Google Sheets MCP Server

What does “Google Sheets” MCP Server do?

The Google Sheets MCP (Model Context Protocol) Server is a TypeScript-based tool that enables AI assistants to securely interact with Google Sheets through well-defined, typed tools. By acting as a bridge between AI agents and the Google Sheets/Drive API, it allows for robust automation and data manipulation workflows. The server supports both OAuth2 and Service Account authentication, making it flexible for different security requirements. Its primary role is to empower developers and AI workflows to perform tasks such as creating spreadsheets, reading and writing cell data, managing sheets and tabs, and sharing documents—all programmatically and securely. This integration streamlines operations like database-style queries, collaborative editing, and workflow automation directly within Google Sheets, enhancing productivity and AI-driven development processes.

List of Prompts

No explicit prompt templates are documented in the repository.

List of Resources

No explicit resources are documented in the repository.

List of Tools

• create
  Creates a new spreadsheet and optionally moves it to a specified Drive folder.
• listSheets
  Lists all sheet tabs in a given spreadsheet.
• renameSheet
  Renames an existing sheet tab.
• createSheet
  Adds a new sheet tab to a spreadsheet.
• spreadsheetInfo
  Fetches metadata (title, sheet IDs, grid properties) for a spreadsheet.
• listSpreadsheets
  Lists all spreadsheets in your configured Drive folder or My Drive.
• shareSpreadsheet
  Shares a spreadsheet with users (reader/commenter/writer) and sends notifications.
• sheetData
  Reads cell values from a sheet and range (or the whole sheet).
• updateCells
  Writes a 2D array of values into an A1-style range.
• batchUpdate
  Applies multiple range updates in a single request.
• addRows / addColumns
  Inserts rows or columns at a specified index.
• copySheet
  Copies a sheet tab between spreadsheets, with an option to rename.

Use Cases of this MCP Server

• Automated Spreadsheet Creation and Management
  Seamlessly create new spreadsheets, add or rename sheets/tabs, and organize files within Google Drive, all through automated workflows.
• Data Extraction and Update
  Read and write large data sets from/to Google Sheets, making it ideal for ETL pipelines, reporting, and dashboard automation.
• Collaborative Access and Sharing
  Programmatically share spreadsheets with different users and set permissions, facilitating team collaboration and automated notifications.
• Bulk Operations and Data Manipulation
  Perform batch updates, insert multiple rows/columns, and apply changes to large sheets efficiently, saving time on repetitive tasks.
• Sheet Duplication and Organization
  Copy sheets between spreadsheets for templating, backup, or organizational workflows, supporting scalable operations across teams.

How to set it up

Windsurf

1. Ensure you have Bun (v1.0+) installed, and set up Google Cloud credentials (either OAuth2 or Service Account).
2. Clone the repository and install dependencies:
   git clone https://github.com/rohans2/mcp-google-sheets.git
cd mcp-google-sheets
bun install
3. Configure environment variables as shown below.
4. In the Windsurf configuration file, add the MCP server using a JSON snippet.
5. Save your configuration and restart Windsurf.

Example JSON configuration:

{
  "mcpServers": {
    "google-sheets": {
      "command": "bun",
      "args": ["index.ts"]
    }
  }
}

Securing API keys with environment variables:

{
  "command": "bun",
  "args": ["index.ts"],
  "env": {
    "CREDENTIALS_CONFIG": "BASE64_ENCODED_CONFIG",
    "EMAIL_ID": "your-email@example.com"
  }
}

Claude

1. Install prerequisites and clone the repository.
2. Set up Google Cloud authentication and environment variables.
3. Locate and edit Claude’s MCP configuration file.
4. Add the Google Sheets MCP server with the configuration snippet.
5. Restart Claude and verify the connection.

Example JSON configuration:

{
  "mcpServers": {
    "google-sheets": {
      "command": "bun",
      "args": ["index.ts"]
    }
  }
}

Cursor

1. Make sure Bun is installed and credentials are configured.
2. Download and set up the repository.
3. Edit the Cursor MCP configuration file.
4. Include the Google Sheets MCP server entry.
5. Save changes and restart Cursor to activate the server.

Example JSON configuration:

{
  "mcpServers": {
    "google-sheets": {
      "command": "bun",
      "args": ["index.ts"]
    }
  }
}

Cline

1. Install Bun and set up your Google Cloud project credentials.
2. Clone the repository and install all required dependencies.
3. Access and edit Cline’s MCP server configuration.
4. Register the Google Sheets MCP server using the JSON format.
5. Restart Cline to apply the setup.

Example JSON configuration:

{
  "mcpServers": {
    "google-sheets": {
      "command": "bun",
      "args": ["index.ts"]
    }
  }
}

Securing API keys using environment variables:

{
  "command": "bun",
  "args": ["index.ts"],
  "env": {
    "CREDENTIALS_CONFIG": "BASE64_ENCODED_CONFIG",
    "EMAIL_ID": "your-email@example.com"
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "google-sheets": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “google-sheets” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

This MCP server is well-documented for setup and offers a robust set of tools for Google Sheets automation. However, it lacks explicit MCP prompt templates and resources, and there is no mention of sampling or roots support. Its strong tool coverage and clear security guidance make it valuable for sheet automation, but it could be improved by adding prompt/resource documentation and clarifying sampling/roots support.

MCP Score