Google Docs MCP Server - Docker

Docker configuration for running the Google Docs MCP Server in a container.

This server provides Model Context Protocol (MCP) tools for interacting with Google Docs and Google Drive, enabling AI assistants like Claude to read, write, and manage your documents.

Features

Document Creation

• Create blank documents - Create new Google Documents from scratch
• Import from Markdown - Create Google Docs with content imported from markdown, including support for:
  • Headings (# to ######)
  • Bold, italic, and bold+italic formatting
  • Bullet and numbered lists
  • Links and inline code
  • Code blocks with monospace formatting
  • Horizontal rules

Document Operations

• Read documents - Export content as text, JSON, or markdown
• Edit documents - Insert, append, and delete text
• Format text - Apply character and paragraph styles (bold, italic, colors, fonts, alignment, etc.)
• Manage structure - Insert tables, page breaks, and images
• Handle tabs - List and work with multi-tab documents
• Bulk operations - Execute multiple document operations in a single batched API call for 5-10x faster performance

Comments

• List, add, reply to, resolve, and delete comments on documents

Drive Integration

• List, search, and get document metadata
• Create and manage folders
• Upload files and images
• Resource-based uploads - Upload files and images using resource identifiers from shared blob storage (for integration with other MCP servers)

Prerequisites

• Docker and Docker Compose installed
• A Google Account
• Google Cloud Project with OAuth credentials

Setup Instructions

Step 1: Obtain Google Cloud Credentials

1. Go to the Google Cloud Console

2. Create or select a project:

   • Click the project dropdown and select "NEW PROJECT"
   • Name it (e.g., "Google Docs MCP") and click "CREATE"

3. Enable required APIs:

   • Go to "APIs & Services" > "Library"
   • Search for and enable Google Docs API
   • Search for and enable Google Drive API

4. Configure OAuth Consent Screen:

   • Go to "APIs & Services" > "OAuth consent screen"
   • Select "External" and click "CREATE"
   • Fill in:
     • App name: e.g., "Google Docs MCP Server"
     • User support email: your email
     • Developer contact: your email
   • Click "SAVE AND CONTINUE"
   • Click "ADD OR REMOVE SCOPES" and add:
     • https://www.googleapis.com/auth/documents
     • https://www.googleapis.com/auth/drive.file
   • Click "UPDATE" then "SAVE AND CONTINUE"
   • Add your Google email as a Test User
   • Click "SAVE AND CONTINUE"

5. Create OAuth Credentials:

   • Go to "APIs & Services" > "Credentials"
   • Click "+ CREATE CREDENTIALS" > "OAuth client ID"
   • Select "Desktop app" as the application type
   • Name it (e.g., "MCP Docker Client")
   • Click "CREATE"
   • Download the JSON file

Step 2: Configure Credentials

1. Create a credentials directory in this project:

   mkdir -p credentials

2. Copy your downloaded OAuth JSON file:

   cp ~/Downloads/client_secret_*.json credentials/credentials.json

Step 3: Build the Docker Image

docker-compose build

Step 4: Authenticate with Google (First-Time Setup)

The first time you run the server, you need to authenticate with Google to generate a token. The authentication uses a loopback OAuth flow - the container starts a temporary web server on port 3000 to receive the OAuth callback.

Important: Create an empty token.json file before running if it doesn't exist:

touch credentials/token.json

Run the container with port 3000 exposed:

docker run -it --rm \
  -p 3000:3000 \
  -v $(pwd)/credentials:/workspace/credentials \
  workspace-google-docs-mcp:latest

Note: On Windows, use full paths instead of $(pwd):

docker run -it --rm ^
  -p 3000:3000 ^
  -v C:/path/to/google-docs-mcp/credentials:/workspace/credentials ^
  workspace-google-docs-mcp:latest

1. The server will output an authorization URL
2. Copy the URL and open it in your browser
3. Log in with your Google account (the one added as a Test User)
4. Click "Allow" to grant permissions
5. Google will redirect to http://localhost:3000 - the container captures this automatically
6. You'll see "Authentication Successful!" in your browser
7. The token.json file will be saved to your credentials/ directory
8. Press Ctrl+C to stop the container

Alternatively, use docker-compose with the auth profile:

docker-compose --profile auth run --rm -p 3000:3000 auth

Step 5: Run the Server

docker-compose up -d

The MCP server is now running and ready to accept connections.

