Gists

Abstract

Google Sheets API now supports programmatic table management (create, delete, modify) as of April 29, 2025. This eliminates previous workarounds and enables direct control, including with Apps Script.

Introduction

Google Sheets tables can now be managed programmatically via the Sheets API, a significant update officially released on April 29, 2025. Ref I learned about this important development from Martin Hawksey’s Apps Script Pulse newsletter. Ref I am very grateful to Martin for bringing this to light. This update introduces the ability to programmatically manage tables directly through the Sheets API, enabling operations such as creating, deleting, and modifying tables and their properties. Previously, programmatic interaction with Sheets tables was limited and often required using workarounds for even simple management tasks, as explored in my earlier reports Ref and Ref. With this official API support, more robust and direct control is now possible. In this report, I will introduce how to manage tables on Google Sheets using the Sheets API, with examples implemented using Google Apps Script. It is worth noting, of course, that the Sheets API can also be used with other programming languages besides Apps Script.

Gists

Abstract

This report details transferring image data via Model Context Protocol (MCP) from Google Apps Script server to a Python/Gemini client, extending capabilities for multimodal applications beyond text.

Introduction

Following up on my previous report, “Building Model Context Protocol (MCP) Server with Google Apps Script” (Ref), which detailed the transfer of text data between the MCP server and client, this new report focuses on extending the protocol to handle image data. It introduces a practical method for transferring image data efficiently from the Google Apps Script-based MCP server to an MCP client. In this implementation, the MCP client was built using Python and integrated with the Gemini model, allowing for the processing and utilization of the transferred image data alongside text, thereby enabling more complex, multimodal applications within the MCP framework.

Gists

Abstract

This text introduces the Model Context Protocol (MCP) for standardizing AI interaction with external systems. It explores the potential of using Google Apps Script (GAS) to host an MCP server, leveraging GAS’s integration with Google Workspace for data access. A sample script demonstrates feasibility, highlighting the current absence of an official GAS SDK. The work aims to foster understanding and encourage SDK development.

Gists

Abstract

The report details a novel Gemini API method to analyze big data beyond AI context window limits, which was validated with Stack Overflow data for insights into Google Apps Script’s potential.

Introduction

Generative AI models face significant limitations when processing massive datasets, primarily due to the constraints imposed by their fixed context windows. Current methods thus struggle to analyze the entirety of big data within a single API call, preventing comprehensive analysis. To address this challenge, I have developed and published a detailed report presenting a novel approach using the Gemini API for comprehensive big data analysis, designed to operate effectively beyond typical model context window limits. Ref

Gists

Abstract

Generative AI faces limits in processing massive datasets due to context windows. Current methods can’t analyze entire data lakes. This report presents a Gemini API approach for comprehensive big data analysis beyond typical model limits.

Introduction

The rapid advancement and widespread adoption of generative AI have been remarkable. High expectations are placed on these technologies, particularly regarding processing speed and the capacity to handle vast amounts of data. While AI processing speed continues to increase with technological progress, effectively managing and analyzing truly large datasets presents significant challenges. The current practical limits on the amount of data that can be processed or held within a model’s context window simultaneously, sometimes around a million tokens or less, depending on the model and task, restrict direct comprehensive analysis of massive data lakes.

GeminiWithFiles was updated to v2.0.6

• v2.0.6 (April 23, 2025)

  1. A new method countTokens was added. Ref When this method is used, you can count tokens of the request.
  2. This pull request was reflected. Ref

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

Gists

Abstract

Learn how Gemini AI and Google Apps Script automate Google Slides generation. A developed application demonstrates this synergy, streamlining initial presentation drafting and showcasing AI’s automation potential within Google Workspace.

Introduction

The field of AI, particularly large language models like Google’s Gemini, is advancing rapidly. A powerful application of this technology involves integrating Gemini with Google Apps Script. Google Apps Script provides a seamless way to automate tasks across Google Workspace by natively handling authorization and interaction with services like Google Docs, Google Sheets, and Google Slides. By combining Gemini’s generative capabilities with Apps Script, sophisticated automations become accessible.

Gists

Abstract

Gemini 2.5 Pro Experimental enabled automated cargo ship stowage planning via prompt engineering, overcoming prior model limitations. This eliminates the need for complex algorithms, demonstrating AI’s potential in logistics.

Introduction

Recently, I encountered a practical business challenge: automating stowage planning through AI. Specifically, I received a request to generate optimal container loading plans for cargo ships, a task traditionally requiring significant manual effort and domain expertise. In initial tests, prior to the release of Gemini 2.5, I found that existing models struggled to effectively handle the complexities of this problem, including constraints like weight distribution, container dimensions, and destination sequencing. However, with the release of Gemini 2.5, I observed a significant improvement in the model’s capabilities. Utilizing the Gemini 2.5 Pro Experimental model, I successfully demonstrated the generation of viable stowage plans using only carefully crafted prompts. This breakthrough eliminates the need for complex, custom-built algorithms or extensive training datasets. The successful implementation involved providing the model with key parameters such as container dimensions, weights, destination ports, and ship capacity. This report details the methodology, prompt engineering, and results of my attempt to create automated stowage planning using Gemini 2.5 Pro Experimental, highlighting its potential to revolutionize logistics and shipping operations.

Gists

Abstract

Gemini and Google Apps Script automate project roadmap creation in Google Sheets, including Gantt charts, improving efficiency and agile planning.

Introduction

When initiating a new project, a comprehensive roadmap is crucial for successful execution. Previously, I meticulously crafted these roadmaps manually, a time-consuming process. However, leveraging the advanced capabilities of Google’s Gemini, I’ve significantly streamlined this workflow. Gemini now assists in generating detailed project roadmaps, enhancing efficiency and accuracy. To further automate this process, I developed a Google Apps Script that dynamically constructs these roadmaps directly within Google Sheets, complete with integrated Gantt charts. This script facilitates the rapid generation of diverse project roadmaps, enabling agile planning and adaptation for future endeavors. This report details the functionality and implementation of this script, demonstrating its potential to optimize project planning and visualization.

Gists

Description

This is a sample Google Apps Script designed to replace all instances of specific text within a Google Slides presentation, while simultaneously applying a desired text style. The built-in Presentation.replaceAllText() method within the Google Slides service is limited; it efficiently replaces text strings but lacks the functionality to modify text formatting during the replacement process. This limitation poses a challenge when aiming for styled text replacements. This report presents a detailed script solution that overcomes this constraint. The script iterates through all text elements within the slides, identifies matching text, and performs a replacement. Crucially, it also applies specified text formatting attributes, such as font, font size, color, and bold/italic settings, to the replaced text. This enhanced functionality allows users to maintain consistent and visually appealing presentations when automating text modifications. Furthermore, the script illustrates the use of TextRange methods for precise text manipulation and styling within Google Slides. This approach offers a more robust alternative to the standard replaceAllText() method, providing greater control over text replacement and formatting in Google Slides automation workflows.