WEBVTT

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Hello everyone and welcome.

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In this video we will learn about MCP.

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That is model context protocol.

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This is the next evolution in the LM architecture.

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We will learn about MCP as an architecture, the communication patterns, server components, transport

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types and an entire end to end flow.

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Having said that, let's first understand key components in MCP.

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So here on the left hand side we have an image with AI application sitting at the core of the uh, different

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uh, communication patterns.

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The core AI system is a center of architecture.

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It communicates with databases, web UIs, and GitHub's as an external service.

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The important part here is the dotted line with MCP.

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It provides a standard protocol for communicating across different services, allowing Llms to discover,

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understand and utilize external capabilities consistently.

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So now let's understand the three key components from MCP.

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So the very first component is MCP client applications like windsurf or cursor.

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They all serve as an LLM facing side of the ecosystem.

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These handle the user interface and interactions.

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Then there is MCP protocol.

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This is the heart of the MCP.

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It's a standardized communication layer that enables two way connections between clients and servers.

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And then there is an MCP server, the component that translates external services and their capabilities

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into a format that client can understand.

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Now having introduced three main components of MCP server, let's understand how it all works together.

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So here is a complete architecture.

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The Model Context protocol creates a standardized ecosystem that enables LM to discover and utilize

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external capabilities consistently across different implementations.

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MCP client.

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On the left Communicates with the MCP server through MCP protocol.

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The server acts as a translator, converting the external service capabilities into standardized format

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that MCP client can work with.

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This is indeed a very strategic and brilliant move.

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Why?

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It's because now MCP server is responsible for providing the services.

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This eliminates the need for custom integrations between each LM and external services.

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It significantly reduces the complexity for scalability and managing the changes that a service provider

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would introduce.

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So what anthropic did is that they said, we want our lives to be more powerful and capable, but it's

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your job to figure out the integration.

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This shifts the entire service burden of building and maintaining integrations from AI companies to

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service providers.

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Now why?

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This is very smart.

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It's smart because service providers, they know their systems best, and they are the most capable

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of providing the integrations by making them responsible for MCP servers.

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Anthropic ensures that integrations stay current, functional, and without having to maintain hundreds

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of different API connections themselves.

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Having said that, let's understand how the communication works in terms of MCP architecture, and let's

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take a deep dive.

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There.
