Article Summary
The article posits that LLMs' fundamental inability to effectively process and reason over large contexts renders protocols like Model Context Protocol (MCP) insufficient to solve core context management issues.
- It argues LLMs perceive context as a flat token sequence, largely ignoring structural cues like XML, contributing to information loss in longer inputs.
- The primary challenge lies in the LLM's intrinsic limitations in prioritizing information and deep semantic understanding, rather than the method of context delivery.
- LLMs' attention mechanisms are optimized for statistical relationships, not for discerning crucial information from noise across extended contexts.
- Consequently, for robust AI assistants and agentic systems, simply feeding more context via protocols or tools does not guarantee improved performance or reliable information utilization.