Claude Desktop Integration

Add this to your Claude Desktop config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

Docker Installation

Run the MCP server in a Docker container. This requires mounting the credentials and token files:

{
  "mcpServers": {
    "google-docs": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-p",
        "3000:3000",
        "-v",
        "C:/path/to/google-docs-mcp/credentials:/workspace/credentials",
        "-v",
        "blob-storage:/mnt/blob-storage",
        "-e",
        "BLOB_STORAGE_ROOT=/mnt/blob-storage",
        "-e",
        "BLOB_STORAGE_MAX_SIZE_MB=100",
        "-e",
        "BLOB_STORAGE_TTL_HOURS=24",
        "workspace-google-docs-mcp:latest"
      ]
    }
  }
}

Configuration notes:

• -p 3000:3000 - Exposes port 3000 for OAuth token refresh callbacks
• The first -v mount maps your local credentials/ directory (containing both credentials.json and token.json) to /workspace/credentials/ in the container
• The second -v mount creates a shared blob storage volume for resource-based file uploads (optional, only needed if using resource-based upload features)
• The -e flags set environment variables for blob storage configuration (optional, defaults shown)

Note: Adjust the path (C:/path/to/google-docs-mcp/credentials) to match your local credentials directory. On Linux/macOS, use Unix-style paths (e.g., /home/user/google-docs-mcp/credentials).

Optional: Remove the blob storage volume mount and environment variables if you don't need resource-based upload features.

Using a Running Container

If you prefer to keep the container running in the background with docker-compose up -d:

{
  "mcpServers": {
    "google-docs": {
      "command": "docker",
      "args": [
        "exec",
        "-i",
        "google-docs-mcp-server",
        "uv",
        "run",
        "google-docs-mcp"
      ]
    }
  }
}

Note: The container must be running before starting Claude Desktop.

Restart Claude Desktop after updating the configuration.

File Structure

.
├── Dockerfile                      # Docker image definition (dev + production)
├── docker-compose.yml              # Docker Compose for production
├── docker-compose.devcontainer.yml # Docker Compose for VS Code devcontainer
├── .devcontainer/
│   └── devcontainer.json           # VS Code devcontainer configuration
├── src/
│   └── google_docs_mcp/            # Python source code
│       ├── server.py               # Main MCP server entry point
│       ├── auth.py                 # OAuth2 authentication (loopback flow)
│       └── api/                    # API modules (documents, comments, drive)
├── tests/                          # Test files
├── credentials/                    # Your Google OAuth credentials (gitignored)
│   ├── credentials.json            # OAuth client credentials
│   └── token.json                  # OAuth access token (generated after auth)
├── pyproject.toml                  # Python project configuration
├── .gitignore                      # Git ignore rules
└── README.md                       # This file

Development

VS Code Devcontainer (Recommended)

The project includes a devcontainer configuration for VS Code:

1. Open the project in VS Code
2. When prompted, click "Reopen in Container" (or use Command Palette: "Dev Containers: Reopen in Container")
3. VS Code will build the container and install dependencies automatically

The devcontainer includes:

• Python 3.12 with uv package manager
• Docker CLI (Docker-outside-of-Docker support)
• Node.js 20 and Claude Code CLI
• VS Code extensions: Python, Pylance, debugpy, Ruff, Claude Code
• Port 3000 forwarded for OAuth loopback callback

Local Development (without container)

# Install dependencies
uv sync

# Run the server
uv run google-docs-mcp

# Run tests
uv run pytest

Commands

Command	Description
docker-compose build	Build the Docker image
docker-compose up -d	Start the server in background
docker-compose down	Stop the server
docker-compose logs -f	View server logs
docker-compose --profile auth run --rm auth	Run auth service interactively

Resource-Based File Uploads

This MCP server integrates with mcp_mapped_resource_lib to support resource-based file uploads. This enables efficient file sharing between multiple MCP servers through a shared Docker volume.

Why Use Resource-Based Uploads?

Traditional MCP file transfers require encoding files as base64 and passing them through the MCP protocol, which can be inefficient for large files. With resource-based uploads:

1. Other MCP servers upload files to a shared blob storage volume and return a resource identifier (e.g., blob://1733437200-a3f9d8c2b1e4f6a7.png)
2. This server can directly access those files via the resource identifier and upload them to Google Drive
3. No file data is transferred through the MCP protocol - only the small resource identifier

Available Resource-Based Tools

• upload_image_to_drive_from_resource - Upload an image to Drive using a resource ID
• upload_file_to_drive_from_resource - Upload any file to Drive using a resource ID
• insert_image_from_resource - Insert an image into a document using a resource ID

