In the rapidly evolving landscape of multi-agent intelligence and Web3-native virtual societies, AIVille 2.0 introduces a breakthrough architectural integration: the Model Context Protocol (MCP). Designed as a coordination standard for large language models (LLMs) and autonomous agents, MCP empowers agents with persistent context awareness, protocol-governed behavior, and modular task execution.

This document offers a deep technical overview of MCP as deployed within AIVille's AI-driven architecture, detailing its multi-layered structure, orchestration logic, and the role of AIV as a governance and incentive mechanism. It is structured for engineers, AI system designers, and protocol architects exploring next-generation AI infrastructure.

Why MCP? The Limitations of Traditional LLM Systems

Conventional LLM usage follows a simple loop: prompt → model response → repeat. This design lacks statefulness, modularity, or interaction memory — all critical features for building intelligent systems.

However, real-world AI applications demand something far more advanced:

• Inputs from multiple sources (player behavior, plugin output, database queries, role-specific settings)
• Synthesis of multi-round historical context
• Task-oriented goals with persistent agent identity
• Distributed reasoning and collaboration across agents
• Output that is logged, structured, and capable of triggering follow-up behavior

In other words:

Player behavior + on-chain state + plugin calls + agent memory

→ contextual synthesis

→ goal resolution

→ tool/model orchestration

→ response logging

→ protocol follow-up

This is where Model Context Protocol (MCP) emerges — a standardized method for defining, dispatching, and executing agent-level cognition and behavior in a composable, explainable, and scalable way.

What is MCP? A Three-Layer Cognitive Execution Stack