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Gmail-mcp-server

MCP.Pizza Chef: Quantum-369

Gmail MCP Server is a powerful and flexible server built on the Model Context Protocol framework, enabling programmatic interaction with Gmail APIs. It supports reading, sending, searching, and managing emails across multiple Gmail accounts, including handling attachments and real-time email monitoring. Designed for developers, it facilitates advanced email workflows and integrations with OAuth 2.0 authentication and Python 3.12+ compatibility.

Use This MCP server To

Read emails from multiple Gmail accounts programmatically Send emails with attachments via Gmail API Search Gmail inbox using advanced query parameters Download attachments from Gmail messages automatically Monitor incoming emails in real-time for notifications Manage email threads and conversations programmatically Integrate Gmail email workflows into custom applications

README

Gmail MCP Server

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A powerful and flexible Gmail integration server built using the MCP (Message Control Protocol) framework. This server provides a robust interface to interact with Gmail APIs, offering functionality for reading, sending, and managing emails programmatically.

Features

  • Read emails from multiple Gmail accounts
  • Send emails with attachments
  • Search emails with advanced query options
  • Download email attachments
  • Handle email conversations and threads
  • Real-time email monitoring
  • Support for multiple Gmail accounts

Prerequisites

Before running the Gmail MCP server, ensure you have the following:

  1. Python 3.12 or higher
  2. Google Cloud Project with Gmail API enabled
  3. OAuth 2.0 Client ID credentials
  4. Required Python packages (specified in pyproject.toml)

Installation

Installing via Smithery

To install Gmail Integration Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @Quantum-369/Gmail-mcp-server --client claude
  1. Clone the repository:
git clone <your-repository-url>
cd gmail-mcp-server
  1. Create and activate a virtual environment:
python -m venv venv
# On Windows
venv\Scripts\activate
# On Unix/MacOS
source venv/bin/activate
  1. Install dependencies:
pip install .

Setup Google Cloud Project

  1. Go to the Google Cloud Console
  2. Create a new project or select an existing one
  3. Enable the Gmail API for your project
  4. Create OAuth 2.0 credentials:
    • Go to "APIs & Services" > "Credentials"
    • Click "Create Credentials" > "OAuth client ID"
    • Choose "Desktop app" as application type
    • Download the client configuration file
  5. Rename the downloaded file to client_secret.json and place it in the project root directory

Configuration

  1. Set up email identifiers in gmail_token_creator.py:
email_identifier = 'your.email@gmail.com'  # Change this for each account
  1. Run the token creator to authenticate your Gmail accounts:
python gmail_token_creator.py
  1. Repeat the process for each Gmail account you want to integrate

Server Structure

  • gmail_server.py: Main MCP server implementation
  • gmail_api.py: Gmail API interaction functions
  • google_apis.py: Google API authentication utilities
  • Supporting files:
    • read_emails.py: Email reading functionality
    • search_emails.py: Email search functionality
    • send_emails.py: Email sending functionality

Usage

Starting the Server

python gmail_server.py

Available Tools

  1. Send Email:
await send_gmail(
    email_identifier="your.email@gmail.com",
    to="recipient@example.com",
    subject="Test Subject",
    body="Email body content",
    attachment_paths=["path/to/attachment"]
)
  1. Search Emails:
await search_email_tool(
    email_identifier="your.email@gmail.com",
    query="from:someone@example.com",
    max_results=30,
    include_conversations=True
)
  1. Read Latest Emails:
await read_latest_emails(
    email_identifier="your.email@gmail.com",
    max_results=5,
    download_attachments=False
)
  1. Download Attachments:
await download_email_attachments(
    email_identifier="your.email@gmail.com",
    msg_id="message_id",
    download_all_in_thread=False
)

Security Considerations

  • Store client_secret.json securely and never commit it to version control
  • Keep token files secure and add them to .gitignore
  • Use environment variables for sensitive information
  • Regularly rotate OAuth credentials
  • Monitor API usage and set appropriate quotas

Error Handling

The server includes comprehensive error handling and logging:

  • Logs are written to gmail_mcp.log
  • Both file and console logging are enabled
  • Detailed error messages for debugging

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

License

Apachelicense2.0

Support

For issues and feature requests, please use the GitHub issue tracker.

Gmail-mcp-server FAQ

How do I authenticate the Gmail MCP Server with Gmail?
You must create a Google Cloud Project, enable the Gmail API, and use OAuth 2.0 Client ID credentials for secure authentication.
What programming language is required to run the Gmail MCP Server?
The server requires Python 3.12 or higher to run and manage dependencies.
Can the Gmail MCP Server handle multiple Gmail accounts simultaneously?
Yes, it supports managing and interacting with multiple Gmail accounts concurrently.
Does the server support real-time email monitoring?
Yes, it provides real-time monitoring of incoming emails for immediate processing or notifications.
How does the Gmail MCP Server handle email attachments?
It supports sending emails with attachments and downloading attachments from received emails.
Is the Gmail MCP Server compatible with other LLM providers like Claude and Gemini?
Yes, as an MCP server, it is provider-agnostic and can be integrated with models like OpenAI, Claude, and Gemini.
What are the prerequisites for installing the Gmail MCP Server?
You need Python 3.12+, a Google Cloud Project with Gmail API enabled, OAuth 2.0 credentials, and required Python packages.
How can I install the Gmail MCP Server?
Installation can be done via Smithery or by following the setup instructions in the GitHub repository.