Gmail MCP Server

Gmail MCP AI Integration

An intelligent Gmail assistant powered by Azure OpenAI and the Model Context Protocol (MCP). This project enables AI agents to interact with Gmail through a standardized MCP server interface, allowing for natural language email management and automation.

Features

• MCP Server for Gmail
• Search emails with natural language queries
• Read email content with full metadata
• Send emails with attachments
• Mark emails as read/unread
• Delete and trash management
• Label operations
• Draft management

• AI Agent Integration
• Azure OpenAI-powered assistant using Agent Framework
• Natural language email understanding
• Context-aware responses
• Efficient inbox management

• Authentication
• OAuth 2.0 Gmail authentication
• Environment variable support for Azure deployments
• Secure credential management
• Refresh token handling

Prerequisites

• Python 3.11 or higher
• Google Cloud account with Gmail API enabled
• Azure OpenAI Service (for agent functionality)
• Required OAuth 2.0 credentials from Google Cloud Console

Setup

1. Clone the repository

git clone <repository-url>
cd Gmail-MCP-AI-Integration

2. Set up Python environment

python -m venv venv
venv\Scripts\activate  # On Windows
# source venv/bin/activate  # On macOS/Linux
pip install -r requirements.txt

3. Configure Gmail API

Step-by-step guide to get Gmail API credentials:

1. Go to Google Cloud Console

1. Create or Select a Project

• Click the project dropdown at the top
• Click "New Project" or select existing project
• Enter a project name (e.g., "Gmail MCP Server")
• Click "Create"

1. Enable the Gmail API

• In the left sidebar, go to APIs & Services → Library
• Search for "Gmail API"
• Click on "Gmail API" in the results
• Click the Enable button

1. Configure OAuth Consent Screen (First-time setup)

• Go to APIs & Services → OAuth consent screen
• Select External user type
• Click Create
• Fill in required fields:
• App name: "Gmail MCP Server"
• User support email: Your email
• Developer contact: Your email
• Click Save and Continue
• On Scopes page, click Save and Continue
• On Test users page, add your Gmail address, click Save and Continue

1. Create OAuth 2.0 Credentials

• Go to APIs & Services → Credentials
• Click + Create Credentials → OAuth client ID
• Choose Application type: Desktop app
• Name it: "Gmail MCP Desktop Client"
• Click Create
• Click Download JSON (or click the download icon for your credential)
• Save the file as credentials.json

1. Place credentials.json in your project

   # Create credentials folder if it doesn't exist
   mkdir credentials
   
   # Move downloaded file to credentials/credentials.json
   # Windows: copy Downloads\credentials.json credentials\credentials.json
   # Linux/Mac: mv ~/Downloads/credentials.json credentials/credentials.json

Note: On first run, a browser window will open asking you to authorize the application. After authorization, a token.json file will be created automatically.

4. Configure Azure OpenAI (Optional, for AI Agent)

If you want to use the AI agent functionality, follow these steps to get Azure OpenAI credentials:

Step-by-step guide to get Azure OpenAI credentials:

1. Go to Azure Portal

• Sign in with your Microsoft account
• If you don't have an account, click "Create one" (Free account available)

1. Create Azure OpenAI Resource

• Click Create a resource (+ icon in top left)
• Search for "Azure OpenAI"
• Click Create → Azure OpenAI
• Fill in the required fields:
• Subscription: Select your Azure subscription
• Resource group: Create new or select existing (e.g., "gmail-mcp-rg")
• Region: Choose a region (e.g., "East US", "West Europe")
• Name: Enter a unique name (e.g., "gmail-mcp-openai")
• Pricing tier: Select Standard S0
• Click Review + Create → Create
• Wait for deployment (usually 1-2 minutes)

1. Get Endpoint and API Key

• After deployment, click Go to resource
• In the left menu, click Keys and Endpoint
• Copy these values:
• Endpoint: Should look like https://your-resource.openai.azure.com/
• Key 1: Your API key (long string of characters)

1. Deploy a Model

• In the left menu, click Model deployments or go to Azure OpenAI Studio
• Click Create new deployment or Deploy model
• Select a model:
• gpt-4: Most capable, higher cost
• gpt-4-turbo: Fast and capable
• gpt-4.1-mini: Fast, cost-effective (recommended for testing)
• gpt-35-turbo: Fastest, lowest cost
• Give it a deployment name (e.g., "gpt-4-mini")
• Click Create

1. Create and configure .env file

   # Copy the example file
   copy .env.example .env

Edit .env and add your Azure OpenAI credentials: ``env AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/ AZURE_OPENAI_API_KEY=your_actual_api_key_here AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4-mini ``

Cost Note: Azure OpenAI is a paid service. Check Azure OpenAI Pricing for rates. New accounts get $200 free credit for 30 days.