Setup for Resource-Based Uploads

1. Configure Blob Storage Volume

Add a shared volume for blob storage in your docker-compose.yml:

services:
  google-docs-mcp:
    # ... existing config ...
    volumes:
      - ./credentials:/workspace/credentials
      - blob-storage:/mnt/blob-storage  # Add this line
    environment:
      - PYTHONUNBUFFERED=1
      - BLOB_STORAGE_ROOT=/mnt/blob-storage  # Required
      - BLOB_STORAGE_MAX_SIZE_MB=100         # Optional: max file size (default: 100)
      - BLOB_STORAGE_TTL_HOURS=24            # Optional: time-to-live (default: 24)

volumes:
  blob-storage:
    driver: local

Configuration Options:

• BLOB_STORAGE_ROOT - Required. Path to the blob storage directory
• BLOB_STORAGE_MAX_SIZE_MB - Optional. Maximum file size in MB (default: 100)
• BLOB_STORAGE_TTL_HOURS - Optional. Time-to-live for blobs in hours (default: 24). Blobs older than this will be automatically cleaned up.

2. Update Claude Desktop Config

When using resource-based uploads, update your claude_desktop_config.json to mount the blob storage volume:

{
  "mcpServers": {
    "google-docs": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-p",
        "3000:3000",
        "-v",
        "C:/path/to/google-docs-mcp/credentials:/workspace/credentials",
        "-v",
        "blob-storage:/mnt/blob-storage",
        "-e",
        "BLOB_STORAGE_ROOT=/mnt/blob-storage",
        "-e",
        "BLOB_STORAGE_MAX_SIZE_MB=100",
        "-e",
        "BLOB_STORAGE_TTL_HOURS=24",
        "workspace-google-docs-mcp:latest"
      ]
    }
  }
}

Note: Replace C:/path/to/google-docs-mcp/credentials with your actual credentials path. On Linux/macOS, use Unix-style paths.

Configuration:

• Adjust BLOB_STORAGE_MAX_SIZE_MB to set the maximum file size (in MB)
• Adjust BLOB_STORAGE_TTL_HOURS to control how long blobs are retained before automatic cleanup

3. Share Volume with Other MCP Servers

Other MCP servers can use the same volume. Example configuration:

{
  "mcpServers": {
    "google-docs": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-p", "3000:3000",
        "-v", "C:/path/to/google-docs-mcp/credentials:/workspace/credentials",
        "-v", "blob-storage:/mnt/blob-storage",
        "-e", "BLOB_STORAGE_ROOT=/mnt/blob-storage",
        "-e", "BLOB_STORAGE_MAX_SIZE_MB=100",
        "-e", "BLOB_STORAGE_TTL_HOURS=24",
        "workspace-google-docs-mcp:latest"
      ]
    },
    "other-mcp-server": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "blob-storage:/mnt/blob-storage:ro",
        "other-mcp-server:latest"
      ]
    }
  }
}

Important: The volume name (blob-storage) must be the same across all MCP servers that need to share resources.

Example Usage

With another MCP server that has blob upload capabilities:

1. Other MCP server uploads a file to blob storage:

   User: Upload this image to blob storage
   Other Server: Uploaded! Resource ID: blob://1733437200-a3f9d8c2b1e4f6a7.png
   

2. This server uploads to Google Drive using the resource ID:

   User: Upload that image to my Google Drive using resource blob://1733437200-a3f9d8c2b1e4f6a7.png
   Google Docs Server: Successfully uploaded image "photo.png" from resource blob://1733437200-a3f9d8c2b1e4f6a7.png
   

Resource ID Format

Resource identifiers follow the pattern: blob://TIMESTAMP-HASH.EXT

• TIMESTAMP - Unix timestamp when the file was uploaded
• HASH - SHA256 hash (truncated) for uniqueness
• EXT - Original file extension

Example: blob://1733437200-a3f9d8c2b1e4f6a7.png

Bulk Operations

The bulk_update_google_doc tool allows you to execute multiple document operations in a single batched API call, providing 5-10x performance improvement over individual tool calls.

Why Use Bulk Operations?

When making multiple changes to a document (formatting, inserting content, adding tables, etc.), each individual tool call requires a separate network round-trip to Google's API. This creates significant latency:

Before (individual calls):

• 10 formatting operations = 10 API calls = ~5-10 seconds

After (bulk operations):

• 10 formatting operations = 1 batched API call = ~0.5-1 second

Supported Operations

The bulk tool supports all document manipulation operations:

1. insert_text - Insert text at a specific index
2. delete_range - Delete content in a range
3. apply_text_style - Apply character-level formatting (bold, italic, colors, etc.)
4. apply_paragraph_style - Apply paragraph-level formatting (alignment, headings, spacing, etc.)
5. insert_table - Insert a table
6. insert_page_break - Insert a page break
7. insert_image_from_url - Insert an image from a URL

