Hello everyone and welcome.
In this video we will learn about MCP.
That is model context protocol.
This is the next evolution in the LM architecture.
We will learn about MCP as an architecture, the communication patterns, server components, transport
types and an entire end to end flow.
Having said that, let's first understand key components in MCP.
So here on the left hand side we have an image with AI application sitting at the core of the uh, different
uh, communication patterns.
The core AI system is a center of architecture.
It communicates with databases, web UIs, and GitHub's as an external service.
The important part here is the dotted line with MCP.
It provides a standard protocol for communicating across different services, allowing Llms to discover,
understand and utilize external capabilities consistently.
So now let's understand the three key components from MCP.
So the very first component is MCP client applications like windsurf or cursor.
They all serve as an LLM facing side of the ecosystem.
These handle the user interface and interactions.
Then there is MCP protocol.
This is the heart of the MCP.
It's a standardized communication layer that enables two way connections between clients and servers.
And then there is an MCP server, the component that translates external services and their capabilities
into a format that client can understand.
Now having introduced three main components of MCP server, let's understand how it all works together.
So here is a complete architecture.
The Model Context protocol creates a standardized ecosystem that enables LM to discover and utilize
external capabilities consistently across different implementations.
MCP client.
On the left Communicates with the MCP server through MCP protocol.
The server acts as a translator, converting the external service capabilities into standardized format
that MCP client can work with.
This is indeed a very strategic and brilliant move.
Why?
It's because now MCP server is responsible for providing the services.
This eliminates the need for custom integrations between each LM and external services.
It significantly reduces the complexity for scalability and managing the changes that a service provider
would introduce.
So what anthropic did is that they said, we want our lives to be more powerful and capable, but it's
your job to figure out the integration.
This shifts the entire service burden of building and maintaining integrations from AI companies to
service providers.
Now why?
This is very smart.
It's smart because service providers, they know their systems best, and they are the most capable
of providing the integrations by making them responsible for MCP servers.
Anthropic ensures that integrations stay current, functional, and without having to maintain hundreds
of different API connections themselves.
Having said that, let's understand how the communication works in terms of MCP architecture, and let's
take a deep dive.
There.