MCP (Model Context Protocol): What It Is and Why Every AI System Will Use It

Model Context Protocol (MCP) is an open standard developed by Anthropic in late 2024 that defines how AI models connect to external tools, data sources, and systems. It is now supported by Claude, and increasingly by other major AI providers.

The analogy that makes it click: MCP is to AI agents what USB-C is to devices. Before USB-C, every device had its own connector. MCP standardises the connector between AI models and the tools they use — so you build an integration once and any MCP-compatible model can use it.

Why This Matters

Before MCP, connecting an AI model to your business systems required custom integration work for each model. If you wanted your AI to read from your database, call your API, and write to your CRM, you had to build that plumbing from scratch — and rebuild it when you switched models.

With MCP, you build an MCP server once for your database, your API, your CRM. Any MCP-compatible AI model can then use it immediately, without additional integration work.

How MCP Works

An MCP setup has three parts:

MCP Host — the AI application or agent that wants to use tools (Claude, your custom AI system, Claude Code)

MCP Server — a lightweight server you (or a vendor) runs that exposes tools and data. Examples: - A filesystem server that lets the AI read and write files - A database server that lets the AI query your PostgreSQL database - A Slack server that lets the AI send and read messages - A GitHub server that lets the AI read code and open pull requests

MCP Client — the protocol layer inside the host that discovers and calls MCP servers

When the AI needs to do something — search your database, read a file, post to Slack — it calls the appropriate MCP server through the standard protocol. The server executes and returns the result. The AI continues reasoning with that result.

What You Can Do With MCP Today

Connect your AI to internal data. Build an MCP server for your knowledge base, documentation, or database. Your AI assistant can then answer questions about your actual business — not just general knowledge.

Give AI agents real tools. An agentic AI with MCP can browse the web, run code, manage files, call APIs, and interact with business systems — all through standardised connectors rather than bespoke integrations.

Switch models without rebuilding. Because MCP is a standard, an MCP server you build for Claude works for any other MCP-compatible model. You are not locked to one AI provider.

Use the growing MCP ecosystem. Major tool providers (Cloudflare, Stripe, Notion, Linear, GitHub, and many others) have published official MCP servers. Your AI can connect to them in minutes.

The Business Impact

For companies building AI products, MCP reduces integration time from weeks to days. For companies using AI internally, it means your AI assistant can actually access your systems rather than living in a sandbox.

We are in the early stages of what MCP enables. As the standard matures and adoption grows, the gap between companies that have connected their AI to their systems and those that have not will become a meaningful competitive moat.

At TrueCodeAI, we build MCP servers as part of every enterprise AI deployment — ensuring the AI we ship can actually connect to your existing stack rather than operating in isolation.