Architecting and Deploying Full-Stack Applications via Claude Code and Model Context Protocol (MCP)

The landscape of software engineering is undergoing a fundamental shift from manual syntax construction to agentic orchestration. We are moving away from writing every line of boilerplate and toward a paradigm of "Specification-Driven Development." This post explores a high-level workflow for building, versioning, and deploying production-ready web applications using Claude Code, the Cursor IDE, and the Model Context Protocol (MCP).

The Agentic Development Environment

To implement an automated deployment pipeline, the environment must support both high-level reasoning and low-level system execution. The foundation of this workflow relies on three core components:

1. Claude Code (Terminal Interface): While a desktop GUI exists, the terminal-based implementation of Claude Code is superior for deployment workflows. It allows for direct interaction with the shell, enabling the AI to execute npm commands, manage file systems, and interface with Git. Installation is handled via cURL, allowing the agent to operate within the standard Unix-like execution environment.
2. Cursor IDE: As a fork of Visual Studio Code, Cursor provides the necessary extension ecosystem. For this workflow, the IDE serves as the visual layer for monitoring the file tree and managing the Claude Code extension.
3. Model Selection (Opus vs. Sonnet vs. Haiku): The choice of LLM impacts both the "reasoning density" and the operational cost. While Claude 3.5 Sonnet is often the standard for coding, using Opus 4.7 (as demonstrated in this workflow) provides the higher-order reasoning required for complex architectural planning, whereas Haiku can be utilized for low-latency, simple tasks.

Phase 1: Specification-Driven Development (The spec.md Pattern)

The primary failure mode in AI-assisted coding is "context drift," where the agent loses track of the original architectural requirements during iterative loops. To mitigate this, we implement a Specification-Driven Development pattern.

Before any code is generated, the developer must prompt the agent to act as a technical architect. The goal is to produce a spec.md file. This file serves as the "Single Source of Truth" (SSOT) for the project. The prompt engineering strategy involves:

• Requirement Elicitation: Instructing the agent to ask clarifying questions regarding design aesthetics, tech stack, and feature sets.
• Tech Stack Definition: For modern, scalable web applications, the recommended stack is Next.js paired with Tailary CSS. This provides a robust framework for both client-side rendering and server-side logic, ensuring the application is optimized for deployment.
• Content Mapping: Defining the information architecture (e.g., hero sections, project galleries, contact forms) within the Markdown spec.

By generating a spec.md file, we provide the agent with a persistent, readable context that can be referenced in future sessions, effectively solving the problem of long-term memory in stateless LLM interactions.

Phase 2: Implementation and Local Orchestration

Once the spec.md is finalized, the agent is instructed to execute the build. The instruction should follow the MVP (Minimum Viable Product) principle: "Build the slim version first, then iterate."

The agent executes the following lifecycle:

1. Scaffolding: Running npx create-next-app and configuring Tailwind CSS.
2. Component Construction: Iteratively generating React components based on the spec.md definitions.
3. Local Testing: Running the development server (typically on localhost:3001) to verify the DOM structure and CSS application.

During this phase, the developer should also initialize Git within the repository. By instructing the agent to "create a Git repo and commit all work," we establish a version control baseline. This allows for "rollback capability"—if an agentic edit introduces a regression, the developer can simply prompt the agent to revert to the previous commit.

Phase 3: Deployment via Model Context Protocol (MCP)

The most advanced stage of this workflow is the integration of the Model Context Protocol (MCP). MCP allows an AI agent to transcend the boundaries of the local file system and interact with external APIs and infrastructure.

In this workflow, we utilize the Hostinger Connector via an MCP server. This transforms the AI from a code generator into a DevOps engineer.

The Deployment Pipeline:

1. Infrastructure Provisioning: Utilizing Hostinger's web hosting services to establish a production environment and a custom domain.
2. API Token Generation: Generating a secure API token within the Hostinger dashboard to allow authenticated communication between Claude Code and the hosting platform.
3. MCP Configuration: The critical technical step involves injecting the Hostinger configuration into the Claude Code environment. This is done by updating the MCP configuration file, which exposes the Hostinger toolset (e.g., list_domains, deploy_site, update_site_name) to the agent.
4. Automated Deployment: Once the MCP server is active, the developer no longer needs to manually upload files or use FTP. A single natural language command—"Deploy my project now and update the site name to [New Name]"—triggers the agent to:
   • Package the Next.js build.
   • Authenticate via the API token.
   • Push the assets to the Hostinger server.
   • Verify the deployment via the live URL.

Conclusion: The Future of DevOps

The integration of Claude Code, Cursor, and MCP represents a move toward Autonomous DevOps. By leveraging specification files for context and MCP for infrastructure interaction, the barrier between "writing code" and "running a service" is virtually eliminated. The developer's role is evolving from a writer of syntax to a curator of specifications and an orchestrator of intelligent agents.