AI agents can reason, but they don’t actually understand your systems. MCP servers fix that by giving your copilots and assistants structured access to your tools, APIs, and CI/CD data. And the best part is that building one is much simpler than most people expect.

In the latest episode of Technical Tips, Tommy walks through how to create a functional MCP server in just a few minutes. He connects it to Semaphore’s API, pulls real project data, and shows how to expose those insights to tools like OpenAI Codex. It’s a straightforward, hands-on demo that finally makes MCP feel practical instead of theoretical.

If you’ve been curious about MCP or you’re trying to make your AI tools genuinely useful in day-to-day engineering work, this is the perfect place to start. You’ll see how an MCP server communicates with AI clients, how to wrap a real API into it, how to test everything using the MCP Inspector, and how easily you can turn your CI/CD workflows into a conversational experience.

By the end, you’ll understand how surprisingly little code it takes to give your AI agents real operational awareness.

Access the full guide on our blog.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit semaphoreio.substack.com