Introduction to Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an innovative, open-source tool developed by Anthropic that revolutionizes the way AI assistants connect to systems and tools. In this article, we will delve into the world of MCP, exploring its theory, architecture, and real-world applications.

What is Model Context Protocol (MCP)?

Introduction to MCP MCP is a groundbreaking protocol that bridges the gap between AI models and real-time data. It is an open-source tool that can be found on GitHub, and its primary function is to connect AI assistants or agents more effectively by linking them to systems and tools where the relevant data resides.

Challenges in AI Integration

Challenges faced by LLMs One of the significant challenges faced by Large Language Models (LLMs) is limited data access. AI models often lack access to real-time or domain-specific data because they are not inherently connected to external systems. This limitation can be overcome using MCP, which solves both of these major issues faced by LLMs.

Core Architecture of MCP

Overview of MCP Architecture The core architecture of MCP consists of three main components: Host, Client, and Server. The Host can be any LLM, such as Claude, that connects to a Server through the MCP protocol. The Server has access to local data sources and remote services.

MCP Host and Client

MCP Host and Client Explanation The MCP Host is a program or tool that uses MCP to access data. The Host can be considered as the Cloud desktop app, and it includes an MCP Client that facilitates communication with the MCP Server. The Client can be thought of as a messenger that connects between the Server and the Host.

MCP Server

MCP Server Explanation The MCP Server is a lightweight program that exposes specific capabilities through the MCP protocol. These Servers have access to local data sources, such as files on a computer, and remote services, such as weather APIs.

Real-World Applications of MCP

Real-World Applications of MCP MCP has various real-world applications, such as creating a folder in a specific directory or calling a weather API. These applications are made possible by the MCP protocol, which enables seamless communication between AI assistants and systems.

Workflow of MCP

Workflow of MCP The workflow of MCP involves the Host sending a prompt to the Client, which then sends requests to different MCP Servers using the standardized MCP protocol. The Servers have access to local data sources or remote services and respond back to the Host with the result.

Tools and Servers

Tools and Servers Explanation Each Server has tools that provide specific functions, such as creating a file or reading the contents of a file. These tools enable the Server to access the specific directory and perform the desired action.

Conclusion and Future Videos

Conclusion and Future Videos In conclusion, MCP is a powerful tool that enables AI assistants to connect to systems and tools more effectively. In future videos, we will explore how to integrate pre-existing servers on the Cloud desktop app and create custom servers for specific use cases.