Marionette MCP #

"Playwright MCP/Cursor Browser, but for Flutter apps"

Marionette MCP enables AI agents (like Cursor, Claude Code, etc.) to inspect and interact with running Flutter applications. It provides a bridge between the MCP protocol and the Flutter VM service, allowing agents to see the widget tree, tap elements, enter text, scroll, and capture screenshots for automated smoke testing and interaction.

Marionette MCP vs Flutter MCP #

The official Dart & Flutter MCP server focuses on development-time tasks: searching pub.dev, managing dependencies, analyzing code, and inspecting runtime errors. Marionette MCP is complementary - it focuses on runtime interaction: tapping buttons, entering text, scrolling, and taking screenshots. Use Flutter MCP to build your app, use Marionette MCP to test and interact with it.

Quick Start #

Note: Your Flutter app must be prepared to be compatible with this MCP.

1. Prepare your Flutter app - Add the marionette_flutter package and initialize MarionetteBinding in your main.dart.
2. Install the MCP server - Add marionette_mcp to your projects dev_dependencies.
3. Configure your AI tool - Add the MCP server command (dart run marionette_mcp) to your tool's configuration (Cursor, Claude, etc.).
4. Run your app in debug mode - Look for the VM service URI in the console (e.g., ws://127.0.0.1:12345/ws).
5. Connect and interact - Ask the AI agent to connect to your app using the URI and start interacting.

Installation #

1. Add MCP Server Package #

Run the following command to activate the marionette_mcp global tool:

dart pub global activate marionette_mcp

dart pub add dev:marionette_mcp

2. Add Flutter Package #

Run the following command in your Flutter app directory:

flutter pub add marionette_flutter

Flutter App Integration #

You need to initialize the MarionetteBinding in your app. This binding registers the necessary VM service extensions that the MCP server communicates with.

Basic Setup #

If your app uses standard Flutter widgets (like ElevatedButton, TextField, Text, etc.), the default configuration works out of the box.

import 'package:flutter/foundation.dart';
import 'package:flutter/material.dart';
import 'package:marionette_flutter/marionette_flutter.dart';

void main() {
  // Initialize Marionette only in debug mode
  if (kDebugMode) {
    MarionetteBinding.ensureInitialized();
  } else {
    WidgetsFlutterBinding.ensureInitialized();
  }

  runApp(const MyApp());
}

Log Collection (get_logs) #

Marionette collects application logs via Dart's logging package by listening to Logger.root.onRecord. This means logging is required for the MCP to be able to scrape logs.

If your app doesn't use logging (or doesn't emit logs via Logger(...)), get_logs will likely be empty. If you already use another logging solution, you may need to bridge it into logging for get_logs to work. If you'd like first-class support for another logging solution, please open an issue describing your setup and expectations.

Custom Design System #

If you use custom widgets in your design system, you can configure Marionette to recognize them as interactive elements or extract text from them.

Why isInteractiveWidget? A typical Flutter screen has hundreds of widgets in its tree - Padding, Container, Column, SizedBox, etc. When the AI agent calls get_interactive_elements, Marionette filters this down to only actionable targets: buttons, text fields, switches, sliders, etc. This gives the agent a concise, manageable list instead of an overwhelming dump of layout widgets.

By default, Marionette recognizes standard Flutter widgets like ElevatedButton, TextField, and Switch. If your app uses custom widgets (e.g., MyPrimaryButton that wraps styling around a GestureDetector), Marionette won't know they're tappable unless you tell it. The isInteractiveWidget callback lets you mark your custom widget types as interactive, so they appear in the element list and can be targeted by tap and other tools.

import 'package:flutter/foundation.dart';
import 'package:flutter/material.dart';
import 'package:marionette_flutter/marionette_flutter.dart';
import 'package:my_app/design_system/buttons.dart';
import 'package:my_app/design_system/inputs.dart';

void main() {
  if (kDebugMode) {
    MarionetteBinding.ensureInitialized(
      MarionetteConfiguration(
        // Identify your custom interactive widgets
        isInteractiveWidget: (type) =>
            type == MyPrimaryButton ||
            type == MyTextField ||
            type == MyCheckbox,

        // Extract text from your custom widgets
        extractText: (widget) {
          if (widget is MyText) return widget.data;
          if (widget is MyTextField) return widget.controller?.text;
          return null;
        },
      ),
    );
  } else {
    WidgetsFlutterBinding.ensureInitialized();
  }

  runApp(const MyApp());
}

Tool Configuration #

Add the MCP server to your AI coding assistant's configuration.

