Gists

Abstract

This report details two methods for processing files using the Gemini CLI and a Google Apps Script MCP server: direct Base64 encoding and indirect transfer via the Google Drive API using ggsrun. The direct method proved ineffective due to token limits. The recommended approach, leveraging ggsrun, allows for efficient, scalable file transfers by using file IDs instead of embedding content within the prompt, enabling advanced automation capabilities.

Gists

Abstract

The Gemini CLI provides a powerful command-line interface for interacting with Google’s Gemini models. By leveraging the Model Context Protocol (MCP), the CLI can be extended with custom tools. This report explores the integration of the Gemini CLI with an MCP server built using Google Apps Script Web Apps. We demonstrate how this combination simplifies authorization for Google Workspace APIs (Gmail, Drive, Calendar, etc.), allowing Gemini to execute complex, multi-step tasks directly within the Google ecosystem. We provide setup instructions and several practical examples showcasing how this integration unlocks significant potential for automation and productivity enhancement.

Gists

Abstract

The Gemini CLI can be integrated with Google Workspace via Google Apps Script to securely access personal data, enabling powerful automations like email summaries and calendar management.

Introduction

The recently released Gemini CLI is a powerful command-line interface for interacting with Google’s Gemini models and cloud resources. Ref While powerful on its own, its utility can be significantly enhanced by connecting it to a user’s personal Google resources, such as Google Sheets, Docs, Slides, Gmail, and Calendar.

Gists

Introduction

The Gemini API recently introduced the URL context tool, a feature designed to allow the model to directly fetch and utilize content from specified URLs to ground its responses. Ref

This report provides a practical demonstration of this tool’s capabilities. We will investigate its impact on two critical aspects of AI model interaction: the accuracy of the generated response and the total token consumption, which directly affects API costs.

Description

I am thrilled to announce a significant update to the Taking Advantage of Web Apps with Google Apps Script repository! Now at Version 2.0.0, this comprehensive guide has been extensively revised and expanded to provide unparalleled insights and practical guidance for developers leveraging Google Apps Script Web Apps.

This major overhaul introduces a wealth of new and enhanced content, designed to empower you with a deeper understanding and more advanced capabilities.

Gists

Overview

Google Sheets now supports managing smart chips via its API, enabling retrieval and placement with Apps Script and other languages.

Description

Smart chips in Google Sheets and Docs are a powerful feature designed to enhance collaboration and information sharing. While there historically haven’t been built-in Google Apps Script methods to manage these chips, a workaround was previously developed to address this limitation (Ref).

Gists

Abstract

A new unified Google Apps Script now deploys both Model Context Protocol (MCP) and Agent2Agent (A2A) networks as a single server, streamlining AI model integration for Google Workspace users.

Introduction

The rapid growth of generative AI has led to increasing integration between AI models, exemplified by protocols like the Model Context Protocol (MCP) and Agent2Agent (A2A) Protocol. Recently, I released MCPApp and A2AApp, which establish the MCP and A2A networks using Google Apps Script. Ref and Ref This approach offers significant advantages for users of Google Workspace and Google APIs, as it enables seamless authorization and integration of these resources directly within the applications.

Gists

Abstract

This article announces that the Gemini API’s Python client library now supports “growing image” generation, a feature previously unavailable. Sample scripts for Python and Node.js are provided to demonstrate this new capability.

Introduction

I recently published an article, “Generate Growing Images using Gemini API,” which detailed a method for progressively generating images. At the time of publication, the official Python client library for the Gemini API lacked the necessary functionality to fully implement this feature, preventing Python users from easily replicating the “growing image” effect.

UtlApp was updated to v1.0.11.

• v1.0.11 (June 14, 2025)

  1. The method getInfFromBlob was added.

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

UtlApp was updated to v1.0.10.

• v1.0.10 (June 13, 2025)

  1. The method getMP3Tag was added.

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