Top 5 MCPs for Flutter Developers

Ever find yourself copying and pasting the same Firebase rules, Sentry errors, or API docs into Gemini or Claude just to get the context? I've been there. I integrated hundreds of APIs from different vendors. This process repeats itself across similar projects, PoCs, and demos.

That's where Model Context Protocol or MCP changes everything. MCP is an open-source standard for connecting AI applications to external systems like getting access to data sources, tools, and workflow.

Instead of manually providing context, MCP lets your code editor's AI assistant directly access your Firebase projects, query your Sentry errors, understand your Flutter dependencies, and more—all from your IDE of choice like Cursor or VS Code.

Why should you care about MCPs?

For Flutter developers specifically, certain MCPs can improve your development experience by providing your code editor with deeper context about your tech stack, tools, and services.

Code editors like Cursor and VS Code act as MCP clients, connecting to MCP servers that expose your tools and data. In this article, I'll break down my top 5 MCP server recommendations that you should consider integrating to your workflow.

1. Firebase MCP

If you are working on a Flutter project, you have likely integrated Firebase. I primarily use it for authentication, databases, storage and push notifications.

What Firebase MCP provides:

Direct access to your Firebase project configuration
Real-time database and Firestore data retrieval
Authentication rules and security policies
Cloud Function logs and deployment status
Firebase Analytics and Crash Reporting data

Use Cases:

Few examples how you can use it:

Security Rule Debugging: Instead of manually copying your Firestore security rules, ask your LLM to review and do it for you:

Look at my current Firestore security rules and identify any potential vulnerabilities

Your LLM will have real-time access to your actual rules and can provide contextual feedback.
Firestore Debugging: I find it challenging to manage Firestore data in the Firebase Console, particularly with large datasets. Firebase MCP gives your LLM access to your Firestore database. At the time of writing, you can query dataset using natural language, there's no write feature yet.
Error Analysis: Ask your LLM to access Firebase crash reports directly, so it can help you debug production issues without you having to dig through Firebase Console.

2. Dart & Flutter MCP

As a Flutter developer, you're constantly managing dependencies, dealing with type errors, navigating through your codebase, and working through deprecated APIs that don't exist anymore because of a new version update.

What Dart & Flutter MCP provides:

Analysis of your pubspec.yaml and dependency graph
Real-time linting and analysis results
Dart language server integration for type information
Flutter version and SDK information
Package compatibility checks

Use Cases:

Few examples how you can use it:

Dependency Resolution: When you're considering adding a new package, ask your LLM to check your current dependency tree and warn you about potential conflicts before you add them.
Deprecation Warnings: As packages update, ask your LLM to check which of your dependencies have breaking changes coming and help you plan migrations. This is useful when you're maintaining multiple Flutter projects.
Code Navigation: Ask your LLM to understand your codebase structure better, helping with larger refactoring tasks. Ask it to refactor a feature across multiple files, and it'll have the full context of your project structure.
Type System Insights: Your LLM gets direct access to your type information, making it better at catching type-related bugs and suggesting proper typing patterns. Howeveer, I recommend you use this only once you have figured out your project's preferred style and lint rules, see static analysis.

3. Figma MCP

If you work with designers, you know the pain of manually extracting colors, spacing, and dimensions from Figma designs. There's Figma API to help you extract objets and layers, but you don't want to integrate these every now and then for every project you have.

What Figma MCP provides:

Get access to your Figma design files and components
Access to your design system and tokens
Design documentation for your Figma files

Use Cases:

Few examples how you can use it:

UI Generation: When implementing new screens, ask your LLM to reference the exact specifications from Figma and generate more accurate code. You can point it to the Figma file using the node ID or link and describe what you need.

Now, this is not 100% perfect, even for Flutter and Dart code. From experience, this gets a lot better if you have setup proper design tokens and design system on both Figma and your Flutter project.
Design System Alignment: You can automate the process of getting dimensions, colors, and spacing directly from your design files rather than eyeballing screenshots. Ask your LLM to extract hex codes, spacing values, and font sizes directly from Figma.
Design Documentation: Ask your LLM to help generate comprehensive documentation for your design system based on actual Figma files. This is especially useful when onboarding new team members.