Cursor #

Or manually add to your project's .cursor/mcp.json or your global ~/.cursor/mcp.json:

{
  "mcpServers": {
    "marionette": {
      "command": "marionette_mcp",
      "args": []
    }
  }
}

Google Antigravity #

Open the MCP store, click “Manage MCP Servers”, then “View raw config” and add to the opened mcp_config.json:

{
  "mcpServers": {
    "marionette": {
      "command": "marionette_mcp",
      "args": []
    }
  }
}

Gemini CLI #

Add to your ~/.gemini/settings.json:

{
  "mcpServers": {
    "marionette": {
      "command": "marionette_mcp",
      "args": []
    }
  }
}

Claude Code #

You can run the following command to add it:

claude mcp add --transport stdio marionette -- marionette_mcp

Copilot #

Add to your mcp.json:

{
  "servers": {
    "marionette": {
      "command": "marionette_mcp",
      "args": []
    }
  }
}

Available Tools #

Once connected, the AI agent has access to these tools:

Example Scenarios #

Marionette MCP shines when used by coding agents to verify their work or explore the app. Here are some real-world scenarios:

1. Verify a New Feature #

Context: You just asked the agent to implement a "Forgot Password" flow. Prompt:

"Now that you've implemented the Forgot Password screen, let's verify it. Connect to the app, navigate to the login screen, tap 'Forgot Password', enter a valid email, and submit. Check the logs to ensure the API call was made successfully."

2. Post-Refactor Smoke Test #

Context: You performed a large refactor on the navigation logic. Prompt:

"I've refactored the routing. Please run a quick smoke test: connect to the app, cycle through all tabs in the bottom navigation bar, and verify that each screen loads without throwing exceptions in the logs."

3. Debugging UI Issues #

Context: Users reported a button is unresponsive on the Settings page. Prompt:

"Investigate the 'Clear Cache' button on the Settings page. Connect to the app, navigate there, find the button using get_interactive_elements, tap it, and analyze the logs to see if an error is occurring or if the tap is being ignored."

How It Works #

1. Initialization: Your Flutter app initializes MarionetteBinding, which registers custom VM service extensions (ext.flutter.marionette.*).
2. Connection: The MCP server connects to your app's VM Service URL.
3. Interaction: When an AI agent calls a tool (like tap), the MCP server translates this into a call to the corresponding VM service extension in your app.
4. Execution: The Flutter app executes the action (e.g., simulates a tap gesture) and returns the result.

Assumptions & Limitations #

• Prefer pasting the VM Service URI manually: While some tooling can sometimes discover or infer the VM Service endpoint, the most reliable workflow is to copy the ws://.../ws URI from your flutter run output (or DevTools link) and paste it to the agent when calling connect.

• The agent may not know your app: Marionette can “see” the widget tree and interact with UI elements, but it doesn’t automatically understand your product’s flows, naming conventions, or edge cases. If you want reliable navigation and assertions, provide extra context in the prompt (what screen to reach, expected labels/keys, preconditions, and the goal of the interaction).

• “Your mileage may vary” interactions: Some actions are implemented via best-effort simulation of user behavior (gestures, focus, text entry, scrolling). Depending on platform, custom widgets, overlays, or app-specific gesture handling, results may vary. If a flow is flaky, consider exposing clearer widget keys, simplifying hit targets, or adding custom MarionetteConfiguration hooks for your design system. And if you hit something that consistently doesn’t behave as expected, a small repro in an issue helps us improve it.

Troubleshooting #

• "Not connected to any app": Ensure the AI agent has called connect with the valid VM Service URI before using other tools.
• Finding the URI: Run your Flutter app in debug mode (flutter run). Look for a line like: The Flutter DevTools debugger and profiler on iPhone 15 Pro is available at: http://127.0.0.1:9101?uri=ws://127.0.0.1:9101/ws. Use the ws://... part.
• Release Mode: Marionette only works in debug (and profile) mode because it relies on the VM Service. It will not work in release builds.
• Elements not found: Ensure your widgets are visible. If using custom widgets, make sure they are configured in MarionetteConfiguration.

🛠️ Maintained by LeanCode #

This package is built with 💙 by LeanCode. We are top-tier experts focused on Flutter Enterprise solutions.

Why LeanCode? #

• Creators of Patrol – the next-gen testing framework for Flutter.

• Production-Ready – We use this package in apps with millions of users.

• Full-Cycle Product Development – We take your product from scratch to long-term maintenance.