This project explores using a Vision-Language Model (VLM) with in-context learning to enhance strategic planning in Pico Park, a cooperative multiplayer puzzle game. Without requiring fine-tuning, the VLM will analyze game visuals and generate strategic recommendations to improve coordination for smaller agents, whether AI-driven or human-controlled. The approach involves curating representative gameplay scenarios, designing effective prompts, and utilizing few-shot learning techniques.