5. Run the MCP Server

Start the Gmail MCP server:

python app/server.py

The server will:

• Authenticate with Gmail (browser window will open on first run)
• Start listening for MCP protocol messages via STDIO
• Log status messages to stderr

6. Test the Server

Use the provided test client:

python mcp_client.py

This will:

• Connect to the MCP server
• Test basic operations (search, read emails)
• Display results

Usage

Using the MCP Server

The server exposes the following MCP tools:

search_emails

Search for emails using Gmail query syntax.

{
  "query": "from:someone@example.com",
  "max_results": 10,
  "include_spam_trash": false
}

get_email

Get detailed information about a specific email.

{
  "message_id": "abc123..."
}

send_email

Send a new email with optional attachments.

{
  "to": "recipient@example.com",
  "subject": "Hello",
  "body": "Message content",
  "cc": "cc@example.com",
  "bcc": "bcc@example.com",
  "attachments": [{"filename": "doc.pdf", "content": "base64..."}]
}

mark_as_read / mark_as_unread

Change read status of emails.

{
  "message_ids": ["id1", "id2"]
}

delete_email / trash_email / untrash_email

Manage email deletion and trash.

{
  "message_id": "abc123..."
}

list_labels

Get all Gmail labels.

{}

add_label / remove_label

Manage email labels.

{
  "message_ids": ["id1", "id2"],
  "label_ids": ["Label_1"]
}

create_draft / list_drafts / send_draft / delete_draft

Manage email drafts.

Using with Claude Desktop

Add to your Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "gmail": {
      "command": "python",
      "args": ["C:/Projects/Gmail-MCP-AI-Integration/app/server.py"]
    }
  }
}

Then restart Claude Desktop. The Gmail tools will be available in conversations.

Using the AI Agent

Run the agent directly:

python app/agent.py

The agent uses Azure OpenAI and can interact with Gmail through the MCP protocol.

Project Structure

Gmail-MCP-AI-Integration/
├── app/
│   ├── __init__.py
│   ├── agent.py           # AI agent using Agent Framework
│   ├── gmail_auth.py      # OAuth 2.0 authentication
│   ├── gmail_client.py    # Gmail API wrapper
│   └── server.py          # MCP server implementation
├── credentials/
│   ├── credentials.json   # OAuth credentials (not in repo)
│   └── token.json        # Access token (auto-generated)
├── mcp_client.py          # Test client for MCP protocol
├── test_*.py             # Various test scripts
├── requirements.txt       # Python dependencies
└── README.md             # This file

Troubleshooting

Common Issues

❌ Authentication Failed

• Cause: Missing or invalid credentials.json
• Solution:
• Ensure credentials.json is in the credentials/ folder
• Verify it's for a Desktop application type
• Re-download from Google Cloud Console if needed

❌ "Access blocked: This app's request is invalid"

• Cause: Incorrect OAuth 2.0 setup
• Solution:
• Make sure you created a "Desktop application" credential type
• Check that Gmail API is enabled in your project

❌ Server Not Responding

• Cause: Server initialization failed
• Solution:
• Check stderr output for error messages
• Verify all dependencies are installed
• Ensure credentials are properly configured

❌ Token Expired

• Cause: Refresh token is invalid or expired
• Solution:
• Delete credentials/token.json
• Re-run the server to re-authenticate

❌ ModuleNotFoundError

• Cause: Missing dependencies
• Solution: pip install -r requirements.txt

Environment Variables for Azure Deployment

For production deployments (e.g., Azure), you can use environment variables:

GOOGLE_CLIENT_ID=your_client_id
GOOGLE_CLIENT_SECRET=your_client_secret
GOOGLE_REFRESH_TOKEN=your_refresh_token

The server will automatically detect and use these instead of file-based authentication.

Architecture

• MCP Protocol: Standardized interface for AI tools
• FastMCP: Framework for building MCP servers
• Gmail API: Official Google API for email operations
• Agent Framework: Azure's framework for building AI agents
• Azure OpenAI: LLM for natural language understanding

Security Notes

• Never commit credentials.json or token.json to version control
• Use environment variables for production deployments
• Limit OAuth scopes to only what's needed
• Regularly review and rotate credentials
• Use Azure Key Vault for production secrets

Testing

Run the test suite:

# Test MCP protocol
python test_mcp_protocol.py

# Test server functionality
python test_server.py

# Test individual features
python test_functionality.py

Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/name)
3. Commit your changes (git commit -am 'Add feature')
4. Push to the branch (git push origin feature/name)
5. Create Pull Request

License

This project is provided as-is for educational and development purposes.

Acknowledgments

• Built with FastMCP framework
• Uses Agent Framework for Azure AI integration
• Powered by Gmail API
• Model Context Protocol by Anthropic

Resources

• MCP Documentation
• Gmail API Documentation
• Azure OpenAI Service
• FastMCP GitHub

Support

For issues and questions:

• Check the Troubleshooting section
• Review test results in TEST_RESULTS.md
• Open an issue on GitHub

1. Commit changes (git commit -am 'Add feature')
2. Push branch (git push origin feature/name)
3. Create Pull Request

License

[Your License Type] - See LICENSE file for details