Gists

Abstract

This article redefines Google Apps Script (GAS) as a central integration hub in the AI era. It introduces the forefront of Google Workspace automation, realized through the fusion of the Model Context Protocol (MCP), Agent2Agent (A2A), and the Gemini CLI ecosystem. I cover everything from data integration bridging local and cloud environments (RAG) and sandbox technologies for safely executing AI-generated GAS, to the coordination of autonomous agents on the newly released Google Antigravity. We will explore next-generation work styles and implementation methods where complex workflows are completed autonomously through simple natural language instructions.

Gists

Abstract

This article demonstrates how to integrate the Google Workspace Extension for Gemini CLI with Google Antigravity. It addresses a Model Context Protocol (MCP) tool naming incompatibility using a custom proxy script, enabling seamless, authenticated automation of Google Workspace tasks directly within the Antigravity IDE environment.

Introduction

Since its release, the Gemini CLI has been rapidly adopted across various development scenarios. Ref Its utility increased significantly with the introduction of Gemini CLI Extensions, which simplify the installation and management of Model Context Protocol (MCP) servers. Ref Most recently, the Google Workspace Extension for Gemini CLI was released by Google, providing an MCP server specifically designed to manage Workspace automation. Ref A distinct advantage of this extension is its streamlined authorization process—authentication runs automatically when the Gemini CLI is launched, making it highly efficient.

Gists

Abstract

Fields Builder for Google APIs is a client-side web application that streamlines constructing the fields parameter. It parses Discovery documents into interactive trees, enabling developers to visually select nested resources, implement Partial Response, and optimize API payload sizes without manual syntax errors.

Introduction

FieldsBuilderForGoogleAPIs is a specialized Web Application designed to streamline the construction of the fields parameter for Google APIs.

Gists

Abstract

This article explores automating Google Workspace by integrating Google Antigravity and Gemini 3.0 with Model Context Protocol (MCP) servers. We demonstrate how to overcome tool limits and utilize custom extensions to enable AI agents to securely execute scripts, manage files, and perform RAG-based tasks using private data.

Introduction

Google Antigravity and Gemini 3.0 are ushering in a new era of “Agent-First” development, transforming how we interact with cloud environments. Ref A key component of this evolution is the integration of Model Context Protocol (MCP) servers. When connected to Antigravity, these servers empower the architecture to resolve complex, multi-step tasks by granting the AI direct, standardized access to external tools and proprietary data.

Gists

Abstract

This article demonstrates a cutting-edge workflow for Google Apps Script development using Google Antigravity and Gemini 3.0. By integrating gas-fakes via the Model Context Protocol (MCP), we establish an environment where autonomous agents can generate, unit-test, and execute cloud-based scripts locally, revolutionizing the standard GAS development lifecycle.

Introduction

Google Antigravity has officially been released. Ref This is a revolutionary “Agent-first” IDE powered by Gemini 3, designed to empower autonomous AI agents to plan, code, and verify tasks across the Editor, Terminal, and Browser. It is anticipated that this platform will trigger a paradigm shift in how we develop applications and auto-generate comprehensive documentation, moving the industry from simple code completion to fully agentic workflows.

Overview

TableApp is a Google Apps Script library for managing Tables on Google Sheets.

Description

Recently, a new feature “Tables” was introduced to Google Sheets. Tables allow users to group data into structured tables with headers, filtering, and specific data types. While these can be managed via the Google Sheets API (v4), constructing the raw JSON requests for operations like creating, updating, and managing tables can be complex.

This library, TableApp, creates an object-oriented wrapper around the Google Sheets API, making it easy to manage Tables directly within Google Apps Script.

Gists

Abstract

This article introduces “TableApp,” a Google Apps Script library designed to simplify managing Google Sheets Tables. It addresses the complexity of the native Sheets API, providing an intuitive interface for creating, updating, and manipulating tables. Sample scripts and installation guides are included to ensure easy implementation.

Introduction

The introduction of Tables in Google Sheets has significantly enhanced data management capabilities. While these tables can be managed via the Sheets API, the process is often complex and verbose. I previously discussed this in my article, Managing Tables on Google Sheets using Google Apps Script.

Gists

Abstract

This article demonstrates how to create a unified file search for Gemini, integrating disconnected local files and Google Workspace data. Using a Google Apps Script-powered extension, users can directly ingest data from Drive, Sheets, and Gmail, enabling a powerful, context-aware RAG system.

Introduction

1. The Challenge of Data Silos

In modern enterprises, data is fragmented. It lives on local machines, in Google Drive, within Google Sheets, and across countless emails. While the Gemini CLI excels at file searches, it traditionally requires manually downloading cloud files to a local environment before they can be used. This workflow is inefficient, error-prone, and creates unnecessary operational overhead, preventing the creation of a truly comprehensive knowledge base for Retrieval-Augmented Generation (RAG).

Here introduces a new Gemini CLI extension that integrates File Search feature. This tool establishes a fully managed Retrieval-Augmented Generation (RAG) system directly on the command line.

The extension is designed to simplify the use of the Gemini API’s File Search, a powerful new feature that enables RAG grounded in personal or proprietary knowledge bases. While the underlying API requires scripting, this Node.js-built CLI extension allows users to seamlessly manage File Search stores and generate context-aware content grounded in their private documents without having to leave the terminal interface.

Gists

Abstract

This article introduces a Gemini CLI extension that integrates File Search feature. This tool provides a fully managed Retrieval-Augmented Generation (RAG) system directly in your command line, enabling content generation grounded in your private documents and data.

Introduction

The Gemini API recently introduced File Search, a powerful feature that enables Retrieval-Augmented Generation (RAG) using your own documents as a knowledge base. This allows you to generate content grounded in personal or proprietary information. While powerful, leveraging this via API calls requires scripting.