → General → LLM Agent Log. Live scrolling view of all commands and responses — green = ok, red = error.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.