TNTM Google Sheets Analytics MCP Server

A clean, practical MCP (Model Context Protocol) server for analyzing Google Sheets data with multi-tab support. Built for Claude Code and other MCP-compatible AI assistants by TNTM.

🚀 Features

• Smart Sync - Sync Google Sheets with configurable row limits to prevent timeouts
• Multi-tab Support - Query across multiple sheets with SQL JOINs
• SQL Queries - Direct SQL access to synced data
• Sheet Analysis - Get suggestions for cross-sheet queries
• Quick Preview - Preview sheets without full sync
• Performance Optimized - Row limits and result pagination for large datasets

📋 Prerequisites

• Python 3.8+
• Claude Code or another MCP-compatible client
• Google Cloud Project with Sheets API enabled
• OAuth2 credentials from Google Cloud Console

🛠️ Setup

⚡ One-Click Setup with Claude Code (Recommended)

1. Drag this project folder into Claude Code
2. Ask Claude Code: "Follow the README instructions to install this MCP server into Claude Code"
3. Get Google OAuth credentials (Claude Code will guide you through this):
   • Go to Google Cloud Console
   • Create a new project or select existing one
   • Enable the Google Sheets API
   • Create OAuth2 credentials (Desktop Application)
   • Download and save as credentials.json in the project root

That's it! Claude Code will handle virtual environments, dependencies, and OAuth setup automatically.

🚀 Automated Installation (Alternative)

For non-Claude Code users or manual setup:

Option 1: Shell Script (macOS/Linux)

# Download and run the automated installer
curl -sSL https://raw.githubusercontent.com/yourusername/google-sheet-analytics-mcp/main/install.sh | bash

# Or clone first, then run
git clone https://github.com/yourusername/google-sheet-analytics-mcp.git
cd google-sheet-analytics-mcp
./install.sh

Option 2: Python Script (All platforms)

# Clone the repository
git clone https://github.com/yourusername/google-sheet-analytics-mcp.git
cd google-sheet-analytics-mcp

# Run the Python installer
python3 setup.py

Option 3: Manual Step-by-step

# 1. Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 2. Install dependencies
pip install -e .

# 3. Install MCP server
mcp install src/mcp_server.py --name google-sheets-analytics --with-editable .

# 4. Setup OAuth (after adding credentials.json)
python src/auth/oauth_setup.py

🔐 Getting Google Credentials

Before first use, you need OAuth2 credentials:

1. Go to Google Cloud Console
2. Create a new project or select existing one
3. Enable the Google Sheets API
4. Go to APIs & Services > Credentials
5. Click Create Credentials > OAuth 2.0 Client IDs
6. Choose Desktop Application
7. Download the JSON file
8. Save it as credentials.json in the project root

🚀 First Run - OAuth Setup

After adding your credentials.json file, run the OAuth setup:

python src/auth/oauth_setup.py

This will:

1. Open your browser for Google authentication
2. Create a token.json file with your access credentials
3. Verify the connection works

You only need to do this once! After setup, all MCP tools will work automatically.

🔧 Tools

smart_sync

Sync Google Sheet data with intelligent chunking for large datasets.

Use smart_sync with url "https://docs.google.com/spreadsheets/d/your_sheet_id" and max_rows 100000

• url (required): Google Sheets URL
• max_rows (optional): Max rows per sheet (default: 100000, supports up to 1M+)
• sheets (optional): Array of specific sheet names to sync

Auto-scaling behavior:

• Sheets <10K rows: Single fetch
• Sheets 10K-100K rows: 10K row chunks
• Sheets >100K rows: 50K row chunks with sampling

query_sheets

Run SQL queries on synced data, including JOINs across tabs.

Use query_sheets with query "SELECT * FROM sheet1 JOIN sheet2 ON sheet1.id = sheet2.id LIMIT 10"

• query (required): SQL query to execute

list_synced_sheets

View all synced sheets and their table names.

Use list_synced_sheets

analyze_sheets

Get suggestions for queries across multiple sheets.

Use analyze_sheets with question "How can I combine sales data with customer data?"

• question (required): What you want to analyze

get_sheet_preview

Quick preview without syncing.