4. Sentry MCP

Sentry is another tool that collects hundreds, if not thousands of issues or bug reports every single day for my projects. Combing through each issue using filters and search queries takes time.

What Sentry MCP provides:

Access Sentry issues and errors
Search for errors in specific files
Query projects and organizations
List and create Sentry DSN's for projects
Invoke Seer to automatically fix issues and retrieve the status and details of an issue fix

Use Cases:

Few examples how you can use it:

Proactive Debugging: Ask your LLM to analyze errors and provide solutions—it will have the full context from Sentry without you needing to manually copy error traces.
File-Based Error Discovery: Ask your LLM to find errors in specific files. For example, "Check Sentry for errors in lib/screens/home_screen.dart and propose solutions". I find this particularly helpful for quick bug fixes and minor refactoring.
Seer Integration: Ask your LLM to invoke Sentry's AI agent (Seer) to automatically analyze issues and get fix recommendations. Use Seer to analyze and propose a solution given a particular issue, eg. PROJECT-456, and it'll run an analysis workflow.
Release Analysis: Before deploying a new release version, ask your LLM to analyze Sentry data for any suspicious patterns or degradation.

5. RevenueCat MCP

Finally, if you are building a SaaS app or plan to add a subscription to your Flutter projects. I highly recommend using RevenueCat. It also enables you to manage your app subscriptions directly into your code editor with its RevenueCat MCP server.

What RevenueCat MCP provides:

Manage apps subscriptions and configure them across all platforms
Handle subscription products and in-app purhcases
Control entitlements and user access permissions
Organize offerings and packages
Generate paywalls for your offerings
Monitor configuration and validate setup

Use Cases:

Few examples how you can use it:

Product and Offering Management: You can ask your LLM to set up subscription products, create offerings, and organize packages. This is an on-going process you need to do, specifically if you're working with multiple apps and products or conducting A/B testing on offerings.
Subscription Flow Debugging: Ask your LLM to debug your subscription logic with real data from RevenueCat about what's happening with your subscribers. Instead of manually checking the RevenueCat dashboard, your LLM can query subscriber status directly.
Configuration Validation: Before launching, ask your LLM to review and validate your entire RevenueCat setup. RevenueCat MCP server can help check for inconsistencies, missing or invalid configurations.

How to get started

Step 1: Review your existing tools

Do you need all 5 MCPs above? Maybe not. Start assessing what you use and go from there:

Using Firebase? Start with Firebase MCP
Have a Figma design system? Add Figma MCP
Monitoring production with Sentry? Integrate Sentry MCP
Using RevenueCat for subscriptions? Include RevenueCat MCP

I recommend adding Dart & Flutter MCP for your Flutter projects by default.

Step 2: Set up MCPs in your code editor

MCPs integrate through your code editor's configuration. For Cursor, you'll configure MCP servers in your settings. For VS Code, you'll need an MCP extension. You'll typically need to:

Install the specific MCP server
Add it to your code editor's MCP configuration (e.g., cursor.json or VS Code settings)
Provide necessary API keys/credentials
Restart your code editor

I recommend adding MCPs at the project level rather than on a device or workspace level. Keeping it at the project level makes it easy for your team to know which MCPs are allowed or available for your projects.

Step 3: Start asking contextual questions

Once set up, you can immediately start asking your LLM contextual questions:

Look at my Sentry errors from today and help me prioritize what to fix first

Check my Firestore security rules against my Firebase auth setup

Compare my current Firebase usage patterns against my pricing tier

Help me implement the exact spacing and colors from my Figma design system

The key here is to be specific with what you need so that your LLMs like Gemini or Claude can use your prompt and the context it gets from the MCP servers to provide a reliable output.

The bottom line

MCPs represent a significant change how developers can be more productive with AI. MCPs transform your code editor into one that deeply understands your specific development setup through MCP clients connecting to specialized MCP servers.

Start with one or two MCPs that match your current tools, then expand it from there based on your needs. Last, your responsibility is to keep yourself updated with the changes that concern your MCPs and re-evaluate your needs from time to time.

What would you like to hear more about MCPs and AI? Shoot me an email.

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