Model Context Protocol: Build Production-Ready AI Integrations

1. MCP Foundations

Understand what MCP is, why it exists, and where it fits in the AI application stack.

1.1 – Why MCP Exists (link to this section)

Learn the integration problem MCP solves and why AI systems need a standard way to access tools, data, and context.

Why MCP Exists

Goal

Understand the integration problem Model Context Protocol solves.

AI products need access to tools, files, databases, SaaS APIs, and business workflows. Without a shared protocol, every AI app builds one-off connectors and every tool vendor reinvents the same bridge.

Key ideas

  • MCP standardizes how AI hosts discover and use external capabilities.
  • Servers expose capabilities as tools, resources, and prompts.
  • The protocol separates model reasoning from system integration.
  • MCP complements APIs, RAG, and function calling rather than replacing all of them.

Practice

List three systems an AI assistant in your organization would need. For each, decide whether it is best exposed as a tool, resource, or prompt.

1.2 – MCP Mental Model (link to this section)

Build the core mental model: hosts, clients, servers, tools, resources, prompts, and transports.

MCP Mental Model

Goal

Build the vocabulary of MCP.

Concepts

  • Host: the AI application the user interacts with.
  • Client: the protocol participant inside the host that connects to a server.
  • Server: a program that exposes capabilities.
  • Tools: callable actions with schemas.
  • Resources: readable context identified by URIs.
  • Prompts: reusable interaction templates.
  • Transport: how messages move between client and server.

Check

Explain the boundary between a host, client, and server in one paragraph.

1.3 – Anatomy of an MCP Interaction (link to this section)

Walk through a complete MCP interaction from user request to tool execution and model response.

Anatomy of an MCP Interaction

Goal

Trace a full MCP interaction.

Flow

  1. User asks for an outcome.
  2. Host and model decide external capability is needed.
  3. Client discovers available server capabilities.
  4. Model selects a tool or resource.
  5. Server validates input and executes.
  6. Result returns to the model.
  7. Model explains or continues the workflow.

Design principle

Every tool result should help the model either complete the task or choose the next safe step.

1.4 – When to Use MCP (link to this section)

Learn where MCP is useful, where it is overkill, and how to choose between MCP, direct APIs, RAG, and custom tool calling.

When to Use MCP

Goal

Choose MCP deliberately.

Use MCP when

  • Multiple AI clients need the same integration.
  • Capabilities need discovery, schemas, and reusable descriptions.
  • You expose internal tools or contextual resources.
  • You want a stable boundary between AI host and service logic.

Avoid MCP when

  • A single direct API call is enough.
  • The data is only needed for offline indexing.
  • You cannot define safe permissions.

Exercise

Evaluate one workflow using criteria: reuse, context need, safety, latency, and operational owner.

Practise this chapter in the workspace

Reading is the map. Every section above also runs as a hands-on workspace session with tools, exercises and a recap quiz.

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