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Mcp Boilerplate
What is Mcp Boilerplate
MCP Boilerplate is a powerful, production-ready MCP server that implements the Model Context Protocol, featuring robust SSE transport, built-in tools, and comprehensive error handling for seamless AI model integration with data sources.
Use cases
Use cases include building AI-driven applications, real-time data analytics platforms, integrating machine learning models with existing data infrastructures, and developing enterprise-grade applications that require robust error management and data streaming.
How to use
To use MCP Boilerplate, clone the repository, navigate to the project directory, install the dependencies using npm, and start the server. Ensure you have Node.js and npm installed beforehand.
Key features
Key features include production readiness, robust SSE transport for efficient data streaming, comprehensive error handling, built-in development tools, and seamless integration capabilities for connecting AI models to various data sources.
Where to use
MCP Boilerplate can be used in fields such as AI development, data integration, real-time data processing, and any application requiring stable and efficient communication between AI models and data sources.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Mcp Boilerplate
MCP Boilerplate is a powerful, production-ready MCP server that implements the Model Context Protocol, featuring robust SSE transport, built-in tools, and comprehensive error handling for seamless AI model integration with data sources.
Use cases
Use cases include building AI-driven applications, real-time data analytics platforms, integrating machine learning models with existing data infrastructures, and developing enterprise-grade applications that require robust error management and data streaming.
How to use
To use MCP Boilerplate, clone the repository, navigate to the project directory, install the dependencies using npm, and start the server. Ensure you have Node.js and npm installed beforehand.
Key features
Key features include production readiness, robust SSE transport for efficient data streaming, comprehensive error handling, built-in development tools, and seamless integration capabilities for connecting AI models to various data sources.
Where to use
MCP Boilerplate can be used in fields such as AI development, data integration, real-time data processing, and any application requiring stable and efficient communication between AI models and data sources.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
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
- 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:
-
Clone the repository:
git clone https://github.com/milxxyzxc/mcp-boilerplate.git
-
Navigate to the project directory:
cd mcp-boilerplate
-
Install the dependencies:
npm install
-
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:
- Fork the repository.
- Create a new branch (
git checkout -b feature/YourFeature
). - Make your changes and commit them (
git commit -m 'Add some feature'
). - Push to the branch (
git push origin feature/YourFeature
). - 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: [email protected]
- Twitter: @MCPBoilerplate
Feel free to contribute and make this project even better!
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.