mcpServers and a2aServerAgentCardURLs.You can see the detail information here https://github.com/tanaikech/adk-gas
Configuring complex time-driven triggers in Google Apps Script—such as executing tasks exclusively on weekday mornings—is notoriously intractable programmatically and strictly impossible via the standard UI. TriggerApp mitigates this architectural friction through a declarative JSON engine, allowing developers to completely bypass granular date-math logic. Now, by embedding a native Model Context Protocol (MCP) server, we cross into a definitive paradigm shift. Developers can orchestrate complex, continuously looping GAS schedules using natural language via Generative AI (Vibe Coding), preserve the hard 20-trigger quota limit through an elegant recursive daisy-chain architecture, and bypass the strict 6-minute execution timeout by dynamically queuing future execution batches.
errgroup), freeze-proof TUI (mpb/v8), SIMD JSON parsing (goccy/go-json), native MCP server (ggsrun mcp), Shared Drives full-support, auto MIME-mapping, isolated fault tolerance, and OAuth2 loopback automation.You can check ggsrun at https://github.com/tanaikech/ggsrun.
v3.2.2 (May 27, 2026)
0 size from the Drive API) caused the progress bar spinners to hang infinitely. Bar completion is now strictly enforced (SetTotal(-1, true)) upon IO completion.defer statements, preventing memory leaks during connection errors.v3.2.1 (May 27, 2026)
Integrating autonomous AI agents into enterprise architectures exposes critical security and latency vulnerabilities. The Autonomous Google API Agent (AGAA) solves this by enforcing a deterministic, zero-trust execution framework directly within Google Apps Script (GAS). By merging GASADK, dynamic REST endpoint resolution via GoogleApiApp, and the Developer Knowledge API through the Model Context Protocol (MCP), AGAA executes complex cross-domain workflows exclusively via natural language. It autonomously researches API schemas, mitigates server-side formula latencies, handles recursive pagination, and mathematically enforces local Role-Based Access Control (RBAC). AGAA enables true “Vibe Coding” across all Google APIs—including Workspace, Analytics, and YouTube—without bloated client libraries.
GASADK is a groundbreaking Agent Development Kit tailored specifically for the resource-constrained Google Apps Script environment. Driven by a deterministic Planner-Executor-Synthesizer (PES) architecture, it effectively bypasses traditional async Node.js assumptions to overcome the absolute 6-minute execution limit and synchronous blocking networking (UrlFetchApp). Featuring dynamic DAG planning, temporal context anchoring, and seamless integrations with MCP, A2A, and Google Drive-based Agent Skills, GASADK transforms GAS into a bulletproof, high-performance runtime for autonomous AI agents. Install via the dedicated library key to orchestrate robust, zero-latency multi-agent systems today.
Google’s Agent Development Kit (ADK) revolutionizes autonomous AI agents, yet its standard Node.js-based asynchronous ReAct architecture is fundamentally incompatible with the restrictive, synchronous, and time-bound execution environment of Google Apps Script (GAS). To unlock enterprise-grade AI natively within Google Workspace, this paper introduces GASADK. By abandoning the cyclical ReAct loop in favor of a deterministic Planner-Executor-Synthesizer (PES) architecture, GASADK proactively manages execution constraints, synchronous network blocking, and payload limits. This framework successfully implements multi-agent orchestration, the Model Context Protocol (MCP), and Agent-to-Agent (A2A) communication directly within GAS, empowering developers to build highly resilient, serverless AI workflows that seamlessly manipulate Workspace applications.
invoke_agent functionality to Agent Skills.You can see the detail information here https://github.com/tanaikech/GeminiWithFiles
UrlFetchApp.fetchAll for much faster folder structure retrieval and folder creation.test.js) to allow safe, automated validation of the library’s functionality.You can see the detail information here https://github.com/tanaikech/MoveFolder
As an active researcher and developer in the AI ecosystem, I have seamlessly integrated Agent Skills into daily workflows using tools like Claude Code, Gemini CLI, and Antigravity. However, I observed a pervasive tendency in the developer community—and initially within my own practice—to treat these capabilities as opaque black boxes. There is a distinct lack of granular understanding regarding the internal execution steps and the recursive orchestration occurring within Generative AI models when a skill mandates subagent delegation.