Google Toolbox
# py-mcp-google-toolbox
[](https://smithery.ai/server/@jikime/py-mcp-google-toolbox)   
An MCP server that provides AI assistants with powerful tools to interact with Google services, including Gmail, Google Calendar, Google Drive, and Google Search.
<a href="https://glama.ai/mcp/servers/@jikime/py-mcp-google-toolbox">
<img width="380" height="200" src="https://glama.ai/mcp/servers/@jikime/py-mcp-google-toolbox/badge" alt="Google Toolbox MCP server" />
</a>
## Overview
py-mcp-google-toolbox provides the following Google-related functionalities:
- Gmail operations (read, search, send, modify)
- Google Calendar management (events creation, listing, updating, deletion)
- Google Drive interactions (search, read files)
- Google Search integration (search web)
## Table of Contents
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Configure MCP Settings](#configure-mcp-settings)
- [Tools Documentation](#tools-documentation)
- [Gmail Tools](#gmail-tools)
- [Calendar Tools](#calendar-tools)
- [Drive Tools](#drive-tools)
- [Search Tools](#search-tools)
- [Development](#development)
- [License](#license)
## Prerequisites
1. **Python**: Install Python 3.12 or higher
2. **Google Cloud Console Setup**:
- Go to [Google Cloud Console](https://console.cloud.google.com/)
- Create a new project or select an existing one
- Enable the Service API:
1. Go to "APIs & Services" > "Library"
2. Search for and enable "Gmail API"
3. Search for and enable "Google Calendar API"
4. Search for and enable "Google Drive API"
5. Search formand enable "Custom Search API"
- Set up OAuth 2.0 credentials from GCP:
1. Go to "APIs & Services" > "Credentials"
2. Click "Create Credentials" > "OAuth client ID"
3. Choose "Web application"
4. Note down the Client ID and Client Secret
- Client ID
- Client Secret
5. download secret json and rename to credentials.json
- Generate an API key
3. Go to [Custom Search Engine](https://cse.google.com/cse/all) and get its ID
## Installation
#### Git Clone
```bash
git clone https://github.com/jikime/py-mcp-google-toolbox.git
cd py-mcp-google-toolbox
```
#### Configuration
1. Install UV package manager:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
2. Create and activate virtual environment:
```bash
uv venv -p 3.12
source .venv/bin/activate # On MacOS/Linux
# or
.venv\Scripts\activate # On Windows
```
3. Install dependencies:
```bash
uv pip install -r requirements.txt
```
4. Get refresh token (if token is expired, you can run this)
```bash
uv run get_refresh_token.py
```
This will:
- Open your browser for Google OAuth authentication
- Request the following permissions:
- `https://www.googleapis.com/auth/gmail.modify`
- `https://www.googleapis.com/auth/calendar`
- `https://www.googleapis.com/auth/gmail.send`
- `https://www.googleapis.com/auth/gmail.readonly`
- `https://www.googleapis.com/auth/drive`
- `https://www.googleapis.com/auth/drive.file`
- `https://www.googleapis.com/auth/drive.readonly`
- Save the credentials to `token.json`
- Display the refresh token in the console
5. Environment variables:
```bash
cp env.example .env
vi .env
# change with your key
GOOGLE_API_KEY=your_google_api_key
GOOGLE_CSE_ID=your_custom_search_engine_id
GOOGLE_CLIENT_ID=your_google_client_id
GOOGLE_CLIENT_SECRET=your_google_client_secret
GOOGLE_REFRESH_TOKEN=your_google_refresh_token
```
6. copy credentials.json to project root folder (py-mcp-google-toolbox)
#### Using Docker
1. Build the Docker image:
```bash
docker build -t py-mcp-google-toolbox .
