Gists

Have you ever tried to read a massive pile of reports and summarize them in under 50 words? It’s hard. Now, imagine asking a cutting-edge Large Language Model (LLM)—like Gemini—to do it.

You might think AIs have perfect memories, but they don’t. When forced to aggregate information from dozens of documents under strict length constraints, AIs suffer from severe “memory loss” biases. They either ignore the middle of your documents or completely forget the older information they read first.

Gists

Abstract

Executing autonomous AI agent payloads in Google Workspace via the Apps Script API’s scripts.run method introduces severe security risks. This article presents a novel sandboxing proposal designed specifically for the scripts.run method, using ggsrun as the orchestrator to execute code safely and efficiently. By performing in-memory token replacement and uploading a separate, alphabetically-prioritized guard file, this approach achieves robust API-level containment. Guided by ggsrun’s automated backup and default rollback lifecycle (exe1), the remote environment is immediately restored, providing a clean, dependency-free security model for AI-driven Workspace automation.

Gists

Motivation

To be quite honest, “Hooks”—the shell commands we trigger at specific points when generative AI agents process tasks—were something I used blindly for a long time. Whenever colleagues asked me about them, I realized I lacked any real confidence in explaining how they actually work. However, when I migrated from Gemini CLI to the new Antigravity CLI, I noticed that the hooks system carried over. This felt like the right moment to stop guessing and finally develop a precise, deep understanding of the mechanism. I went back to the basics to analyze exactly how hooks operate under the hood and how we can use them effectively in the Antigravity environment. My goal is to demystify hooks so we can write them with confidence, and if this guide proves useful to your own workflows as well, I would be very glad.

Gists

Abstract

Nexus-MCP resolves “Tool Space Interference” in Large Language Models by aggregating multiple MCP servers into a single gateway. Utilizing a strictly deterministic 4-phase workflow—Discovery, Mapping, Schema Verification, and Bridged Execution—it prevents context saturation and tool hallucinations, enabling the use of massive tool ecosystems without sacrificing reasoning accuracy.

Introduction

The integration of Gemini CLI and Google Antigravity with the Model Context Protocol (MCP) has significantly expanded the capabilities of LLM-based agents. However, this expansion introduces a critical performance bottleneck. As the number of available tools grows, Large Language Models (LLMs) suffer from a measurable decline in reasoning accuracy and tool-selection reliability.

Gists

Abstract

This article introduces a major update to gas-fakes enabling dynamic loading of Google Apps Script libraries. This enhancement allows developers to build modular, maintainable Model Context Protocol (MCP) servers. We demonstrate this by integrating sophisticated library-based tools with Gemini CLI and Google Antigravity for seamless Google Workspace automation.

Introduction

I recently published an article titled “Power of Google Apps Script: Building MCP Server Tools for Gemini CLI and Google Antigravity in Google Workspace Automation.” In that piece, I demonstrated how to bridge the Model Context Protocol (MCP) with Google Workspace by implementing an MCP server using Google Apps Script (GAS) and gas-fakes. This successfully established a communication channel for sophisticated AI agents—such as the Gemini CLI and Google Antigravity—to interact directly with Workspace data.

Gists

Abstract

This article demonstrates how to build Model Context Protocol (MCP) tools directly using Google Apps Script. By leveraging the gas-fakes CLI, developers can execute Google Apps Script locally to automate Google Workspace via Gemini CLI and Google Antigravity, streamlining development and eliminating the overhead of dynamic tool creation.

Introduction

With the rapid advancement of generative AI, ensuring the security of executing AI-generated scripts is of paramount importance to prevent arbitrary code execution vulnerabilities. Addressing this, I previously published a secure sandbox environment for Google Apps Script (GAS) known as gas-fakes, which emulates the Apps Script environment locally. Ref

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

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.