AI Personal Assistant with MCP Client

This project implements an AI-powered personal assistant that interacts with users via a chat interface. The assistant connects to multiple MCP (Model Context Protocol) servers, processes queries using Anthropic AI, and maintains a history of conversations similar to ChatGPT. It also provides a web interface where users can seamlessly interact with the AI assistant.

Features

• Chat Interface: A clean and user-friendly chat interface for interacting with the AI assistant.
• Chat History: Ability to view previous messages in the conversation.
• Multiple MCP Server Connectivity: The MCP client connects to multiple servers for redundancy and load balancing.
• AI Integration: Uses Anthropic AI for generating responses based on user input.
• Session Management: Each conversation is tied to a unique session for context retention.
• Responsive Design: Fully responsive chat UI that works on both desktop and mobile devices.

Tech Stack

• Frontend:
  • React
  • TypeScript
  • Tailwind CSS (for styling)
• Backend:
  • Node.js
  • Express.js
  • Anthropic AI (for generating AI responses)
  • MCP Client (for handling multiple server communication)

Setup Instructions

1. Clone the Repository

git clone https://github.com/yourusername/ai-personal-assistant.git
cd ai-personal-assistant

2. Install Dependencies

npm install

3. Set Up Environment Variables

Create a .env file in the root directory and add the following:

REACT_APP_API_URL=http://localhost:3000   # Replace with your backend server URL
VITE_API_URL=http://localhost:3000       # API URL for MCP server
OPENAI_API_KEY=your_openai_api_key       # Replace with your OpenAI API key (if using GPT-3 or another model)

4. Start the Development Server

npm start

Acknowledgments

Anthropic AI: For providing the AI model used in this assistant.

MCP Protocol: For handling communication with multiple servers.

Explanation of the Sections:

1. Project Overview: A brief description of the project’s key features.
2. Tech Stack: List of technologies used in both frontend and backend.
3. Setup Instructions: Steps to clone, install dependencies, and set up the project.
4. Running the Application: Instructions on how to start the project and the backend server.
5. Testing: How to test the chat functionality.
6. Contribution Guidelines: Information on how others can contribute to the project.
7. License: The license the project is under (here it’s MIT).
8. Acknowledgments: Credit for libraries, tools, and services used in the project.

How to Use the README:

1. Replace placeholders such as your_openai_api_key and GitHub URL with actual information related to your project.
2. Adjust setup instructions if needed based on your project structure.
3. Add or modify any sections according to your project’s requirements.

Let me know if you need any further customization!