MCPApp was updated to v1.0.2

• v1.0.2 (May 29, 2025)

  1. From v1.0.2, in order to use MCPApp as a library, LockService is given.

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

A2AApp was updated to v2.0.1

• v2.0.1 (May 29, 2025)

  1. From v2.0.1, A2AApp can also be used as a library. Ref

You can see the detail information here https://github.com/tanaikech/A2AApp

Gists

Abstract

This report details the Agent2Agent (A2A) network built with Google Apps Script’s Web Apps. It facilitates communication between diverse AI agents, overcoming platform limitations. Key improvements include parallel task execution with asynchronous processes and enhanced security through secure access token handling and user-specific Web App availability, demonstrating a robust and secure A2A implementation.

Introduction

This report details an updated implementation of Agent2Agent (A2A), an open protocol designed to enable communication and collaboration between diverse AI agents. The goal of A2A is to overcome limitations of isolated platforms, allowing AI agents to work together on complex tasks while maintaining their internal structures. I recently published a report titled “Building Agent2Agent (A2A) Server with Google Apps Script”. Ref This updated report focuses on successfully creating an A2A network using Google Apps Script’s Web Apps functionality.

GeminiWithFiles was updated to v2.0.10

• v2.0.10 (May 21, 2025)

  1. Implemented the parallel function calling. Ref

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

Gists

Abstract

Exploring Agent2Agent (A2A) protocol implementation in Google Apps Script seamlessly allows AI agents to access Google Workspace data and functions. This could enable complex workflows and automation, overcoming platform silos for integrated AI applications.

Introduction

Agent2Agent (A2A) is a proposed open protocol facilitating communication and collaboration among diverse AI agents, aiming to overcome platform silos and enable complex tasks while preserving agent opacity. This report examines the feasibility of implementing a core A2A server component using Google Apps Script within Google Workspace. Such an implementation could seamlessly allow AI agents to securely access and utilize data and functionalities across Google services like Docs, Sheets, and Gmail via a standardized protocol. This would enable sophisticated AI-powered workflows and automation directly linked to user data. A sample script demonstrates the technical potential despite the current lack of a dedicated Apps Script SDK for A2A. While acknowledging potential Apps Script limitations, such as execution time, this exploratory approach remains valuable for developing internal or user-centric AI applications and integrations within Google Workspace. A successful demonstration could potentially highlight the capabilities of Google Apps Script.

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.

Introduction

Recently, the Model Context Protocol (MCP) has emerged as a standard protocol for connecting AI applications with third-party systems and data sources. Acting like a universal adapter or “USB-C for AI,” the MCP standardizes how AI models can dynamically discover and interact with external resources, tools, and context, often incorporating mechanisms for user consent and secure communication. The detailed specification of this protocol can be confirmed at the official site. Ref

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.