What is Mcp Email Fetch

MCP-email-fetch is an AI-powered email assistant that utilizes the Model Context Protocol (MCP) to fetch, process, rank, and summarize emails. It integrates sentiment analysis and a vector store for efficient email retrieval.

Use cases

Use cases include automating responses to customer inquiries, summarizing important emails for quick review, prioritizing emails based on sentiment, and managing large volumes of email efficiently.

How to use

To use MCP-email-fetch, set up the Python backend, configure the Google API authentication, and interact with the provided API endpoints to fetch and manage emails.

Key features

Key features include email fetching from Gmail, AI-powered summarization, sentiment analysis for prioritization, email ranking using LLM, storage of email embeddings in a FAISS vector store, automatic replies, and a dashboard API for user interaction.

Where to use

MCP-email-fetch can be used in various fields such as customer support, personal email management, and any domain requiring efficient email processing and prioritization.

MCP Email Fetch

Overview

MCP Email Fetch is an AI-powered email assistant that integrates the Model Context Protocol (MCP) to fetch, process, rank, and summarize emails. It leverages LLM-based sentiment analysis and a vector store for efficient retrieval. The project consists of a Python-based backend and an MCP server for seamless interaction.

Features

• Email Fetching: Retrieves emails from Gmail.
• AI-Powered Summarization: Extracts key insights from emails.
• Sentiment Analysis: Determines email sentiment for prioritization.
• Email Ranking: Uses an LLM-based ranking system.
• FAISS Vector Store: Stores email embeddings for efficient retrieval.
• Automatic Replies: Generates AI-powered responses to emails.
• Dashboard API: Provides endpoints for user interaction.

Project Structure

└── hiteshydv001-mcp-email-fetch/
    ├── backend/
    │   ├── main.py                  # Main entry point for backend API
    │   ├── requirements.txt         # Dependencies for backend
    │   ├── ai/
    │   │   ├── email_ranker.py       # AI-powered email ranking
    │   │   ├── llm_handler.py        # Handles interactions with LLM
    │   │   └── sentiment_analysis.py # Sentiment classification
    │   ├── config/
    │   │   ├── config.py             # General configuration
    │   │   ├── logging_config.py     # Logging configuration
    │   │   ├── mcp_config.py         # MCP-specific settings
    │   │   ├── token.json            # Google API authentication token
    │   │   └── secrets.json          # Google API client secrets
    │   ├── database/
    │   │   ├── db.py                 # Database connection
    │   │   └── models.py             # Database models
    │   ├── email_processing/
    │   │   ├── email_reply.py        # AI-generated replies
    │   │   ├── email_summarizer.py   # Summarizes email content
    │   │   └── gmail_fetch.py        # Fetches emails from Gmail
    │   ├── mcp/
    │   │   ├── faiss_vectorstore.py  # FAISS-based email storage
    │   │   └── mcp_retriever.py      # Retrieves relevant emails
    │   ├── routes/
    │   │   ├── dashboard_routes.py   # API routes for dashboard
    │   │   └── email_routes.py       # API routes for emails
    │   └── utils/
    │       ├── logger.py             # Logging utilities
    │       ├── preprocess.py         # Data preprocessing
    │       └── token_manager.py      # Token authentication
    └── mcp-server/
        ├── package-lock.json
        ├── package.json
        ├── tsconfig.json             # TypeScript config for MCP server
        ├── dist/
        │   └── index.js              # Compiled MCP server
        └── src/
            └── index.ts              # MCP server implementation

Installation

Backend Setup

1. Clone the repository:

   git clone https://github.com/Hiteshydv001/mcp-email-fetch.git
   cd mcp-email-fetch/backend
   

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Set up environment variables and Google API authentication:

   • Obtain Google Cloud Email API credentials.
   • Download token.json and secrets.json from Google Cloud Console.
   • Place them inside the config/ directory.
   • Ensure gmail_fetch.py uses these credentials.
   • Create a .env file in the backend/ directory and add:

     MONGODB_URI=your_mongodb_connection_string
     GEMINI_API_KEY=your_gemini_api_key
     GMAIL_SECRET_KEY=your_gmail_secret_key
     DEBUG=True
     

5. Run the backend server:

   python main.py
   

MCP Server Setup

1. Navigate to the mcp-server directory:

   cd ../mcp-server
   

2. Install dependencies:

   npm install
   

3. Run the MCP server:

   npm run start
   

Usage

• Use the API endpoints to fetch, summarize, and reply to emails.
• Interact via a dashboard to view email sentiment and ranking.
• Automate email responses with LLM-generated replies.

Contributing

Feel free to contribute by submitting issues or pull requests.

License

This project is licensed under the MIT License.

🚀 Developed by Hitesh Kumar