```
2. Run the container:
```bash
docker run py-mcp-google-toolbox
```
#### Using Local
1. Run the server:
```bash
mcp run server.py
```
2. Run the MCP Inspector
```bash
mcp dev server.py
```
## Configure MCP Settings
Add the server configuration to your MCP settings file:
#### Claude desktop app
1. To install automatically via [Smithery](https://smithery.ai/server/@jikime/py-mcp-google-toolbox):
```bash
npx -y @smithery/cli install @jikime/py-mcp-google-toolbox --client claude
```
2. To install manually
open `~/Library/Application Support/Claude/claude_desktop_config.json`
Add this to the `mcpServers` object:
```json
{
"mcpServers": {
"Google Toolbox": {
"command": "/path/to/bin/uv",
"args": [
"--directory",
"/path/to/py-mcp-google-toolbox",
"run",
"server.py"
]
}
}
}
```
#### Cursor IDE
open `~/.cursor/mcp.json`
Add this to the `mcpServers` object:
```json
{
"mcpServers": {
"Google Toolbox": {
"command": "/path/to/bin/uv",
"args": [
"--directory",
"/path/to/py-mcp-google-toolbox",
"run",
"server.py"
]
}
}
}
```
#### for Docker
```json
{
"mcpServers": {
"Google Toolbox": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"py-mcp-google-toolbox"
]
}
}
}
```
## Tools Documentation
### Gmail Tools
- `list_emails`: Lists recent emails from Gmail inbox with filtering options
- `search_emails`: Performs advanced Gmail searches with detailed email content retrieval
- `send_email`: Composes and sends emails with support for CC, BCC recipients
- `modify_email`: Changes email states (read/unread, archived, trashed) by modifying labels
### Calendar Tools
- `list_events`: Retrieves upcoming calendar events within specified time ranges
- `create_event`: Creates new calendar events with attendees, location, and description
- `update_event`: Modifies existing calendar events with flexible parameter updating
- `delete_event`: Removes calendar events by event ID
### Drive Tools
- `read_gdrive_file`: Reads and retrieves content from Google Drive files
- `search_gdrive`: Searches Google Drive for files with customizable queries
### Search Tools
- `search_google`: Performs Google searches and returns formatted results
## Development
For local testing, you can use the included client script:
```bash
# Example: List emails
uv run client.py list_emails max_results=5 query="is:unread"
# Example: Search emails
uv run client.py search_emails query="from:test@example.com"
# Example: Send email
uv run client.py send_email to="test@example.com" subject="test mail" body="Hello"
# Example: Modify email
uv run client.py modify_email id=MESSAGE_ID remove_labels=INBOX add_labels=ARCHIVED
# Example: List events
uv run client.py list_events time_min=2025-05-01T00:00:00+09:00 time_max=2025-05-02T23:59:59+09:00 max_results=5
# Example: Create event
uv run client.py create_event summary="new event" start=2025-05-02T10:00:00+09:00 end=2025-05-02T11:00:00+09:00 attendees="user1@example.com,user2@example.com"
# Example: Update event
uv run client.py update_event event_id=EVENT_ID summary="update event" start=2025-05-02T10:00:00+09:00 end=2025-05-02T11:00:00+09:00 attendees="user1@example.com,user2@example.com"
# Example Delete event
uv run client.py delete_event event_id=EVENT_ID
# Example: Search Google
uv run client.py search_google query="what is the MCP?"
# Example: Search Google Drive
uv run client.py search_gdrive query=mcp
# Example: Read file
uv run client.py read_gdrive_file file_id=1234567890
```
## License
MIT License
TDQS
Scored across 11 tools
Most tools have distinct purposes targeting different Google services (Calendar, Gmail, Drive, Search), but 'list_emails' and 'search_emails' could cause some confusion since both involve email retrieval. The descriptions help differentiate them, but the overlap in email listing vs. searching might lead to occasional misselection.
The naming follows a consistent verb_noun pattern (e.g., create_event, list_emails, search_gdrive) with clear, descriptive names. There is a minor deviation with 'modify_email' using 'modify' instead of a more specific verb like 'update', but overall the pattern is predictable and readable.
With 11 tools, the count is well-scoped for a 'Google Toolbox' covering multiple services (Calendar, Gmail, Drive, Search). Each tool earns its place by providing essential operations without being overwhelming, making it appropriate for the server's broad but manageable purpose.
The tool surface has notable gaps in coverage for the implied domain of Google services. For example, there is no tool for creating or updating Google Drive files (only reading and searching), and Calendar lacks a 'get_event' tool. While core workflows are partially covered, these omissions could lead to agent failures in more complex tasks.