Model Context Protocol: The AI Integration Layer for Modern Business

This four-part series through Gun.io explores how Model Context Protocol (MCP) bridges the gap between large language models (LLMs) and real-world systems. If you’re building with AI—or just trying to make your software more intelligent—MCP offers a lightweight, standards-based way to expose your existing tools and APIs to natural language interfaces.

In Part 1, Model Context Protocol: The Missing Layer Between AI and Your Apps, we introduce MCP through a real-world scenario where an executive queries multiple systems by voice while out for a run. The article lays the foundation for how LLMs can take structured actions in real systems.

In Part 2, Wrapping an Existing API with MCP: How to Expose Your Current APIs to LLMs, we walk through how to make an existing API—like a support ticket system—LLM-friendly by defining it as a tool. With just a few lines of code, your endpoints become accessible via plain-language prompts.

In Part 3, Building a Standalone MCP Server (Coming Soon), we walk through how to build a standalone server using MCP. Instead of wrapping an existing API, you’ll define purpose-built tools and serve them directly to large language models.

In Part 4, Creating Business Workflows with LLMs and MCP (Coming Soon), we go a step further and show how to compose tools into intelligent workflows that automate tasks across your stack, from CRMs to calendars.