MCP Protocol:
The USB-C of AI Integrations

The Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI models communicate with external tools, APIs, and data sources. Announced in late 2024 and rapidly adopted across the AI ecosystem, MCP is solving one of the most persistent problems in applied AI: the lack of a standardised way for AI models to access and act on external systems.

The Problem MCP Solves

Before MCP, every AI integration required custom code: a specific connector for Google Drive, a different one for Slack, another for GitHub, and so on. Each connector used different conventions, authentication patterns, and error handling. Maintaining a suite of AI integrations was a significant engineering burden, and adding a new tool meant starting from scratch.

MCP introduces a universal standard. Any tool that implements the MCP specification can be connected to any MCP-compatible AI model with minimal additional code. It's the equivalent of USB standardising device connections - instead of a different cable for every device, one standard that works everywhere.

How MCP Works

MCP defines three core primitives: Resources (data the AI can read - files, database records, API responses), Tools (functions the AI can call - creating records, sending emails, running queries), and Prompts (reusable templates for common interactions). Servers expose these primitives; clients (AI applications) discover and use them.

When an AI agent needs to read a Google Doc, it calls the Google Drive MCP server's read_file resource. When it needs to create a Jira ticket, it calls the Jira MCP server's create_issue tool. The AI model doesn't need to know the implementation details of either system - just the MCP interface.

MCP in Practice: Business Applications

AI Assistant with Full Business Context

Connect your AI assistant to your CRM (Salesforce MCP), project management tool (Asana MCP), and communication platform (Slack MCP). Now your assistant can answer "What's the status of the Acme project?" by actually checking your project management tool - not just responding from its training data.

Multi-System AI Workflows

Build AI automation workflows that seamlessly read from and write to multiple systems. An invoice processing workflow reads from Gmail (MCP), extracts data, validates against Xero (MCP), creates a record in Salesforce (MCP), and notifies via Slack (MCP) - all orchestrated through a standard protocol.

Who Should Care About MCP?

If you're building AI systems that need to interact with more than one external service - which is virtually every serious business AI application - MCP should be part of your architecture. It reduces development time, simplifies maintenance, and makes your AI systems more extensible as new tools and services adopt the standard.