MCP Episode 2: Exploring MCP’s Structure, Sessions, and Context Blocks | Toni Maxx | June 2025

In the realm of artificial intelligence, current applications often manage “context” in a limited manner, primarily as long strings of conversation logs. While this approach can function effectively, it faces significant limitations:

  • No memory control: AI struggles to retain relevant information across interactions.
  • No role awareness: There’s often a lack of understanding about the roles of different entities in a conversation.
  • No separation of knowledge and action: AI systems frequently confuse what they know versus what they should do.

To address these challenges, the concept of MCP (Multi-layered Context Processing) emerges as a solution, advocating for a structured and layered approach to context management.

“It’s not about prompt tricks. It’s about thinking in systems.”

This innovative perspective encourages a deeper understanding of how AI can better mimic human-like interactions. For those intrigued by the evolving landscape of AI, exploring MCP could reveal transformative insights for the future.

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