sheets-mcp-server
Provides tools for reading, writing, appending, creating, and formatting Google Sheets using the Google Sheets API v4.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@sheets-mcp-serverCreate a new spreadsheet called 'Inventory' with tabs 'Stock' and 'Orders'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
sheets-mcp-server
An MCP server that lets AI agents read and write Google Sheets. Built with the Model Context Protocol and the Google Sheets API v4.
Tools
Tool | Description |
| Read data from a range in a Google Sheet |
| Write a 2D array of values to a range |
| Append rows to the end of a sheet |
| Create a new spreadsheet with custom tabs |
| Get spreadsheet metadata (title, sheets, dimensions) |
| Apply bold, background color, or text color to a range |
Related MCP server: Google Sheets Analytics MCP
Setup
1. Create a Google Cloud project
Go to the Google Cloud Console.
Click Select a project > New Project.
Give it a name and click Create.
2. Enable the Google Sheets API
In the Cloud Console, go to APIs & Services > Library.
Search for Google Sheets API and click Enable.
3. Create a service account
Go to APIs & Services > Credentials.
Click Create Credentials > Service account.
Give it a name (e.g.
sheets-mcp) and click Done.Click the new service account, go to the Keys tab.
Click Add Key > Create new key > JSON and download the file.
Save the JSON key file somewhere secure (e.g.
~/.config/sheets-mcp/service-account.json).
4. Share your spreadsheets
Open any Google Sheet you want the server to access, click Share, and add the service account email address (found in the JSON key file under client_email). Grant Editor access.
5. Set the environment variable
export GOOGLE_SERVICE_ACCOUNT_KEY=/path/to/service-account.jsonOr create a .env file in the project directory:
GOOGLE_SERVICE_ACCOUNT_KEY=/path/to/service-account.jsonInstallation
# With pip
pip install .
# Or with uv
uv pip install .Claude Desktop configuration
Add this to your Claude Desktop config file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"sheets": {
"command": "sheets-mcp",
"env": {
"GOOGLE_SERVICE_ACCOUNT_KEY": "/path/to/service-account.json"
}
}
}
}Or if running from the source directory with uv:
{
"mcpServers": {
"sheets": {
"command": "uv",
"args": ["run", "--directory", "/path/to/sheets-mcp", "sheets-mcp"],
"env": {
"GOOGLE_SERVICE_ACCOUNT_KEY": "/path/to/service-account.json"
}
}
}
}Example usage
Once connected, an AI agent can use the tools like this:
Read data:
Read cells A1 through D10 from spreadsheet
1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgVE2upms
Write data:
Write these sales figures to Sheet1!A1:C3: [["Product", "Q1", "Q2"], ["Widget", 150, 230], ["Gadget", 320, 180]]
Create a new spreadsheet:
Create a new spreadsheet called "Project Tracker" with tabs "Tasks", "Timeline", and "Budget"
Format headers:
Bold the header row A1:E1 and give it a blue background (#4285F4) with white text (#FFFFFF)
Append rows:
Append these new entries to the bottom of the log in Sheet1: [["2026-03-13", "Completed review", "Alice"]]
License
MIT
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