Vibecoding in Between Meetings: Learning MCP Servers and Building a PoC ( with AWS Kiro)

Source: Dev.to

Introduction

A few weeks ago, an opportunity came up for my team to collaborate with our data‑science team on a small prototype: building and deploying an MCP server. Nothing massive, no long‑term roadmap commitment—just a chance to explore something new and see if it could be useful.

I took it, not only because the topic was interesting, but because it gave me an excuse to get my hands dirty again. The hype around AI‑native development often stays outside inspirational talks and LinkedIn posts. This felt like a good opportunity to experiment with an AI‑native SDLC in practice, as a way of working rather than just a concept.

This experience sparked two ideas:

1. It would make a good conference talk.
2. I should write about it while it’s still fresh, messy, and honest, creating a series of posts where I learn in public.

Vibecoding in Between Meetings: Building & Deploying MCP Servers with Kiro and Agent Core Runtime explores how adopting an AI‑native SDLC helped me reclaim building time—allowing me to learn a new topic, structure my exploration, and build a proof of concept in the gaps between meetings.

We’ll go from zero to deployed: starting with a simple stdio MCP server, evolving it into an HTTP MCP server, validating everything with the MCP UI Inspector, and finally deploying it on AWS using Agent Core Runtime. Along the way, we’ll lean on Kiro agents, personas, and prompts as well as MCP servers to accelerate learning loops, automate documentation, and capture architectural decisions.

I’m hoping to bring this talk to a conference sometime next year. This series is, in many ways, the raw material for it.

Phase One Summary

We’ve just wrapped up phase one of the proof of concept:

• A first stdio MCP server.
• Its HTTP version, run locally on Docker and tested with MCP Inspector.
• Deployment on our AWS infrastructure with Agent Core Runtime.

That’s a solid foundation—but there’s a lot we haven’t explored yet. Agent Core has more components we need to understand. Costs and operational pitfalls need to be evaluated. Most importantly, we need to figure out how (or if) this fits into our actual product.

Future Plans

The series isn’t a fixed plan; it will evolve as development continues over the next month or two.

Rough Structure

1. From “No Time to Build” to an AI‑Native SDLC
2. Getting Started with Kiro: Setup, Powers, and Steering the AI
3. MCP Servers Explained: The Missing Layer Between AI and Your Systems
4. Vibecoding a First MCP Server: Building a stdio MCP for Internal Use
5. From stdio to HTTP: Evolving an MCP Server with FastMCP
6. From Local to the Cloud: Running MCP Servers on Agent Core Runtime
7. Beyond the Runtime: Gateway, Identity, and Memory in Agent Core

Some posts may split, others may merge, and new ones might appear as we uncover unexpected integration, security, and cost considerations.

Who This Is For

If you’re an Engineering Manager, a Staff+ engineer, or anyone trying to stay technically sharp despite spending your days trapped in meetings, I hope you find this series interesting and useful. My goal isn’t to present perfect solutions; it’s to show how AI can become a multiplier—helping us learn faster, validate earlier, and build again, even when time is scarce.

Let’s see where this goes.