Introduction

Model Context Protocol (MCP) shifts how we use LLMs by allowing them to interact with external systems and services. Instead of being limited to their pre-trained knowledge, MCP-enabled models can call external functions to retrieve real-time data, use specialized tools, and perform dynamic tasks.

Originally introduced by Anthropic—the creators of Claude—MCP provides a standardized way for models to extend their capabilities. The official specification is maintained at modelcontextprotocol.io.

For AI Agents

Before MCP, integrating tools with LLMs was a bit of a free-for-all. We had plugin systems in USDK and Eliza, tool abstractions in LangChain, and plenty of other one-off solutions. But there was no universal approach—just different frameworks reinventing the wheel in their own ways.

MCP changes that by introducing a common standard. Instead of each framework defining its own way to connect LLMs with external tools, MCP provides a shared protocol that anyone can build on. This means better interoperability, easier adoption, and a more consistent experience across different AI agent frameworks.