Todo App using MCP Server

What is Todo App using MCP Server?

A simple Todo application enhanced with natural language processing using the Google Gemini API, MongoDB for persistence, and MCP (Model Context Protocol) for real-time updates via Server-Sent Events (SSE). Built with a modular MVC architecture using Express.js, it is intended for developers who want to manage todos through plain‑language commands and receive live updates.

How to use Todo App using MCP Server?

Clone the repository, install dependencies, and create a .env file with PORT, MONGO_URI, and GEMINI_API_KEY. Start the server with npm start. Send POST requests to /api/command with a JSON body containing a "command" field (e.g., {"command": "Add a task to buy milk"}). Supported commands include adding, listing, completing, and deleting todos. Connect to /sse for real‑time updates (requires an MCP‑compatible client).

Key features of Todo App using MCP Server

• Add todos using natural language (e.g., “buy milk”)
• List todos, optionally including completed ones
• Mark todos as complete by MongoDB ID
• Delete todos by MongoDB ID
• Real‑time updates via SSE with MCP integration

Use cases of Todo App using MCP Server

• Quickly capture tasks by speaking or typing natural commands
• Manage a shared todo list that updates live across clients
• Experiment with MCP and SSE in a minimal Express.js application
• Prototype a voice‑friendly task manager backed by a cloud database

FAQ from Todo App using MCP Server

How does the server interpret natural language?

The server sends the command text to the Google Gemini API, which extracts the intent and parameters (e.g., “add”, “list”, “complete”).

What database does it use?

Todos are stored in a MongoDB Atlas (cloud) cluster using Mongoose as the ODM.

How do I receive real‑time updates?

Connect to the /sse endpoint with an MCP‑compatible client. The server pushes SSE events when todos change.

What runtime and dependencies are required?

Node.js v18 or higher, a MongoDB Atlas cluster, and a Google Gemini API key (available from Google AI Studio).

How do I configure the application?

Set PORT, MONGO_URI, and GEMINI_API_KEY in a .env file in the project root. The server reads these on startup.