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gmail-mcp-server

Created Oct 19, 2025 by jasonsum

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Python

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28

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10

README

Gmail Server for Model Context Protocol (MCP)

This MCP server integrates with Gmail to enable sending, removing, reading, drafting, and responding to emails.

Note: This server enables an MCP client to read, remove, and send emails. However, the client prompts the user before conducting such activities.

https://github.com/user-attachments/assets/5794cd16-00d2-45a2-884a-8ba0c3a90c90

Components

Tools

  • send-email

    • Sends email to email address recipient
    • Input:
      • recipient_id (string): Email address of addressee
      • subject (string): Email subject
      • message (string): Email content
    • Returns status and message_id
  • trash-email

    • Moves email to trash
    • Input:
      • email_id (string): Auto-generated ID of email
    • Returns success message
  • mark-email-as-read

    • Marks email as read
    • Input:
      • email_id (string): Auto-generated ID of email
    • Returns success message
  • get-unread-emails

    • Retrieves unread emails
    • Returns list of emails including email ID
  • read-email

    • Retrieves given email content
    • Input:
      • email_id (string): Auto-generated ID of email
    • Returns dictionary of email metadata and marks email as read
  • open-email

    • Open email in browser
    • Input:
      • email_id (string): Auto-generated ID of email
    • Returns success message and opens given email in default browser

Setup

Gmail API Setup

  1. Create a new Google Cloud project
  2. Enable the Gmail API
  3. Configure an OAuth consent screen
    • Select "external". However, we will not publish the app.
    • Add your personal email address as a "Test user".
  4. Add OAuth scope https://www.googleapis.com/auth/gmail/modify
  5. Create an OAuth Client ID for application type "Desktop App"
  6. Download the JSON file of your client's OAuth keys
  7. Rename the key file and save it to your local machine in a secure location. Take note of the location.
    • The absolute path to this file will be passed as parameter --creds-file-path when the server is started.

Authentication

When the server is started, an authentication flow will be launched in your system browser. Token credentials will be subsequently saved (and later retrieved) in the absolute file path passed to parameter --token-path.

For example, you may use a dot directory in your home folder, replacing [your-home-folder].:

Parameter Example
--creds-file-path /[your-home-folder]/.google/client_creds.json
--token-path /[your-home-folder]/.google/app_tokens.json

Usage with Desktop App

Using uv is recommended.

To integrate this server with Claude Desktop as the MCP Client, add the following to your app's server configuration. By default, this is stored as ~/Library/Application\ Support/Claude/claude_desktop_config.json.

{
  "mcpServers": {
    "gdrive": {
      "command": "uv",
      "args": [
        "--directory",
        "[absolute-path-to-git-repo]",
        "run",
        "gmail",
        "--creds-file-path",
        "[absolute-path-to-credentials-file]",
        "--token-path",
        "[absolute-path-to-access-tokens-file]"
      ]
    }
  }
}

The following parameters must be set | Parameter | Example | |-----------------|--------------------------------------------------| | --directory | Absolute path to gmail directory containing server | | --creds-file-path | Absolute path to credentials file created in Gmail API Setup. | | --token-path | Absolute path to store and retrieve access and refresh tokens for application. |

Troubleshooting with MCP Inspector

To test the server, use MCP Inspector. From the git repo, run the below changing the parameter arguments accordingly.

npx @modelcontextprotocol/inspector uv run [absolute-path-to-git-repo]/src/gmail/server.py --creds-file-path [absolute-path-to-credentials-file] --token-path [absolute-path-to-access-tokens-file]
Last updated: Oct 19, 2025

Publisher info

jasonsum's avatar

jasonsum

Product-focused data scientist obsessed with prototyping, operationalizing, and scaling Generative AI and Machine Learning in the cloud.

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