AI-First Architecture for Flutter with Gemini CLI, MCP and Firebase AI Logic

This architecture accelerates cross-platform Flutter development by combining Gemini CLI, the MCP server, and Firebase AI Logic. It automates key tasks, ensures best practices, and exposes advanced AI on the client without its own backend.

Context: from AI-assisted to AI-driven in Flutter

In many teams, AI is only used for code autocompletion or to generate isolated snippets. This architecture goes a step further and turns AI into a first-class actor within the development flow and application runtime: AI creates the project foundation, proposes technical design, executes tasks on code via MCP, and in production, powers intelligent experiences from the client with Firebase.

For technical profiles (CTOs, Staff Engineers, Tech Leads), this means less friction when adopting AI at scale: it relies on official tools (Gemini CLI, Flutter MCP, Firebase AI Logic) and open protocols (MCP), avoiding proprietary "black boxes" that are difficult to audit.

Solution layers

The architecture can be understood in three main layers:

• Development layer:
  Gemini CLI with Flutter Extension connected to MCP server automates code, refactors, and testing.
• Rules and specs layer:
  AI Rules, DESIGN.md, IMPLEMENTATION.md, and custom commands for spec-driven development.
• Execution layer:
  Firebase AI Logic with Gemini 3 Pro preview for AI capabilities within the Flutter app without its own backend.

Each layer is designed to be interchangeable and extensible, so it can integrate with existing tools (VS Code, Cursor, CI/CD, etc.).

Flutter Extension for Gemini CLI: assisted but structured development

The Flutter Extension extends Gemini CLI with specific commands for Flutter and Dart, following explicit quality rules and connecting to the official MCP server.

After installing the extension, commands are enabled such as:

• /create-app: guide for bootstrapping a new Flutter project with best practices, generating design and implementation step by step.
• /create-package: scaffolding of Dart packages with standard structure and ready tooling.
• /modify: structured modification sessions on existing code, with automated planning and phased changes.
• /commit: automated pre-commit with analysis, tests, and generation of descriptive commit messages.

Example AGENTS.md file

# AGENTS.md

ProjectName: MarketXFlutter
WidgetNaming: PascalCaseWidget
FolderStructure: lib/features/{feature}/presentation
PreferRiverpod: true

MCP Server: bridge between AI and the real Flutter stack

The Dart and Flutter MCP server exposes ecosystem actions (analysis, execution, tooling) to compatible clients like Gemini CLI, IDEs, or external agents.

At a high level, the MCP architecture can be represented as:

LLM / Gemini CLI ⇄ MCP Client (CLI/IDE/agent) ⇄ Dart/Flutter MCP Server ⇄ Code, tools, and local environment.

Typical MCP server capabilities include:

• Analysis and error correction in code (Dart Analysis Server).
• Hot reload, getting the selected widget, capturing runtime errors.
• Searching and managing dependencies in pub.dev and pubspec.yaml.
• Running tests and analyzing results.

This allows AI to work not on a "static copy" of the repo, but on the same tooling used by the development team, with auditably defined operations.

AI Rules and living documentation: from prompt to technical contract

The extension not only adds commands, it also applies a set of AI rules to ensure best practices in Dart and Flutter, including code style, architecture, testing, and accessibility.

These rules can be complemented with specification files in the repo itself:

• DESIGN.md: high-level architectural vision, key decisions, navigation and state patterns.
• IMPLEMENTATION.md: incremental implementation plan, organized by phases and features, with progress checklist.
• Files like GEMINI.md or AGENTS.md to adjust naming conventions, folder structure, or state management preferences for each project.

This approach turns prompts into something versionable and reviewable by the team, aligning AI with the organization's internal standards instead of relying on ad-hoc instructions in each session.

Spec-driven development with custom commands

From these rules and specifications, the workflow becomes explicitly "spec-driven":

1. The product or functionality is defined in natural language (for example, "workout planning app with progress and exercise library").
2. /create-app transforms that intention into a DESIGN.md and IMPLEMENTATION.md that the team can review, correct, and approve.
3. AI implements each phase of the plan, marking progress in the documents and asking for validation upon completing each block.
4. Subsequent commands like /modify allow applying changes to specific parts of the system (a feature, a widget, a module), keeping the plan and documentation synchronized with the code.

This cycle reduces the communication cost between product, architecture, and execution, especially in distributed teams or those with high turnover.

Integration with editors and existing tools

The architecture is designed to couple with tools already present in the team's stack:

• VS Code and GitHub Copilot: the Dart plugin can register the MCP server and expose its tools to Copilot and other AI integrations.
• Cursor: the Dart/Flutter MCP server can be configured via a .cursor/mcp.json file, making the editor have documentation and tooling in real time.
• Other MCP clients: any agent that speaks MCP can leverage the same capabilities without direct access to source code from the cloud.