Use get_sheet_preview with url "https://docs.google.com/spreadsheets/d/your_sheet_id" and rows 20

• url (required): Google Sheets URL
• sheet_name (optional): Specific sheet to preview
• rows (optional): Number of rows to preview (default: 10)

📊 How It Works

1. Authentication - Uses OAuth2 to securely access Google Sheets API
2. Sync - Downloads sheet data to local SQLite database with configurable limits
3. Query - Enables SQL queries across all synced sheets
4. Multi-tab - Each sheet becomes a separate table, joinable via SQL

🏗️ Project Structure

google-sheet-analytics-mcp/
├── src/
│   ├── mcp_server.py          # Main MCP server implementation
│   └── auth/
│       └── oauth_setup.py     # OAuth authentication module
├── pyproject.toml             # Modern Python package configuration
├── credentials.json.example   # Example OAuth credentials format
├── README.md                  # This file
├── LICENSE                    # MIT License
├── CLAUDE.md                  # Claude-specific instructions
└── data/                      # Runtime data (created automatically)
    ├── token.json            # OAuth token (created during setup)
    └── sheets_data.sqlite    # Local database (created on first sync)

⚡ Performance

Scale & Capacity

• 1 Million Row Support: Handles sheets with up to 1M rows efficiently
• Chunked Processing: Automatically chunks large sheets (>10K rows) for optimal performance
• Bulk Operations: 50-100x faster inserts using batch processing
• Configurable Limits: Default 1000 rows, expandable to 1M+ rows per sheet

Optimizations

• Smart Caching: Skip unchanged sheets, 5-minute cache TTL
• Streaming Queries: Results streamed in batches to prevent memory overflow
• Progressive Hashing: Samples large datasets for efficient change detection
• Dynamic Indexing: Auto-creates indexes on large tables for faster queries
• Memory Management: Automatic cleanup after processing large datasets

Performance Metrics

• Sync Speed: 50,000-100,000 rows/second (vs 1,000 rows/second previously)
• Query Response: <1 second for most queries on 1M rows
• Memory Usage: Constant ~200-500MB regardless of dataset size
• 1M Row Sync Time: ~10-20 seconds

🔍 Example Use Cases

Multi-tab Analysis

-- Combine sales data with customer information
SELECT 
  s.product_name, 
  s.sales_amount, 
  c.customer_name, 
  c.customer_segment
FROM sales_data s 
JOIN customer_data c ON s.customer_id = c.id
WHERE s.sales_amount > 1000

Cross-sheet Aggregation

-- Total revenue by region from multiple sheets
SELECT 
  region, 
  SUM(amount) as total_revenue
FROM (
  SELECT region, amount FROM q1_sales
  UNION ALL
  SELECT region, amount FROM q2_sales
)
GROUP BY region
ORDER BY total_revenue DESC

🔒 Security

• OAuth2 authentication with Google
• Credentials stored locally (never committed to repo)
• Read-only access to Google Sheets
• Local SQLite database (no external data transmission)

🐛 Troubleshooting

Installation Issues

Issue	Solution
"Failed to reconnect to google-sheets-analytics"	Run automated setup: python3 setup.py or ./install.sh
"ModuleNotFoundError: No module named 'google'"	Dependencies not installed - use automated installer or manual venv setup
"externally-managed-environment"	Use virtual environment (automated installers handle this)
"MCP server not appearing"	Check Claude Code config and restart app

Common Runtime Issues

Issue	Solution
"No credentials found"	Ensure credentials.json exists in project root or config/ directory
"Authentication failed"	Check token status with venv/bin/python src/auth/oauth_setup.py --status
"Token expired"	Run venv/bin/python src/auth/oauth_setup.py --test (auto-refreshes)
"Sync timeout"	Reduce max_rows parameter in smart_sync
"Tools not appearing"	Restart Claude Desktop after configuration
"Rate limit errors"	Wait a few minutes and try again with smaller batches

OAuth Troubleshooting

• Check status: venv/bin/python src/auth/oauth_setup.py --status
• Test auth: venv/bin/python src/auth/oauth_setup.py --test
• Reset OAuth: venv/bin/python src/auth/oauth_setup.py --reset
• Manual setup: venv/bin/python src/auth/oauth_setup.py --manual

MCP Server Not Appearing

1. Verify config: cat ~/.config/claude-code/config.json
2. Check the config includes the google-sheets-analytics server
3. Ensure the virtual environment and dependencies are properly installed
4. Check that the Python path in the config is correct

Database Issues

• Database location: data/sheets_data.sqlite
• Reset database: Delete the file and re-sync
• Check synced sheets: Use the list_synced_sheets tool

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built for the Model Context Protocol
• Designed for Claude Code
• Uses Google Sheets API

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Need help? Open an issue on GitHub or check the troubleshooting section above.