My Acquisition Stack, Powered by MCPs & Claude

This analysis outlines a modernized mobile app acquisition stack that leverages Model Context Protocol (MCP) and Claude to automate complex workflows. The primary thesis is that integrating AI agents into daily operations—specifically for App Store Optimization (ASO) and Apple Search Ads (ASA)—significantly reduces the time required for routine tasks like keyword analysis, cannibalization checks, and bid management, allowing practitioners to focus on high-level strategy.

The workflow utilizes a dual-environment approach. Claude.ai, integrated with the AppsFlyer MCP, is used for high-level ROAS analysis across multiple markets and campaigns. Simultaneously, Claude Code is deployed within isolated local project folders to manage Apple Search Ads via API. To overcome the limitation of AI memory between sessions, the stack relies on structured markdown files—specifically a CLAUDE.md file—which provides the model with persistent context, including account structures, KPI benchmarks, and operational rules. This setup enables the automation of bulk tasks, such as generating CSVs for ad group updates and translated keyword management, which previously required hours of manual spreadsheet work.

The scope of this stack covers the management of four apps and three games across various client accounts. By replacing static Excel templates with interactive, AI-driven tools, the workflow has shifted from manual data entry to an automated, iterative process. While a fully closed-loop system—connecting keyword performance directly to downstream attribution—remains a future objective, the current implementation demonstrates a measurable increase in operational efficiency. The findings suggest that delegating repetitive analytical tasks to AI agents allows for more frequent optimization cycles and improved campaign performance, effectively transforming the role of the acquisition manager from a manual operator to a strategic architect.