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What is the Model Context Protocol?

The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). Think of it as a universal connector that allows LLMs to interact with external tools, data sources, and services in a consistent way.
MCP was created by Anthropic and is designed to solve the fragmentation problem in LLM integrations. Instead of building custom integrations for each tool, MCP provides a single, standardized protocol.

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Why MCP Matters

Large Language Models are powerful, but they have limitations:
  • No access to real-time data: LLMs are trained on static datasets and can’t fetch current information
  • No ability to take actions: They can generate text but can’t execute code, make API calls, or modify systems
  • Limited context: They only know what’s in their training data and your prompt
MCP solves these problems by providing a standardized way for LLMs to:

Access Tools

Execute functions and operations like calculations, API calls, or system commands

Read Resources

Fetch data from external sources like databases, files, or APIs

Use Prompts

Leverage pre-built prompt templates for common tasks

Maintain Context

Keep conversation context across multiple interactions

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How MCP Works

MCP uses a client-server architecture where:
  1. MCP Servers expose capabilities (tools, resources, prompts) through the protocol
  2. MCP Clients connect to servers and invoke these capabilities
  3. LLM Applications use MCP clients to extend the LLM’s capabilities

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Core Concepts

Tools are functions that the LLM can invoke to perform operations. They define:
  • A unique name and description
  • Input parameters with validation schemas
  • The operation to execute when called
Example: A calculator tool that performs mathematical operations.
Resources represent data that can be read by the LLM. They can be:
  • Static resources with fixed URIs
  • Dynamic resources with templated URIs
  • Any data source: files, API responses, database records
Example: A resource that fetches random quotes from an API.
Prompts are reusable templates that help guide the LLM’s responses. They include:
  • Structured messages with roles (system, user, assistant)
  • Parameterized content that can be customized
  • Context and instructions for specific tasks
Example: A code review prompt that structures how to analyze code.
Transport defines how clients and servers communicate. MCP supports:
  • stdio: Communication through standard input/output (most common)
  • HTTP with SSE: Server-sent events for web applications
  • Custom transports for specific use cases

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What This Course Covers

This comprehensive course will teach you how to build production-ready MCP servers and clients through hands-on examples.

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Course Structure

1

Fundamentals

Learn the core MCP concepts, protocol details, and architecture patterns.
  • Protocol specification and message types
  • Client-server communication patterns
  • Tools, resources, and prompts in depth
2

Building Servers

Create MCP servers in TypeScript, Python, and Go with real-world examples.
  • Game of Thrones quotes API integration
  • Calculator with mathematical operations
  • Todo management with state
  • EDteam course API integration
3

Building Clients

Develop clients that connect to MCP servers and invoke their capabilities.
  • Basic clients in TypeScript and Python
  • Ollama integration for local LLM function calling
  • Error handling and connection management
4

Real-World Integration

Deploy and integrate MCP servers with popular LLM applications.
  • Claude Desktop integration
  • Custom LLM application integration
  • Production deployment patterns

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Code Examples

All examples in this course are real, working code from the MCP Course repository. You’ll find implementations in:
  • TypeScript: Using the official @modelcontextprotocol/sdk
  • Python: Using both the official SDK and FastMCP
  • Go: Using the mark3labs/mcp-go SDK
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "Game of Thrones Quotes",
  version: "1.0.0",
  capabilities: {
    resources: { listChanged: true },
    tools: {},
    prompts: {}
  }
});

server.tool(
  "get_random_quotes",
  { count: z.number().optional().default(5) },
  async ({ count }) => {
    const quotes = await fetchRandomQuotes(count);
    return {
      content: [{ type: "text", text: formatQuotes(quotes) }]
    };
  }
);

const transport = new StdioServerTransport();
await server.connect(transport);

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Key Sections

Explore the course content:

Quickstart

Get your first MCP server running in 5 minutes

MCP Protocol

Deep dive into the protocol specification

Server Examples

Learn from working server implementations

Client Examples

Build clients that connect to MCP servers

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Prerequisites

To get the most out of this course, you should have:
  • Basic understanding of programming concepts
  • Familiarity with at least one of: TypeScript/JavaScript, Python, or Go
  • Understanding of async/await patterns
  • Basic knowledge of APIs and HTTP
Don’t worry if you’re not an expert! The course includes detailed explanations and real code examples you can follow along with.

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Next Steps

Ready to get started? Head to the Quickstart Guide to build your first MCP server, or explore the MCP Protocol to understand the underlying concepts.
MCP is an evolving standard. This course uses MCP SDK version 1.7.0. Check the official MCP documentation for the latest updates.

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