Flutter/Dart Model Context Protocol (MCP) Usage Guide
# MCP (Model Context Protocol) for Dart & Flutter Developers
Software development is rapidly moving toward **AI-assisted workflows** and **smart tooling**. From IDEs that autofill your code, to AI assistants that analyze projects, and automated test pipelines — these all need a **standardized way to exchange information**.
That’s where the **Model Context Protocol (MCP)** comes in.
If you’re a Dart or Flutter developer wondering how MCP fits into your workflow, this guide explains:
- **What MCP is and why it matters**
- **How Dart integrates MCP via the `dart_mcp` package**
- **How to build and connect MCP servers & clients**
- **Example implementations**
- **Best practices for secure, AI-driven workflows**
---
## 📚 Table of Contents
1. [Prerequisites](#prerequisites)
2. [What is MCP?](#what-is-mcp)
3. [Why MCP Matters for Dart & Flutter Developers](#why-mcp-matters-for-dart-and-flutter-developers)
4. [MCP in the Dart Ecosystem](#mcp-in-the-dart-ecosystem)
- [The `dart_mcp` Package](#the-dart_mcp-package)
5. [Real-World Use Cases](#real-world-use-cases)
6. [How MCP Works](#how-mcp-works)
7. [Getting Started – Step by Step](#getting-started--step-by-step)
8. [Hands-on Example](#hands-on-example)
9. [Building a Simple MCP Server in Dart](#building-a-simple-mcp-server-in-dart)
10. [Creating an MCP Client](#creating-an-mcp-client)
11. [MCP vs. Custom HTTP Servers](#mcp-vs-custom-http-servers)
12. [Best Practices](#best-practices-safety-and-permissions)
13. [Beginner Roadmap](#beginner-roadmap)
14. [Advanced Extensions](#moving-beyond-beginner-level)
15. [Conclusion](#conclusion)
16. [References](#references)
---
## Prerequisites
Before diving in, make sure you have:
- **Dart SDK 3.9+** / **Flutter 3.35 Beta+**
- Knowledge of async programming (`async` / `await`)
- Understanding of `stdin` / `stdout` (MCP uses them for transport)
- Access to an MCP-compatible client (IDE plugin, CLI tool)
- Pub dependency: dependencies:
dart_mcp: ^latest
---
## What is MCP?
The **Model Context Protocol** allows **AI models** to talk to **developer tools** in a **structured, safe, and permission-based way**.
Instead of pasting code into chats, MCP lets the AI call predefined **tools** from your project:
- Run analyzer
- Get file contents
- Run tests
- Search in pub.dev
This makes AI a **context-aware collaborator**.
---
## Why MCP Matters for Dart & Flutter Developers
With MCP you can:
- **Expose project-specific tools** to AI assistants
- **Integrate AI across multiple environments** without reinventing protocols
- **Create secure & scoped automation** for your project
---
## MCP in the Dart Ecosystem
The Dart team has an **official experimental** package: [`dart_mcp`](https://pub.dev/packages/dart_mcp).
### The `dart_mcp` Package
Features:
- Build MCP servers & clients in Dart
- STDIO transport for low-latency local communication
- Supports **Tools**, **Resources**, **Prompts**
- Schema validation & structured requests
Limitations:
- HTTP & stream transports are experimental
- Authorization not fully implemented
---
## Real-World Use Cases
- **AI bug fixing** (RenderFlex overflow)
- **Dependency checks** and upgrade suggestions
- **Automated code review** in PRs
- **Mentorship tools** for learners
- **Custom dev tools** – e.g., list routes, generate navigation tests
---
## How MCP Works
AI Assistant ⇄ MCP Client (IDE/Agent) ⇄ Dart MCP Server ⇄ Tools & Codebase
- **Server**: runs locally, exposes tools with permission controls
- **Client**: IDE extension, agent CLI, connects using MCP transport
Structured capability negotiation keeps AI powerful but safe.
---
## Getting Started — Step by Step
### 1. Start MCP Serverdart mcp-server
### 2. Configure MCP Client
Example (**Gemini CLI** config in `settings.json`):{
"mcpServers": {
"dart": {
"command": "dart",
"args": ["mcp-server"]
}
}
}
### 3. Make a Request
From IDE assistant:
> "List untested functions"
### 4. Optional — Build Custom Capabilityserver.registerTool(
'list_routes',
(ctx, params) async => { 'routes': await extractRoutesFromProject() },
);
---
## Hands-on Example
**Fixing a layout overflow**:
1. Client calls `diagnose_layout`
2. Server inspects widget tree
3. Suggests wrapping `Row` children in `Expanded`
---
## Building a Simple MCP Server in Dart
import 'package:dart_mcp/server.dart';
class MyServer extends MCPServer with ToolsSupport, ResourcesSupport {
MyServer() : super(Implementation(name: 'my-dart-mcp-server', version: '1.0.0'));
@override
Future initialize() async {
registerTool(
'analyzeCode',
description: 'Analyze Dart code using the analyzer.',
inputSchema: {
'type': 'object',
'properties': { 'path': { 'type': 'string' } },
'required': ['path']
},
callback: (args, extra) async {
final path = args['path'];
return { 'message': 'Analyzed project at $path successfully.' };
},
);
await super.initialize();
}
}
void main() => MyServer().connect(StdioServerTransport());
---
## Creating an MCP Client
import 'package:dart_mcp/client.dart';
void main() async {
final client = await MCPClient.connectStdioServer(stdin, stdout);
final initResult = await client.initialize(Implementation(name: 'my-client', version: '1.0.0'));
print('Tools: ${initResult.serverCapabilities.tools}');
final result = await client.callTool('analyzeCode', { 'path': 'lib/' });
print(result);
}
---
## MCP vs. Custom HTTP Servers
**Custom HTTP**:
- Ad-hoc commands
- Manual JSON parsing
- No standard schema negotiation
**MCP**:
- Formal protocol
- Capability discovery
- Schema validation
- Safe integration with AI tools
---
## Best Practices, Safety, and Permissions
- **Start read-only**
- **Review AI changes**
- **Limit scope**
- **Audit MCP calls**
- **Define team policies**
---
## Beginner Roadmap
1. Learn MCP basics from [spec](https://dart.dev/tools/mcp-server)
2. Build minimal `dart_mcp` server
3. Integrate with IDE assistant
4. Add more tools gradually
5. Implement monitoring & logs
---
## Moving Beyond Beginner Level
- Integrate MCP with CI/CD pipelines
- Create AI-powered assistants for Flutter projects
- Combine with **multi-platform publishing tools** like [AiToEarn](https://aitoearn.ai) for distributing AI-generated code summaries, tutorials, and reports
- Publish to Douyin, Kwai, Bilibili, Facebook, YouTube, and more with analytics and AI ranking
---
## Conclusion
MCP gives Dart & Flutter developers a **secure, standardized** bridge between projects and AI tools.
With `dart_mcp`, you can build extensible workflows today.
Integrating with publishing platforms like **[AiToEarn](https://aitoearn.ai)** connects technical automation to monetized content workflows — making projects both **smarter** and **more impactful**.
---
## References
- [Dart MCP Documentation](https://dart.dev/tools/mcp-server)
- [`dart_mcp` on pub.dev](https://pub.dev/packages/dart_mcp)
- [Dart MCP Server GitHub](https://github.com/its-dart/dart-mcp-server)
---