Example Usage

Here's an example of creating a formatted document with a title, introduction, table, and styled text in a single API call:

{
  "document_id": "your-document-id-here",
  "operations": [
    {
      "type": "insert_text",
      "text": "Project Status Report\n\n",
      "index": 1
    },
    {
      "type": "apply_paragraph_style",
      "start_index": 1,
      "end_index": 23,
      "named_style_type": "HEADING_1",
      "alignment": "CENTER"
    },
    {
      "type": "insert_text",
      "text": "Executive Summary\n\n",
      "index": 23
    },
    {
      "type": "apply_paragraph_style",
      "start_index": 23,
      "end_index": 42,
      "named_style_type": "HEADING_2"
    },
    {
      "type": "insert_text",
      "text": "This report provides an overview of project progress and key metrics.\n\n",
      "index": 42
    },
    {
      "type": "insert_text",
      "text": "Key Metrics\n\n",
      "index": 113
    },
    {
      "type": "apply_paragraph_style",
      "start_index": 113,
      "end_index": 126,
      "named_style_type": "HEADING_2"
    },
    {
      "type": "insert_table",
      "rows": 4,
      "columns": 3,
      "index": 126
    },
    {
      "type": "insert_text",
      "text": "\n\nConclusion\n",
      "index": 127
    },
    {
      "type": "apply_text_style",
      "text_to_find": "Conclusion",
      "match_instance": 1,
      "bold": true,
      "font_size": 14
    }
  ]
}

Operation Parameters

Each operation is a dictionary with a type field and operation-specific parameters:

insert_text

• text (string): Text to insert
• index (integer): Position to insert at (1-based)
• tab_id (string, optional): Tab ID for multi-tab documents

delete_range

• start_index (integer): Start of range (1-based, inclusive)
• end_index (integer): End of range (1-based, exclusive)
• tab_id (string, optional): Tab ID

apply_text_style

Range targeting (choose one):

• start_index and end_index (integers): Direct range specification
• text_to_find (string) and match_instance (integer): Find specific text

Style properties:

• bold, italic, underline, strikethrough (boolean)
• font_size (float): Font size in points
• font_family (string): Font name (e.g., "Arial", "Times New Roman")
• foreground_color, background_color (string): Hex color (e.g., "#FF0000")
• link_url (string): URL for hyperlink

apply_paragraph_style

Range targeting (choose one):

• start_index and end_index (integers): Direct range specification
• text_to_find (string) and match_instance (integer): Find text, format its paragraph
• index_within_paragraph (integer): Format paragraph containing this index

Style properties:

• alignment (string): "START", "END", "CENTER", "JUSTIFIED"
• indent_start, indent_end (float): Indentation in points
• space_above, space_below (float): Spacing in points
• named_style_type (string): "NORMAL_TEXT", "HEADING_1" through "HEADING_6", "TITLE", "SUBTITLE"
• keep_with_next (boolean): Keep paragraph with next

insert_table

• rows (integer): Number of rows
• columns (integer): Number of columns
• index (integer): Position to insert (1-based)

insert_page_break

• index (integer): Position to insert (1-based)

insert_image_from_url

• image_url (string): Publicly accessible image URL
• index (integer): Position to insert (1-based)
• width, height (float, optional): Dimensions in points

Limitations

• Maximum 500 operations per call (automatically batched into groups of 50 for Google API)
• Operations are executed in the order provided
• All operations must be valid before any are executed (fail-fast validation)

Tips for Best Performance

1. Group related operations: Combine all changes to a document in a single bulk call
2. Use index-based targeting when possible: Text-finding operations require fetching the document first
3. Order matters: Structure your operations to account for index changes (e.g., insert text before applying formatting to that text)

Security Notes

• Never commit credentials.json or token.json to version control
• The .gitignore file is configured to exclude these files
• Treat these files like passwords - they grant access to your Google account
• The token.json file allows the server to access your Google account without re-authentication

Troubleshooting

"credentials.json not found" error:

• Ensure you've placed credentials.json in the credentials/ directory
• Check the file is named exactly credentials.json

Authentication fails:

• Verify you added your email as a Test User in Google Cloud Console
• Ensure you enabled both Google Docs API and Google Drive API

Docker container won't start:

• Check that both credentials.json and token.json exist in credentials/
• Run docker-compose logs to see error messages

Claude Desktop shows "Failed to connect":

• Ensure the container is running: docker-compose ps
• Verify the container name is google-docs-mcp-server
• Try restarting Claude Desktop

License

This Docker configuration is provided under the MIT License. The underlying google-docs-mcp server is licensed separately.