Understanding MCP: Model Context Protocol Use Cases and Examples

Model Context Protocol connects AI to external systems through an open standard—think of it as USB-C for AI, but for artificial intelligence applications.

What makes MCP powerful is how it transforms your workflow. Rather than constantly switching between different tools and losing your train of thought, AI can access them directly through MCP, which eliminates the context switching that kills productivity and accelerates your entire workflow.

I'll explain what MCP is, how it works, and show you some practical examples.

The Fundamental Difference

Without tools, an AI agent is just a chatbot. It can talk, answer questions, and generate text, but it can't actually do things in the real world.

That's where MCP changes everything. When you add MCP, you're giving AI the ability to act—not just respond. It connects AI directly to your systems, enabling it to fetch live data, update records, create content, and perform actual actions rather than just talking about them.

This represents a fundamental shift from conversation to action, from answering questions to actually doing work, from chatbot to true agent.

What is Model Context Protocol (MCP)?

Model Context Protocol is an open standard that connects AI applications to external systems through a standardized interface. No need for custom integrations for every tool.

MCP uses a client-server model. The AI application is the client, and external systems run MCP servers. They communicate using JSON-RPC over stdio or Server-Sent Events (SSE). Simple and reliable.

MCP gives you three things:

• Tools: Functions the AI can call—APIs, database queries, actions
• Resources: Data sources the application controls—files, database records, system state
• Prompts: Reusable templates users can invoke

MCP is vendor-neutral, so it works across different AI platforms. Use Claude, Cursor, or any other AI tool—they all work with the same MCP servers. No rebuilding integrations when you switch tools.

How MCP Works

MCP uses a simple client-server architecture. AI applications are clients, external systems run MCP servers, and they communicate via JSON-RPC over stdio or Server-Sent Events. When you ask AI to fetch data or perform an action, MCP handles the connection complexity. It standardizes the interface so everything looks the same to the AI.

That standardization is powerful. Build one MCP server, use it with multiple AI tools. Build once, use everywhere.

When to Use MCP

Use MCP when you need real-time access to external databases, integration with business tools (CRM, project management, analytics), or multi-tool workflows. MCP shines with live connections to systems that change constantly—it's built for current, up-to-date data, not static knowledge.

Real-World MCP Examples

Here are some real examples of MCP in action.

Atlassian MCP: Jira and Confluence Integration

Atlassian MCP connects AI to your Jira and Confluence instances. Instead of switching tabs constantly, ask AI to fetch Jira ticket details—description, acceptance criteria, linked issues, comments. You get complete context without leaving your editor.

After implementing a feature, AI drafts Confluence pages and MCP publishes them. You review and approve. Need to communicate? AI drafts comments and MCP posts them to Jira tickets. Your team stays informed, you stay focused on coding.

GitLab MCP: Merge Request Management

GitLab MCP streamlines code review. Instead of writing MR descriptions manually, AI analyzes your code changes, MCP fetches related issues, and AI drafts descriptions. To address review comments, ask "Get all comments on MR !123" and MCP fetches everything. AI analyzes feedback and suggests code changes, then MCP posts responses and marks comments resolved.

Check MR status, respond to comments, update descriptions—all from your editor. No context switching. Review cycle time drops 40-60% because you're not jumping between tools.

Why MCP Matters

MCP doesn't just connect systems—it transforms workflows by eliminating friction. With Atlassian MCP, you eliminate context switching between Jira, Confluence, and your editor. GitLab MCP streamlines code review into a single workflow. These translate to hours saved per week, faster delivery, and better collaboration.

Getting started is straightforward: choose an MCP server, configure it in your AI tool (Claude, Cursor, etc.), connect to your systems, and start using AI to interact with your tools. Most MCP servers are open source and well-documented. Small investment—usually just configuration time—but substantial return.

Conclusion: MCP Connects AI to Your World