The Model Context Protocol (MCP) is an open standard that enables AI, particularly large language models, to interact with external tools, APIs, and services.
How Does MCP Work?
MCP formalizes how a model interacts with its environment, transforming it from a static generator into a dynamic task-executing system. This architecture allows developers to:
* Abstract API calls and services into standardized interfaces; * Safely and modularly expose functions to AI agents; * Maintain a high degree of control, security, and auditability over autonomous behavior.
Comparison of MCP, RAG, and AI Agents
While MCP, RAG, and AI agents are often mentioned together, they serve distinct roles within an autonomous system. In short:
* RAG augments what the model knows; * MCP extends what the model can do; * AI agents orchestrate both to pursue long-term goals.
Example from AIVille: How MCP is Implemented
At AIVille, we are building a fully AI-native simulation — a virtual society governed by autonomous agents. Each character will interact with MCP for seamless connection to both on-chain and off-chain systems. Planned use cases include:
* Agents with environment-aware behavior; * Dynamic task routing; * External system integration; * Governance participation.
MCP is not just a protocol — it is the execution layer of the AI-native internet. AIVille aims to create a self-organizing and intelligent society based on this technology.
