mcp-server

# MCP Server A TypeScript implementation of a Model Context Protocol (MCP) server, designed to evaluate MCP capabilities over/with RAG (Retrieval-Augmented Generation) approaches for [Nutrimate](https://github.com/samrasugu/nutrimate) and other AI applications. ## Overview This project explores the Model Context Protocol as an alternative to RAG systems, providing a standardized way for AI applications to access external data sources and tools. The server is built with TypeScript, Node.js and the [MCP TypeScript SDK](https://github.com/modelcontextprotocol/typescript-sdk). ## Features - **TypeScript-first**: Built with full TypeScript support for better developer experience - **MCP Protocol**: Implements the Model Context Protocol via Anthropic's TypeScript SDK ## Prerequisites - Node.js (v18 or higher) - npm or yarn - TypeScript knowledge (recommended) ## Getting Started 1. **Clone the repository:** ```bash git clone https://github.com/samrasugu/mcp-server.git cd mcp-server ``` 2. **Install dependencies:** ```bash npm install ``` 3. **Development mode:** ```bash npm run dev ``` 4. **Build the project:** ```bash npm run build ``` 5. **Start the production server:** ```bash npm run start ``` ## Environment Variables Set the following environment variables (or configure them in your MCP file): - `GOOGLE_PUBLIC_API_KEY`: Your Google API key - `GOOGLE_CALENDAR_ID`: Your Google Calendar ID (e.g., your Gmail address) You can set these in a `.env` file: ``` GOOGLE_PUBLIC_API_KEY=your-google-api-key GOOGLE_CALENDAR_ID=your-calendar-id ``` ## Usage ### Get Today's Meetings To print today's meetings directly in your terminal: ```bash node src/index.js today ``` ## Integrating with Cursor MCP & Chat Interaction This project supports integration with [Cursor MCP](https://www.cursor.so/mcp), enabling you to interact with your server via chat and automate workflows. ### 1. Configure MCP Server in Cursor 1. Open Cursor and go to the MCP panel. 2. Add a new MCP server with the following settings (example): - **Name**: [your name]'s Calendar - **Command**: `node` - **Args**: `src/index.ts` - **Host**: `localhost` - **Port**: `3000` - **Environment Variables**: Set `GOOGLE_PUBLIC_API_KEY` and `GOOGLE_CALENDAR_ID` as needed. You can also create a `.cursor/mcp.json` file for quick setup. The content of the `.cursor/mcp.json` file should be like this: ```json { "servers": [ { "name": "[your name]'s Calendar", "command": "node", "args": ["src/index.ts"], "host": "localhost", "port": 3000, "env": { "GOOGLE_PUBLIC_API_KEY": "your-google-api-key", "GOOGLE_CALENDAR_ID": "your-calendar-id" } } ] } ``` ### 2. Start the MCP Server Run the server locally: ```bash node src/index.ts ``` Or use the MCP panel's built-in controls to start/stop the server. ### 3. Interact via Chat - Open the chat panel in Cursor. - Select your MCP server (e.g., "Sam's Calendar") from the chat source dropdown. - Ask questions like: - `Do I have any meetings today?` - `What is my next event?` - `List all meetings for this week.` - The server will respond with information from your Google Calendar. ### 4. Customization You can extend the server to support more commands or integrate with other tools. Update `src/index.ts` and restart the server to apply changes. ## Project Structure ``` mcp-server/ ├── src/ │ └── index.ts # Main server implementation ├── package.json # Dependencies and scripts ├── tsconfig.json # TypeScript configuration └── README.md # This file ``` ## Development This project is actively being developed as part of an evaluation comparing MCP to RAG systems. The implementation may evolve as the evaluation progresses. ### Available Scripts - `npm run dev` - Start development server with hot reload - `npm run build` - Build the TypeScript project - `npm run start` - Start the production server - `npm test` - Run tests (to be implemented) ## MCP vs RAG Evaluation This server is being developed to evaluate the effectiveness of the Model Context Protocol compared to traditional RAG approaches for AI applications, particularly focusing on: - **Performance**: Response times and resource usage - **Flexibility**: Ease of adding new data sources and tools - **Maintainability**: Code organization and extensibility - **Integration**: How well it works with existing AI workflows ## License MIT