Gists

Abstract

This article introduces a Node.js wrapper that dramatically reduces the startup time for the Gemini CLI when used with MCP servers built on Google Apps Script. This optimization enhances user experience by accelerating the initialization process, achieving a speed boost of approximately 15 times.

1. Introduction

The Model Context Protocol (MCP) is a vital open standard enabling AI agents to connect with external tools and data sources for complex, real-world tasks. To integrate the Gemini AI agent with Google Workspace, I developed two open-source tools: MCPApp, for managing the MCP server lifecycle, and ToolsForMCPServer, a suite of tools for interacting with services like Gmail and Drive. These are built with Google Apps Script for use with the Gemini CLI.

ToolsForMCPServer was updated to v1.0.29

• v1.0.29 (September 15, 2025)

  1. The following 5 tools were added. These tools provide the information for building the request body of Google APIs.

     • explanation_analytics_data_properties_runReport
     • explanation_analytics_data_properties_runRealtimeReport
     • explanation_manage_google_sheets_using_sheets_api
     • explanation_manage_google_docs_using_docs_api
     • explanation_manage_google_slides_using_slides_api

  2. The following 8 tools were updated.

     • get_google_sheet_object_using_sheets_api
     • manage_google_sheets_using_sheets_api
     • get_google_doc_object_using_docs_api
     • manage_google_docs_using_docs_api
     • get_google_slides_object_using_slides_api
     • manage_google_slides_using_slides_api
     • analytics_data_properties_runReport
     • analytics_data_properties_runRealtimeReport

You can see the detailed information here https://github.com/tanaikech/ToolsForMCPServer

ToolsForMCPServer was updated to v1.0.28

• v1.0.28 (September 11, 2025)

  1. The following 9 tools were added.

     • create_document_body_in_google_docs
     • remove_files_on_google_drive
     • maps_get_route
     • maps_convert_location_to_lat_lon
     • maps_convert_lat_lon_to_location
     • maps_create_map
     • explanation_create_maps_url
     • explanation_reference_generate_google_apps_script
     • explanation_reference_export_google_sheets_as_pdf

  2. A bug of a tool “convert_mimetype_of_file_on_google_drive” was removed. This is from this issue report.

You can see the detailed information here https://github.com/tanaikech/ToolsForMCPServer

Gists

Abstract

This article demonstrates integrating Google Maps with natural language using the Gemini CLI and an MCP server. This powerful combination allows users to automate complex location-based tasks, such as route planning and information retrieval, through simple, intuitive text-based prompts.

Introduction

The Gemini CLI, when paired with Model Context Protocol (MCP) servers, is a powerful tool for integrating various applications with natural language. When the MCP servers are built using Google Apps Script Web Apps, it becomes easy to integrate Google Workspace and other Google APIs with seamless authorization. This concept has been explored in several articles, which you can find here: Ref, Ref, Ref, Ref, Ref, Ref, Ref. This article introduces the integration of Google Maps and natural language using the Gemini CLI with an MCP server.

Gists

Abstract

Generating Google Apps Script (GAS) with Gemini CLI from natural language introduces security risks due to broad permissions. This report investigates a “Fake-Sandbox” using the gas-fakes library, translating GAS calls into granularly-scoped API requests to securely execute scripts created from user prompts.

Introduction

1. Background: Generative AI and the Challenge of Secure Script Execution

The emergence of Generative AI now makes it possible to generate executable scripts directly from natural language instructions, particularly through interfaces like the Gemini CLI. For locally executable languages such as JavaScript (Node.js) and Python, code generated from a simple prompt can be run directly. However, Google Apps Script (GAS) presents a unique challenge as it operates within Google’s server-side infrastructure. Executing locally generated GAS code requires the remote invocation of a server-side function via the scripts.run method of the Apps Script API. This process highlights the critical need for a sandbox environment to manage permissions effectively and mitigate the risks associated with executing code generated from natural language, which can sometimes produce unintended or insecure outcomes.

ToolsForMCPServer was updated to v1.0.27

• v1.0.27 (August 22, 2025)

  1. Following 6 tools for Google Analytics were added.

     • analytics_admin_accountSummaries_list: Retrieves a list of all Google Analytics accounts accessible by the current user
     • analytics_admin_properties_get: Get detailed information about a single Google Analytics property
     • analytics_data_properties_runReport: Fetches a custom report from a Google Analytics property
     • analytics_data_properties_runRealtimeReport: Generates a customized report of real-time event data from a Google Analytics property

You can see the detailed information here https://github.com/tanaikech/ToolsForMCPServer

Gists

Abstract

This report introduces a powerful method for automating Google Analytics tasks using the Gemini CLI and a custom MCP (Model Context Protocol) server built with Google Apps Script. This integration enables streamlined web page analysis through simple natural language commands, simplifying authorization and complex data retrieval workflows.

Introduction

Accessing and interpreting web analytics data often involves navigating complex interfaces and manual report generation. However, the emergence of natural language interfaces is changing this paradigm. Gemini CLI, when paired with MCP servers, allows users to orchestrate sophisticated, multi-step workflows using conversational commands. This creates a more intuitive and efficient way to interact with powerful services like Google Analytics.

Gists

Abstract

This document demonstrates a transformative method for unifying Google Workspace applications by using natural language. Through the integration of the Gemini CLI with MCP, this approach empowers users to intuitively manage Google Drive, Gmail, Google Calendar, Drive Activity, and Google People. Complex tasks and collaborative workflows are streamlined into simple, conversational text commands.

Introduction

In today’s dynamic, collaborative environments, managing document workflows, tracking changes, and coordinating team efforts can be fragmented and inefficient. This article introduces a powerful solution that unifies these processes by leveraging the Gemini CLI and MCP (Model Context Protocol). This integration breaks down the barriers between applications, allowing users to orchestrate complex tasks across Google Workspace with natural language prompts. Whether you’re finding a file in Drive, checking its comment history, retrieving contributor details from Contacts, and drafting a thank-you email in Gmail, these actions can now be executed from a single, conversational interface, dramatically boosting productivity.

Gists

Abstract

Automate Google Classroom management with natural language. This guide details using the Gemini CLI and an MCP server to streamline creating classes, managing assignments, and interacting with students.

Introduction

Unlock the power of natural language to command your Google Workspace. I’ve recently demonstrated how you can automate Google Workspace applications using simple, conversational commands through the Gemini CLI and the MCP (Model Context Protocol) server.

MCPApp was updated to v2.0.7

• v2.0.7 (August 6, 2025)

  1. Starting with v2.0.7, you can now selectively enable or disable the LockService.

     • By default, this library runs with the LockService enabled. To disable it, simply modify return new MCPApp.mcpApp({ accessKey: "sample" }) to return new MCPApp.mcpApp({ accessKey: "sample", lock: false }).
     • When the LockService is disabled (lock: false), asynchronous requests from clients like the Gemini CLI may see an increase in processing speed. However, it’s important to note that the maximum number of concurrent requests must not exceed 30. Please use this option with caution.

You can see the detail information here https://github.com/tanaikech/MCPApp