mcpField
G
Google Drive MCP Server

Google Drive MCP Server

Local
github.com/asadudinFiles & Storage1Updated 76w ago

About Google Drive MCP Server

A Python MCP server for AI systems and clients that need to manage files in Google Drive. It exposes file, folder, and sharing operations through a standardized interface.

Key features

  1. 01List, upload, download, and delete files
  2. 02Create and organize folders
  3. 03Share files and manage permissions
  4. 04Paginated file listings
  5. 05API connection diagnostics

Use cases

  1. 01Finding files in Google Drive
  2. 02Uploading or downloading Drive files
  3. 03Organizing files into folders
  4. 04Sharing files with other users

MCP support

The server exposes Google Drive file, folder, sharing, and diagnostic operations as MCP tools for clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Google Drive MCP Server

A Model Context Protocol (MCP) server for interacting with Google Drive API. This server provides a standardized interface for AI systems to access and manipulate files in Google Drive.

Features

  • File Operations: List, upload, download, and delete files
  • Folder Management: Create folders and organize content
  • File Sharing: Share files with specific users and manage permissions
  • Pagination Support: Handle large file listings efficiently
  • Comprehensive Error Handling: Detailed error reporting for easier debugging

Prerequisites

  • Python 3.8+
  • Google Cloud project with Drive API enabled
  • Service account with appropriate permissions

Setup

1. Google Cloud Setup
  1. Create a project in Google Cloud Console
  2. Enable the Google Drive API
  3. Create a service account with appropriate permissions
  4. Download the service account JSON key file
2. Local Setup

Clone the repository and install dependencies:

# Clone the repository
git clone <repository-url>
cd mcp-server-gdrive

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Copy example environment file and edit with your settings
cp .env.example .env

Edit the .env file with your configuration:

HOST=0.0.0.0
PORT=8055
GOOGLE_SERVICE_ACCOUNT_FILE=your-service-account-file.json
3. Docker Setup

Alternatively, you can use Docker:

# Build and run with Docker Compose
docker-compose up --build

Running the Server

Local Execution
python main.py --transport sse
Docker Execution
docker-compose up

MCP Tools

This server provides the following MCP tools:

File Operations
  • list_files: List files in Google Drive with pagination support
  • get_file_info: Get detailed information about a specific file
  • upload_file: Upload a file to Google Drive
  • download_file: Download a file from Google Drive
Folder Operations
  • create_folder: Create a new folder in Google Drive
Sharing and Permissions
  • share_file: Share a file or folder with another user
Debugging
  • debug_api_connection: Debug the Google Drive API connection

Client Configuration

To connect to this MCP server from a client:

{
  "gdrive": {
    "transport": "sse",
    "serverUrl": "http://localhost:8055/sse"
  }
}

Note: Make sure to include /sse at the end of the URL for SSE transport.

Security Considerations

  • The service account JSON file contains sensitive credentials. Never commit it to version control.
  • Use environment variables for all sensitive configuration.
  • Consider implementing additional authentication if deploying publicly.

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.