Build A Smart Cloud Application With Vibe Coding And Mcp

Bonisiwe Shabane

-Jan 20, 2026, 4:52 AM

build a smart cloud application with vibe coding and mcp

This guidance demonstrates how to accelerate AWS application development using AI coding assistants powered by AWS Model Context Protocol (MCP) Servers. By integrating specialized MCP servers for AWS documentation, architecture visualization, React component generation, cost analysis, and security assessment, developers can streamline cloud development workflows through natural language interactions. The solution reduces time spent on manual tasks like documentation research and architecture design while ensuring adherence to AWS best practices, enabling teams to focus on business logic rather than infrastructure complexity, ultimately accelerating... Ship production-ready applications faster with AI-powered coding assistance and pre-built AWS integrations. Reduce development cycles while maintaining security and cost optimization best practices. Enable developers to build complex AWS architectures without deep expertise through intelligent documentation access and visual diagram generation.

Transform natural language requests into working AWS solutions. Assess cost implications and security compliance before deployment with integrated pricing analysis and CDK security evaluation. Make data-driven decisions that optimize both performance and budget. This architecture diagram illustrates how to effectively develop AWS applications using AI assistants enhanced with AWS MCP Servers, demonstrated through a sample hotel booking application built on Amazon Bedrock AgentCore. This lab focuses on using Gemini CLI with Google Cloud Run and Cloud Build to configure, test, and deploy an enhanced and functional ADK Agent with a remote MCP server. Build, innovate, and scale with Google Cloud Platform.

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world. Engage hands-on with the tools and technologies you’re learning. You pick the skill, we provide the credentials and environment. All labs have detailed instructions and objectives, guiding you through the learning process and ensuring you understand every step.

Over the past few months, I’ve explored vibe coding — a workflow where you prompt the AI, it generates code, and you iterate. Fast and interactive, yes — but in enterprise projects, context and governance are critical. Without them, hallucinations and mismatched code structures quickly emerge. This is where MCP servers (Model Context Protocol) and SAP AI Core become essential. Tools like fiori server and cap server allow AI assistants to understand your CDS models, Fiori metadata, and application structure — enabling contextual, reliable code generation. In this blog, we’ll explore the architecture, practical usage, and enterprise best practices that make AI-assisted coding robust and production-ready.

Vibe coding is a transformative approach to software development — essentially, coding by conversation. The power of vibe coding lies in freeing developers from repetitive boilerplate tasks, allowing them to focus on high-value work like architecture, user experience, and business logic. This Guidance demonstrates how to build AI-powered development workflows using Amazon Bedrock AgentCore and the Model Context Protocol (MCP). It provides a complete, deployable hotel booking agent system that showcases "vibe coding" techniques - an AI-assisted development approach that accelerates software development through intelligent code generation, discovery, and problem-solving. The Guidance is designed as an interactive workshop where participants learn to: Participants deploy a realistic hotel booking agent using Amazon Bedrock AgentCore and gain hands-on experience with AI development tools including Kiro, Amazon Q, and AWS MCP Servers.

The skills learned can be immediately applied to production projects and shared across development teams. This workshop leverages AWS MCP Servers, specialized MCP servers that enhance foundation model capabilities through the Model Context Protocol (MCP). MCP is an open protocol that enables seamless integration between LLM applications and external data sources and tools. The workshop demonstrates these capabilities across three practical scenarios: Modern software development is shifting from static workflows to dynamic, agent-driven coding experiences. At the center of this transition is the Model Context Protocol (MCP), a standard for connecting AI agents to external tools, data, and services.

MCP provides a structured way for large language models (LLMs) to request, consume, and persist context. This makes coding sessions more adaptive, reproducible, and collaborative. In short, MCP acts as the “middleware” that enables Vibe Coding—an interactive style of programming where developers and AI agents co-create in real time. Below are seven notable MCP servers that extend developer environments with specialized capabilities for version control, memory, database integration, research, and browser automation for Vibe Coders. GitMCP focuses on making repositories natively accessible to AI agents. It bridges MCP with Git workflows, allowing models to clone, browse, and interact with codebases directly.

This reduces the overhead of manually feeding context to the agent. Supabase MCP integrates real-time databases and authentication directly into MCP-enabled workflows. By exposing a Postgres-native API to LLMs, it lets agents query live data, run migrations, or even test queries without leaving the coding session. Browser MCP enables agents to launch headless browsers, scrape data, and interact with web applications. It effectively equips an LLM with browsing capabilities inside a coding environment. Vibe coding is a new way of writing software where you describe what you want in natural language and let an AI generate the code.

The term was coined in February 2025 by Andrej Karpathy, co‑founder of OpenAI . Essentially, you “vibe” the code into existence and review it only when it looks right. A new approach to software development: Describe your project in a few natural sentences, let AI build it, and only look at the code once it runs or looks right. An unpredictable but powerful new way to write software useful for prototyping, but risky for anything critical. You let AI write code, then review it only after it works visually or functionally ignoring the detailed structure until later Fast iteration and prototyping — In experienced hands, it can be a fast and creative way to build, but it’s not reliable for critical production systems without oversight

The coding landscape is shifting, and at the heart of this transformation is the Model Context Protocol (MCP) server, a game-changer for vibe coding and function coding. Vibe coding lets developers express ideas in natural language, while function coding emphasizes modular, reusable code. Together, powered by MCP servers, they enable a seamless, AI-driven development experience. This blog explores what MCP servers are, how they supercharge vibe coding, and why they’re revolutionizing function coding for developers of all skill levels. An MCP server is a specialized service that implements the Model Context Protocol (MCP), an open standard for connecting large language models (LLMs) to external tools, data sources, and development environments. Think of it as a bridge that gives AI coding assistants—like GitHub Copilot, Cursor, or Cline—access to real-time project context, APIs, and services.

MCP servers standardize interactions, replacing clunky HTTP-based integrations with a unified, discoverable protocol. These servers empower AI to understand your codebase, documentation, and workflows, making vibe coding intuitive and function coding modular and efficient. Vibe coding is about capturing the creative “vibe” of a project. Instead of wrestling with syntax or debugging, you describe your goal in natural language—e.g., “Build a function to fetch user data from a REST API”—and the AI generates the code. MCP servers enhance this by providing the AI with context, such as your project’s database schema or API documentation, ensuring accurate and relevant output. For example, with an Apidog MCP server, you can prompt, “Create a Python function to call my user API,” and the AI will:

Build A Smart Cloud Application With Vibe Coding And Mcp

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