Mcp時代におけるコンテキスト肥大化への対応

In the era of Model Context Protocol (MCP), context engineering has evolved to efficiently provide external data to production models, yet challenges arise with the management of burgeoning context. While MCP offers a streamlined method for delivering context by separating context logic from application logic and reducing complexity in workflows, the abundance of context can lead to issues such as token bloating, reduced relevance, and increased latency. Effective context engineering requires careful filtering, prioritization, and transformation of data before it reaches the model, ensuring high-signal, low-noise inputs. Poorly managed context can result in errors, inefficiencies, and misleading outputs, while well-engineered context enhances model performance and output accuracy. The balance of providing sufficient context without overwhelming the system is crucial, with strategies like deduplication, prioritization, and isolation of context proving beneficial. This approach allows for more focused and efficient outputs in workflows, as demonstrated by improvements seen in the author's own MCP client for code review.

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