The article by Tommy Elizaga discusses the challenges and solutions associated with the "Ballooning Context Problem" in the Model Context Protocol (MCP) era, particularly in the context of Large Language Models (LLMs) and their applications. MCP has become a popular protocol for integrating external data into models, offering benefits like separating context logic from application logic and improving workflow reliability. However, the influx of excessive context data can overwhelm LLMs, leading to issues like token bloat, relevance decay, and increased latency. The author emphasizes the importance of effective context engineering, which involves curating, compressing, and prioritizing data to ensure that only pertinent information reaches the model. Key strategies include context deduplication, summarization, prioritization, and quarantining to maintain clarity and efficiency. The article warns against common anti-patterns such as blind vector stuffing and indiscriminately providing all available context, which can degrade model performance. Through careful context management, the goal is to enhance model outputs and efficiency, as demonstrated by improvements in their own MCP client for code reviews.