MCP for Beginners: Understanding the New AI Standard from Anthropic

The Model Context Protocol (MCP) has been generating a lot of buzz in the AI community lately. Social media is full of discussions, demos, and tutorials about it. But while the hype is high, what many resources lack is a clear, fundamental explanation of what MCP actually is, how it works, and why it matters.

In this blog, we’ll break it down step-by-step — starting from its origins, moving through the technical details, and ending with real-world examples you can relate to.

What is the Model Context Protocol (MCP)?

The Model Context Protocol is a relatively new standard introduced by Anthropic in November 2024. Its main purpose is to standardize how AI systems — particularly those powered by large language models (LLMs) — interact with external tools and data.

Anthropic, an AI-first company founded in January 2021 by former OpenAI employees, is best known for its Claude language models. With MCP, Anthropic aims to create a consistent and scalable way for AI agents to communicate with databases, APIs, and other systems without needing to rebuild integrations for every new agent.

The Problem MCP Solves

Traditionally, interactions between users and LLMs are straightforward:

1. The user enters a prompt.

2. The LLM processes it.

3. The LLM returns a response.

While this works well for many scenarios, it has one big limitation — the LLM cannot directly access your organization’s tools, databases, or unstructured data (like SharePoint lists).

To work around this, we’ve been building agents (for example, using Power Virtual Agents), which have access to specific tools, knowledge bases, connections, and workflows.

But here’s the problem:

When you build a new agent, you have to recreate all the integrations from scratch — wiring up the same tools, databases, and APIs over and over again. This is time-consuming, repetitive, and hard to scale.

How MCP Changes the Game

MCP introduces a server-based architecture that separates tools and data from individual agents.

Here’s how it works:

 The Server → Holds all the tools, knowledge, connections, and workflows in one centralized location.

 The Agent (Host/Client) → Handles user prompts, communicates with the server, and interacts with the LLM.

The communication between the agent and the server happens via JSON or HTTPS protocols.

Why this matters:

Multiple agents can now access the same server and its resources without duplicating work. You build your tools once and reuse them across any number of agents.

MCP in Action — Example Scenarios

Let’s make this concrete with two examples:

Example 1: Checking PTO Hours

User Prompt: “How many PTO hours do I have left?”

Step-by-step process:

1. The user enters the prompt into the agent.
2. The agent sends the prompt and available server tools info to the LLM.
3. The LLM identifies that the Workday tool on the server can answer this question.
4. The agent makes a GET request to the server’s Workday tool.
5. The server retrieves the data and sends it back to the agent.
6. The agent delivers the PTO hours to the user.

Example 2: Submitting a PTO Request

User Prompt: “Request 2 days off starting next Monday.”

Step-by-step process:

• The user enters the prompt into the agent.
• The agent sends the prompt and tool list to the LLM.
• The LLM determines that the Workday tool can submit PTO requests.
•  The agent sends a POST request to the server to:

• Submit the PTO request in Workday.
• Trigger an approval process for the user’s manager.
• The server completes the request and sends confirmation.
• The agent returns the confirmation to the user.

Why Learn MCP?

The Model Context Protocol offers a new approach to AI application design:

• Centralized resource management — build tools once, use them everywhere.
• Scalable architecture — multiple agents can connect to the same resource hub.
• Faster development — no more repetitive wiring of tools for each project.

Whether you’re an AI developer, a solutions architect, or simply curious about the future of AI integrations, MCP is worth exploring.

In the next step, you can try building your own MCP setup from scratch. You’ll need:

• A GitHub account
• An Azure subscription
• Access to Copilot Studio

From there, you can wire your MCP server into Copilot Studio and start building agents that are smarter, faster, and more connected than ever before.

Final Thought:

MCP isn’t just another AI buzzword — it’s a practical standard that could fundamentally change how AI systems work with your data and tools. Understanding it now puts you ahead of the curve as it becomes more widely adopted.