Flutter MCP Server

The Flutter MCP Server is an open - source implementation of the Model Context Protocol (MCP) for the Dart/Flutter ecosystem. It provides a unified API interface for AI assistants and developer tools, supports remote calls to the Dart/Flutter development toolchain, and enables secure and programmable automated development processes.

Developer tools Artificial intelligence chatbots #Flutter tools #AI development #Automation #Dart ecosystem

rating : 2.5 points

downloads : 29

update time : 2025-04-28

What is the Flutter MCP Server?

The Flutter MCP Server is an open - source tool that bridges AI assistants, development tools, and automated workflows through the Model Context Protocol (MCP). It supports remote calls to various tools in the Dart and Flutter SDKs (such as analysis, formatting, fixing, creation, running, and testing), enabling secure, programmable, and context - aware automated development tasks.

How to use the Flutter MCP Server?

First, install the dependencies, then configure the environment variables and start the server. You can choose to run it locally or deploy it in a container. After starting, AI assistants or other clients can interact with the server through the MCP protocol to perform various development tasks.

Applicable scenarios

Suitable for the development of Dart and Flutter projects that require automation, such as code analysis, formatting, unit testing, CI/CD integration, etc. In addition, it also supports resource endpoints to obtain official documentation, news, and community examples.

Main Features

Support for multiple toolsAllows calling tools in the Dart and Flutter SDKs through the MCP protocol, such as analyzers, formatters, fixers, creators, runners, and testing tools.

Secure environment variable managementSensitive information is securely stored through environment variables to ensure that data is not exposed.

Resource endpointsProvides query interfaces for official documentation, news, and community examples, facilitating AI clients to quickly retrieve relevant information.

Advantages and Limitations

Advantages

Enable seamless interaction between AI and code, improving development efficiency.

Support automated repetitive tasks, reducing manual operations.

Unify API interfaces, simplifying the tool - calling process.

Support containerized deployment, suitable for cloud platforms and local environments.

Follow open standards, making it easy to expand new functions.

Limitations

Currently only supports local deployment and is not fully integrated into Smithery.

Some advanced features may require additional configuration.

For novice users, the initial setup may be a bit complicated.

How to Use

Clone the code repository

Clone the code of the Flutter MCP Server from GitHub.

Install dependencies

Use the Dart Pub tool to install the required dependencies.

Configure environment variables

Copy the `.env.example` file and fill in the necessary keys.

Start the server

Run the server locally or use a Docker container.

Usage Examples

Case 1: Code analysisCall the analyzer to check the code quality.

Case 2: Code formattingFormat the code to conform to the code style.

Case 3: Run testsExecute unit tests in the project.

Frequently Asked Questions

How to run the Flutter MCP Server locally?

Does it support Docker deployment?

Does the server support all Dart and Flutter tools?

Related Resources

Flutter MCP Server documentation

Detailed introduction to the server's configuration and usage methods.

GitHub code repository

View the source code and contribute your ideas.

Dart official documentation

Understand the basic knowledge of the Dart language.

🚀 Flutter MCP Server

A bridge between AI assistants, development tools, and automated workflows in the Dart and Flutter ecosystem

⚠️ Important Note

Smithery's cloud deployment is not yet complete. This MCP server currently only supports local use. Smithery integration and registry list functionality are not yet available.

🚀 Quick Start

Overview

Flutter MCP Server is an open - source implementation of the Model Context Protocol (MCP) in the Dart and Flutter ecosystems. It serves as a bridge between AI assistants, development tools, and automated workflows, enabling the automation of Dart and Flutter development tasks in a secure, programmatic, and context - aware manner.

✨ Features

Functionality of the MCP Server

Expose Dart & Flutter SDK tools (analyze, format, fix, create, run, test) as programmable endpoints, allowing AI agents, scripts, or remote clients to call them via the MCP protocol.
Provide a unified API for operations such as code analysis, formatting, project building, and running tests - remotely and securely.
Securely manage environment variables using best practices to ensure that secrets and configurations are not exposed.
Support resource endpoints for document queries, news, and community examples, enabling AI clients to easily retrieve relevant information for Dart/Flutter projects.
Designed for platform integration such as Smithery, but can also be run locally or deployed in any Docker - compatible environment.

Problems Solved

Bridge the gap between AI and code: Allow AI - driven tools (such as code assistants, chatbots, or CI agents) to interact with real Dart/Flutter projects, call tools, and perform context - aware programmatic operations.
Automate repetitive tasks: Developers and teams can automatically perform operations such as code formatting, analysis, testing, and project creation through a unified and secure interface.
Standardize tool calls: Eliminate the need for custom scripts or manual tool execution - all content is exposed through a consistent and discoverable API.
Support remote and cloud workflows: Can be deployed in CI/CD, cloud platforms, or AI - driven development pipelines.
Promote best practices: Handle secrets through environment variables, support containerized deployment, and follow open standards for protocol and API design.

Feature Completeness

Tool endpoints: Invocation of Dart and Flutter tools such as analyze, format, fix, create, run, test.
Resource management: Access and query of resources such as documentation, sample code, and news.
Environment variable support: Configure server behavior and functionality through environment variables.
Scalability: Support custom plugins and extensions to meet specific project requirements.

📦 Installation

Local Installation

Clone the repository:

git clone https://github.com/your-repository/flutter-mcp-server.git
cd flutter-mcp-server

Create a virtual environment (recommended) and install dependencies:

python -m venv env
source env/bin/activate  # Use `env\Scripts\activate` on Windows
pip install -r requirements.txt

Start the server:
```
python server.py
```

Deployment (Smithery)

Associate the project repository with your Smithery account.
Push code changes to trigger a build.
Configure environment variables and deployment parameters in the Smithery interface.
Use the Playground or a compatible client provided by Smithery to verify the server's running status.

📚 Documentation

Server Settings

Edit the config.json file:

{
  "host": "localhost",
  "port": 5000,
  "debug_mode": true,
  "api_key": "your_api_key_here"
}

Environment Variables

Set the following environment variables during deployment or runtime:

FLUTTER_PATH: Specify the Flutter installation path.
DART-sdk_PATH: Specify the Dart SDK installation path.
MCP_SERVER_MODE: Set it to development or production.

💻 Usage Examples

Basic Usage

CLI Invocation

# Analyze code
mcp-server analyze --path ./my_project/

# Format code
mcp-server format --path ./my_project/ --fix

# Create a new project
mcp-server create-project --name my_new_project --type flutter

API Invocation

Use an HTTP request:

curl -X POST http://localhost:5000/api/analyze \
     -H "Content-Type: application/json" \
     -d '{"path":"./my_project/"}'

📄 License

MIT

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