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Generative AI applications are a great step forward — they let users interact using natural language prompts. But as those apps grow more complex, you need a consistent architecture to extend them reliably, support multiple models, and avoid re-building integrations from scratch. This is where MCP comes in.

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Learning Objectives

By the end of this module, you’ll be able to:
  • Define the Model Context Protocol and its use cases
  • Understand how MCP standardizes model-to-tool communication
  • Identify the core components of MCP architecture
  • Describe real-world applications of MCP in enterprise and development contexts

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What Is the Model Context Protocol?

The Model Context Protocol (MCP) is an open, standardized interface that allows Large Language Models (LLMs) to interact seamlessly with external tools, APIs, and data sources. It provides a consistent architecture to enhance AI model functionality beyond training data, enabling smarter, scalable, and more responsive AI systems.
MCP acts as a universal standard for AI interactions — much like how USB-C standardized physical device connections. Instead of every AI model needing custom code to work with every tool, MCP creates a universal way for them to communicate.

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Why Standardization in AI Matters

Before MCP, integrating models with tools required:
  • Custom code per tool-model pair
  • Non-standard APIs for each vendor
  • Fragile integrations that broke with updates
  • Poor scalability when adding more tools
MCP addresses these problems by unifying model-tool integrations, reducing brittle one-off solutions, and allowing multiple models from different vendors to coexist within a single ecosystem.
BenefitDescription
InteroperabilityLLMs work seamlessly with tools across different vendors
ConsistencyUniform behavior across platforms and tools
ReusabilityTools built once can be used across projects and systems
Accelerated DevelopmentReduce dev time by using standardized, plug-and-play interfaces
While MCP bills itself as an open standard, there are currently no plans to standardize it through bodies such as IEEE, IETF, W3C, or ISO.

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MCP Architecture Overview

MCP follows a client-server model with three primary roles:
  • MCP Hosts — AI applications (like Claude Desktop or VS Code) that run models and coordinate connections
  • MCP Clients — Protocol clients embedded in the Host that maintain 1:1 connections with servers
  • MCP Servers — Lightweight programs that expose specific capabilities (tools, data, prompts) through the standardized protocol

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How a Request Flows Through MCP

1

User initiates a request

A user interacts with an application (the MCP Host). The Host passes the request to the AI model.
2

AI model identifies needed tools

The model determines it needs external data or an action (e.g., a web search, database query, or calculation) and issues a tool call.
3

Host routes to MCP Server

The MCP Host — not the model directly — sends the tool call to the appropriate MCP Server using the standardized protocol. The Host’s Tool Registry identifies which server to use.
4

Server executes and returns

The MCP Server executes the requested operation and returns a structured result to the Host.
5

Model generates final response

The Host relays the tool output to the model, which incorporates it into a final response and sends it back to the user.

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MCP Host Components

The Host does more than forward requests — it manages the entire lifecycle:
  • Tool Registry — maintains a catalog of available tools and their capabilities
  • Authentication — verifies permissions for tool access
  • Request Handler — processes incoming tool requests from the model
  • Response Formatter — structures tool outputs into a format the model understands

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Server Primitives

MCP servers expose three types of primitives:

Resources

Static or dynamic data that provides context to AI models — files, database records, API responses, knowledge bases. Identified by URIs like file://documents/spec.md or api://weather/current.

Prompts

Reusable templates that structure interactions with language models. Support variable substitution and define standardized workflows for common tasks.

Tools

Executable functions that AI models can invoke with specific parameters. Defined with JSON Schema validation and support behavioral annotations like readOnlyHint or destructiveHint.

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Real-World Use Cases

ApplicationDescription
Enterprise Data IntegrationConnect LLMs to databases, CRMs, or internal tools
Agentic AI SystemsEnable autonomous agents with tool access and decision-making workflows
Multi-modal ApplicationsCombine text, image, and audio tools within a single unified AI app
Real-time Data IntegrationBring live data into AI interactions for more accurate, current outputs

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Scalable Agent Solution

One of MCP’s most powerful properties is federating tools and knowledge across servers. A single LLM can connect to multiple MCP servers simultaneously, and those servers can even communicate with each other through a universal connector:
  • ServerA provides knowledge and tools for one domain (e.g., your company’s internal data)
  • ServerB provides knowledge and tools for another (e.g., a third-party API)
  • Both are discoverable dynamically — adding a new MCP server to an agent’s system makes its functions immediately usable without requiring changes to the agent’s instructions

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Protocol Foundation

MCP uses a two-layer architecture:
Built on JSON-RPC 2.0, the data layer defines message structure, semantics, and interaction patterns. It handles:
  • Connection initialization and capability negotiation
  • Server primitives: tools, resources, prompts
  • Client primitives: sampling, elicitation, roots, logging
  • Real-time notifications for dynamic updates

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Building an MCP Server: Quick Preview

Here’s how you register a tool on an MCP server in four popular languages: 
from fastmcp import FastMCP

mcp = FastMCP(name="Weather MCP Server", version="1.0.0")

@mcp.tool()
def get_weather(location: str) -> dict:
    """Gets current weather for a location."""
    return {
        "temperature": 72.5,
        "conditions": "Sunny",
        "location": location
    }

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Practical Benefits of MCP

  • Freshness — models can access up-to-date information beyond their training data
  • Capability Extension — models can leverage specialized tools for tasks they weren’t trained for
  • Reduced Hallucinations — external data sources provide factual grounding
  • Privacy — sensitive data can stay within secure environments instead of being embedded in prompts

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Exercise

Think about an AI application you’re interested in building:
  1. Which external tools or data sources could enhance its capabilities?
  2. How might MCP make that integration simpler and more reliable?
  3. Would your server expose Resources, Prompts, or Tools — or a combination?

Python SDK

The official Python SDK for building MCP servers and clients.

TypeScript SDK

The official TypeScript/JavaScript SDK.

Java SDK

The official Java SDK for MCP implementations.

C#/.NET SDK

The official C#/.NET SDK for MCP.

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What’s Next

Now that you understand what MCP is and why it matters, continue to Module 1: Core Concepts to dive deep into the client-server architecture, all MCP primitives, and the full information flow.

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