Gists

Abstract

This article introduces a Google Apps Script-based Agent2Agent architecture to solve Tool Space Interference. While the provided demonstration utilizes a single server for testing purposes, the architecture is designed for distributed task execution. By running multiple category-specific A2A servers in parallel, users can achieve scalable, high-efficiency agent networks.

Introduction

As the Model Context Protocol (MCP) standardizes LLM connectivity, the Agent2Agent (A2A) paradigm is becoming essential for executing complex, multi-step tasks. However, integrating a high volume of tools often triggers “Tool Space Interference (TSI)"—a phenomenon where verbose metadata saturates context windows and degrades reasoning accuracy. Ref Ref Current industry guidelines suggest a “soft limit” of 20 functions per agent; exceeding this threshold frequently results in hallucinations and logic failures.

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.

UtlApp was updated to v1.0.12.

• v1.0.12 (December 12, 2025)

  1. Updated the method consolidateA1Notations. In the current version, the result is returned as an array. The array includes the combinations of the calculated result. The 1st element of the array is the best calculated result.

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

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

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.