Google Sheet MCP SERVER

🚀 Google Sheet MCP Server

Google Sheet MCP Server is a powerful integration tool designed to expose Google Sheets functionality to your AI workflows, automation pipelines, and Apify Actors using the Model Context Protocol (MCP).

It enables seamless interaction with Google Sheets — including authentication, reading, writing, creating, and deleting sheets — directly from your code or automation flow.

🧠 Key Features

Function	Description
✅ get_credentials_tool	Initiates Google OAuth authentication and provides an authorization URL for connecting your Google account.
✅ save_auth_code	Saves the authorization code obtained from get_credentials_tool to securely enable access.
✅ list_spreadsheets	Lists all spreadsheets in your connected Google Drive.
✅ list_sheets	Retrieves all sheet (tab) names from a specific spreadsheet.
✅ read_range	Reads cell values from a specified range (e.g. Sheet1!A1:B2).
✅ write_range	Writes values to a defined range (e.g. Sheet1!A1:B2) using JSON arrays such as ["test","test@gmail.com","message"].
✅ clear_range	Clears data from a specified range (e.g. Sheet1!A1:B2).
✅ insert_row	Appends one or multiple rows to the end of a specific sheet. Args: spreadsheet_id — Spreadsheet ID sheet_name — Tab name row_values_json — JSON or 2D array (e.g. ["A","B","C"] or [["A","B"],["C","D"]]).
✅ create_sheet	Creates a new sheet/tab inside an existing spreadsheet.
✅ delete_sheet	Deletes a sheet/tab using its numeric sheet ID.
✅ create_spreadsheet	Creates a brand-new spreadsheet in Google Drive and returns its ID.
✅ delete_spreadsheet	Deletes a specified spreadsheet from your Google Drive.
✅ read_all_content	Reads all data from all sheets within a spreadsheet and returns JSON { "SheetName": [[...],[...]] }.
✅ read_sheet_content	Reads spreadsheet data selectively with filtering options for sheet names. Args: spreadsheet_id — Spreadsheet ID sheet_filter — Single name or JSON array (e.g. "Sheet1" or ["Sheet1","Data"]) include_all — Boolean flag to read all sheets or only matches.

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🧩 Ideal For

• AI tool developers using MCP (Model Context Protocol) for LLM-driven automations.
• Apify Actors that need to read/write structured data dynamically.
• Automation systems connecting Google Sheets with Apify, ChatGPT, or custom APIs.
• Data collection pipelines storing real-time results directly into Google Sheets.

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🔐 Authentication

Before running the server, you must authenticate your Google account:

1. Run the get_credentials_tool to obtain your authorization URL.
2. Visit the URL and approve access.
3. Copy the generated auth code and pass it to save_auth_code.

This grants permission for all subsequent API actions (read/write/create/delete).

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🧾 Example Output (JSON)

{
  "sheet_name": "Sheet1",
  "values": [
    ["Name", "Email", "Status"],
    ["Alice", "alice@example.com", "Active"],
    ["Bob", "bob@example.com", "Inactive"]
  ]
}

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🧠 Best Practices

• Always limit data ranges (Sheet1!A1:D1000) to improve performance.
• Use insert_row instead of write_range for scalable appends.
• Periodically refresh your credentials for long-running automations.
• Combine this MCP Server with Apify Actors for full workflow orchestration.

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📦 Output Format

Each tool method returns structured JSON, ideal for:

• AI agents,
• Automation pipelines,
• Data monitoring dashboards,
• Google Sheet → Apify → AI feedback loops.

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💡 Example Integration

This Actor can be directly integrated in Apify + Gemini AI or Apify + ChatGPT pipelines:

graph TD
A[Apify Scraper] --> B[Gemini AI MCP Server]
B --> C[Google Sheet MCP Server]
C --> D[Google Sheets Data Storage]

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🏁 Summary

Google Sheet MCP Server is the bridge between Google Sheets and AI-driven automations, providing ready-to-use methods for all essential operations — from authentication to spreadsheet management.

It’s the easiest way to bring Google Sheets I/O capabilities into your AI workflows, Apify Actors, or MCP pipelines.

Demo Videos

Check Demo video using TEST MCP Client

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