Why Everyone’s Talking About MCP?

Today we're diving into the model context protocol or MCP. One of the most significant advancements in LLM integration released by Anthropic in late

2024. So what exactly is MCP? At its

2024. So what exactly is MCP? At its

core, the model context protocol is an open standard that enables seamless integration between AI models like claude and external data sources or tools. is addressing a fundamental

tools. is addressing a fundamental limitation that has held back AI assistance from reaching their potential. Before MCP, connecting models

potential. Before MCP, connecting models to each new data source require custom implementations, which can get expensive. MCB solves this by providing

expensive. MCB solves this by providing a universal open standard for connecting AI systems with data sources, replacing fragmented integrations with a single

protocol. This means we can give AI

protocol. This means we can give AI systems access to databases, file systems, APIs, and other tools in a standardized way. Let's break down the

standardized way. Let's break down the architecture. MCP follows a client

architecture. MCP follows a client server models with three key components.

Hosts, clients, and servers. Host are

LLM applications like cloud desktop that provide the environment for connections.

Clients are components within the host that establish and maintain onetoone connections with external servers.

Servers are separate processes that provide context, tools, and prompts to these clients, exposing specific capabilities through the standardized

protocol. Let's dive deeper into the

protocol. Let's dive deeper into the five core primitives that power MCP.

These primitives are the building blocks that enable standardized communication between AI models and external systems. Servers support three primitives. First

prompts. These are instructions or templates that can be injected into the LLM context. They guide how the model

LLM context. They guide how the model should approach certain tasks or data.

Second, resources, structured data objects that can be included in the LLM's context window. They allow the model to reference external information.

Third, tools. Executable functions that the LLM can call to retrieve information or perform actions outside its context like quering a database or modifying a

file. On the client side, there are two

file. On the client side, there are two primitives that are equally important.

First, the root primitive. Think of it as creating a secure channel for file access. It allows the AI application to

access. It allows the AI application to safely work with files on your local system by opening documents, reading code, or analyzing data files without

giving unrestricted access to your entire file system. Second, the sampling primitive. This enables a server to

primitive. This enables a server to request the LLM's help when needed. For

example, if an MCP server is analyzing your database schema, it needs to generate a relevant query, you can ask the LLM to help formulate that query through the sampling primitive. This

create a two-way interaction where both the AI and the external tools can initiate requests to each other making the whole system more flexible and

powerful. Now, the real power of MCP

powerful. Now, the real power of MCP becomes clear when we consider the N byN problem it solves. Previously

integrating n different LLMs with m different tools require m by m different integrations with mcp tool builders who implement one protocol and llm vendors

like anthropic implement the same protocol dramatically simplifying the integration landscape. Let's look at a

integration landscape. Let's look at a practical example using cloud. When we

need cloud to analyze data from our postgress database we don't need to build a custom integration. Instead, we

can use an MCP server for Postgress that exposes database connections through the protocol's primitives. Claude through an

protocol's primitives. Claude through an MCP client can then query the database where MCP server process the results and incorporate the insight into its

responses all while maintaining security and context. The ecosystem is growing

and context. The ecosystem is growing rapidly. Developers have already created

rapidly. Developers have already created many integrations using MCP for systems like Google Drive, Slack, GitHub, Git and Postgress. There are SDKs availables

and Postgress. There are SDKs availables in multiple languages including Typescript and Python making it easier to implement in various environments.

Looking ahead, MCP is positioned to become a foundational technology in the AI landscape, particularly for building sophisticated AI applications that interact with diverse data sources and

tools. The open source nature and

tools. The open source nature and growing ecosystem make it accessible to developers of all sizes. If you like our videos, you might like our system design

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