文章摘要
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.