The Model Context Protocol (MCP) represents a new coordination standard for autonomous agents in AIVille 2.0. This article examines its features and significance.
Why MCP?
Current implementations of large language models (LLMs) often rely on a simple loop: prompt → model response → repeat. This leads to a lack of state and memory, limiting the capabilities for building intelligent systems. Real-world AI applications demand advanced features such as:
* Inputs from multiple sources (player behavior, plugin output, database queries, role-specific settings) * Synthesis of historical context * Task-oriented goals with persistent agent identity * Distributed reasoning and collaboration among agents * Logged output and structured responses.
Structure of MCP
MCP consists of three layers:
1. **Context Layer** — manages the agent's current state, memory, and input streams, allowing for structured hierarchical models of the world. 2. **Protocol Layer** — defines task schemas that are formalized as machine-readable contracts describing agent actions, inputs, and constraints. 3. **Execution Layer** — maps protocol contracts to actual system capabilities, providing model routing, tool invocation, and asynchronous task orchestration.
AIVille Integration and the Role of AGT
AIVille deploys MCP as the core coordination layer behind all autonomous agent behavior. Every AI character, from Mayor Logan to Merchant Lucas, executes actions based on active protocol instances and contextual memory. The AGT token serves for:
* Role permissions * Execution incentives * Collective governance through protocol voting.
The Model Context Protocol transforms how autonomous agents think, coordinate, and act. In AIVille 2.0, it forms the substrate for AI-driven governance, economy, and storytelling.
