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Google Enters the MCP Race

On November 10, 2024, Google announced official Model Context Protocol support for Gemini 2.0. This makes Google the third major AI provider to embrace MCP, following Anthropic and OpenAI. The announcement includes 12 official MCP servers covering Google's entire ecosystem.

Official Google MCP Servers

Google Workspace MCP (Gmail, Drive, Calendar, Docs)
Google Cloud Platform MCP (BigQuery, Cloud Storage, Compute)
Google Search MCP (Real-time web search integration)
YouTube MCP (Video search, transcripts, analytics)

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Getting Started with Gemini MCP

Installation

Google provides official MCP servers through the Google Cloud SDK. Install them with a single command:

# Install Google Cloud SDK with MCP support
curl https://sdk.cloud.google.com | bash

# Install official Google MCP servers
gcloud components install mcp-servers

# Authenticate
gcloud auth login

# List available MCP servers
gcloud mcp servers list

Basic Usage with Gemini 2.0

Connect Gemini to Google Workspace using the official MCP server:

import google.generativeai as genai
from google.cloud import mcp

# Configure Gemini with MCP
genai.configure(api_key="your-api-key")

# Initialize Workspace MCP server
workspace_mcp = mcp.WorkspaceServer(
    credentials="path/to/credentials.json"
)

# Create Gemini model with MCP tools
model = genai.GenerativeModel(
    model_name="gemini-2.0-pro",
    tools=[workspace_mcp.get_tools()]
)

# Query your Gmail
response = model.generate_content(
    "Summarize all unread emails from last week"
)

print(response.text)
# Gemini automatically queries Gmail via MCP
# and returns a structured summary

Google Workspace MCP Server

Gmail Integration

The Gmail MCP server provides full access to your email with natural language queries:

# Search and analyze emails
response = model.generate_content(
    "Find all emails about the Q4 budget and create a summary"
)

# Send emails
response = model.generate_content(
    "Send an email to team@company.com with subject 'Meeting Tomorrow' "
    "and body 'Don't forget our 2pm standup'"
)

# Manage labels and filters
response = model.generate_content(
    "Create a filter to automatically label emails from clients as 'Important'"
)

Google Drive Integration

Access and manipulate files in Google Drive:

# Search files
response = model.generate_content(
    "Find all spreadsheets modified in the last month"
)

# Read and analyze documents
response = model.generate_content(
    "Read the file 'Q3 Report.docx' and extract key metrics"
)

# Create new files
response = model.generate_content(
    "Create a new Google Doc titled 'Meeting Notes' with today's date"
)

Calendar Integration

Manage your calendar with natural language:

# Check availability
response = model.generate_content(
    "Am I free tomorrow afternoon?"
)

# Schedule meetings
response = model.generate_content(
    "Schedule a 1-hour meeting with john@company.com "
    "next Tuesday at 2pm titled 'Project Review'"
)

# Find conflicts
response = model.generate_content(
    "Show me all scheduling conflicts for next week"
)

Google Cloud Platform MCP

BigQuery Integration

Query your data warehouse with natural language:

from google.cloud import mcp

# Initialize BigQuery MCP server
bigquery_mcp = mcp.BigQueryServer(
    project_id="your-project-id"
)

model = genai.GenerativeModel(
    model_name="gemini-2.0-pro",
    tools=[bigquery_mcp.get_tools()]
)

# Natural language queries
response = model.generate_content(
    "What was our total revenue in Q3 2024? "
    "Show me a breakdown by product category"
)

# Gemini automatically:
# 1. Translates to SQL
# 2. Executes on BigQuery via MCP
# 3. Formats results in natural language

print(response.text)
# "Your total Q3 2024 revenue was $2.4M:
#  - Product A: $1.2M (50%)
#  - Product B: $800K (33%)
#  - Product C: $400K (17%)"

Pro Tip: Gemini can automatically join tables, aggregate data, and create visualizations from BigQuery results

Cloud Storage Integration

Manage files in Google Cloud Storage:

# List and search files
response = model.generate_content(
    "List all CSV files in the 'data-exports' bucket from last week"
)

# Read and analyze files
response = model.generate_content(
    "Read the file gs://my-bucket/sales-data.csv and calculate average order value"
)

# Upload files
response = model.generate_content(
    "Upload the local file 'report.pdf' to gs://reports-bucket/"
)

Google Search MCP Server

Real-Time Web Search

The Google Search MCP server gives Gemini access to real-time web data:

from google.cloud import mcp

# Initialize Search MCP server
search_mcp = mcp.SearchServer(
    api_key="your-search-api-key"
)

model = genai.GenerativeModel(
    model_name="gemini-2.0-pro",
    tools=[search_mcp.get_tools()]
)

# Real-time information
response = model.generate_content(
    "What's the current stock price of GOOGL and how has it changed today?"
)

# News and trends
response = model.generate_content(
    "Summarize the latest news about AI regulation in the EU"
)

# Fact checking
response = model.generate_content(
    "Verify this claim: 'Python is the most popular programming language in 2024'"
)

Search Features

Real-time web search with source citations
News search with date filtering
Image search and analysis
Shopping search for product comparisons

Pricing and Availability

Google's MCP servers are available now in public beta. Pricing follows the standard Gemini API rates with no additional MCP fees:

Service	Free Tier	Paid Tier
Workspace MCP	1,000 calls/day	Unlimited
Cloud Platform MCP	500 calls/day	Unlimited
Search MCP	100 searches/day	$5 per 1,000 searches

Note: MCP calls count toward your Gemini API quota but don't incur additional charges beyond standard API pricing

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