This opens the door to specialized agents (for example, focused on performance, accessibility, or refactoring) working on the same shared MCP layer.

Gemini 3 and Firebase AI Logic: production AI without dedicated backend

In the execution layer, Firebase AI Logic offers direct access to Gemini 3 Pro preview from client SDKs, without the need to deploy or maintain its own backend for AI.

Some key points:

• Support for most Gemini 3 capabilities: improved reasoning ("improved thinking"), function calling, "thought signatures," and improved image resolution.
• Client SDKs that automatically manage complex details like preserving thought context via thought_signature, without manual orchestration.
• Available models like Gemini 3 Pro and vision variants, accessible for mobile and web apps from Firebase.

For Flutter, this translates to integrating the firebase_ai package along with firebase_core, initializing Firebase, and creating a GenerativeModel pointing to "gemini-3-pro-preview".

Reasoning, "thought signatures" and cost control

Gemini 3 introduces specific improvements aimed at production environments that need balance between response quality, latency, and cost:

• Improved thinking: deeper reasoning capability, with the possibility of including summaries of the thought process when the corresponding configuration is enabled.
• Thought signatures: encrypted metadata representing the model's "thread of thought" between turns; Firebase SDKs handle them transparently.
• Thinking budgets / levels: configuration of the "thinking budget" to reduce latency and control spending in use cases that don't require extensive analysis.

Added to this is the ability to adjust multimodal input resolution (for example, images) to prioritize accuracy or performance, depending on product needs.

Client agents with function calling

One of the most interesting patterns for CEOs and CTOs interested in "agentic apps" is the combination of Gemini 3 with function calling to orchestrate actions within the Flutter app directly from the client.

The typical flow is:

1. tools are defined in the model (for example, change theme, open a dialog, record feedback) with well-typed schemas.
2. The user formulates an intention in natural language ("set dark theme", "add this exercise to my routine").
3. Gemini decides which function to call, passes typed arguments, and the app executes the action, returning a result that the model can use to continue the interaction.

Example agentic app with function calling

final tools = [

Tool(functionDeclarations: [
FunctionDeclaration('changeThemeColor', 'Changes app color', {...}),
FunctionDeclaration('sendFeedback', 'Sends user feedback', {...})
])
];

final model = Firebase.ai(
backend = GenerativeBackend.googleAI()
).generativeModel(
modelName: "gemini-3-pro-preview",
tools: tools,
);

This provides "copilot in the app" type experiences without the need for a backend orchestrator, which reduces initial complexity and accelerates time-to-market.

Observability and governance: AI Monitoring and security

For corporate environments, observability and governance are as important as functionality. Firebase AI Logic includes a specific monitoring panel for AI usage that allows:

• Analyzing costs, request volume, and latencies per model.
• Comparing Gemini 3 performance against previous models over time.
• Inspecting traces with request attributes, inputs, and outputs for debugging and optimization.

Additionally, it integrates with other Firebase services:

• App Check: protection against unauthorized API use, ensuring consumption comes from legitimate apps.
• Remote Config: dynamic adjustment of models, prompts, and parameters (temperature, top-k, etc.) without needing to publish new app versions.
• Gemini 3 security and compliance infrastructure, which has undergone more thorough security and content safety evaluations within the Google ecosystem.

Benefits for teams and business

From a business and technical leadership perspective, this architecture offers several concrete benefits:

• Reduced startup time for new projects thanks to /create-app and structured design and implementation generation.
• Decreased technical debt by systematically applying quality rules and leveraging MCP for refactors, fixes, and automated tests.
• Accelerated AI feature delivery by eliminating the need to build and maintain a dedicated backend for the model, especially in early product phases.
• Better cost, performance, and risk control thanks to the AI monitoring dashboard, App Check, and Remote Config.

For recruiters and managers, it also facilitates evaluating and scaling teams in environments where AI is natively integrated into the development lifecycle, but under consistent patterns, contracts, and tooling.

How could this help your project?

This type of architecture is especially interesting if:

• You want to launch or scale Flutter products with AI features (in-app assistants, content analysis, recommendations) without exponentially growing backend complexity.
• You're looking to standardize how your organization uses AI to write code, apply refactors, and maintain quality in cross-platform repositories.
• You need a traceable and governable AI approach, where prompts, rules, and decisions are versioned in the repository itself, rather than living only in the team's heads.

Want to explore this AI-first architecture for your Flutter project?
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