What Is MCP? The Model Context Protocol, Explained for Business
by Prince Radebe, Technical Director
The most useful way to understand the Model Context Protocol (MCP) is by analogy: MCP is to AI what USB-C is to hardware. Before it, every connection between an AI assistant and a business system (your CRM, your database, your ticketing tool) was a custom, one-off integration. MCP replaced that with a single open standard: build one connector, and any MCP-capable AI application can use it.
What MCP actually is
MCP is an open protocol, introduced by Anthropic in late 2024 and since adopted across the industry, that defines how AI applications connect to external systems. The architecture has two halves:
- MCP servers expose a system's capabilities, such as "search the product catalogue" or "fetch this customer's invoices", as tools an AI can discover and call, plus data it can read.
- MCP clients are the AI applications themselves, such as chat assistants, coding agents, and internal copilots, which connect to any number of servers and use whatever they expose.
The key property is the decoupling. The team that builds the ERP connector does not need to know anything about the AI application that will use it, and vice versa. That is what turned a combinatorial integration problem (every AI × every system) into a linear one (one connector per system).
Why this matters beyond the development team
Most of the value of AI in a business is locked behind a simple problem: the AI does not know your data and cannot touch your systems. A general-purpose model can draft an email, but it cannot tell a customer their actual order status, because it has no connection to the order system. MCP is how that connection gets made: safely, through defined tools with defined permissions, rather than by pasting your data into a chat window.
Concretely, MCP is what makes these patterns practical:
- An assistant that answers from your systems: support queries answered against the real customer record, policy questions answered from the actual policy documents.
- Agents that do real work: reconciling invoices against orders, updating records across systems, assembling month-end reports from live data.
- One investment, many surfaces: the same MCP server that powers an internal copilot can power a customer-facing assistant later, because the standard is shared.
The part that deserves caution
Giving AI systems access to business systems raises exactly the questions you would expect: who can call which tools, with whose permissions, and what happens when the AI is wrong? A well-built MCP integration treats tool access like any other production access: scoped permissions, audit logs, human confirmation on destructive actions, and careful thought about what data leaves your boundary. Under POPIA, that last point is not optional: personal information flowing through AI systems needs the same governance as personal information anywhere else.
This is where the engineering matters more than the hype. The protocol makes connections possible; making them safe and reliable is a design discipline.
Where to start
The pragmatic first MCP project is usually read-only: connect an assistant to one high-value system (the knowledge base, the product database, the order system) and let it answer questions before it is allowed to change anything. That proves value in weeks, builds the security muscle, and creates the foundation the write-capable use-cases build on.
We build MCP integrations and AI systems as part of our AI development practice, grounded in the same engineering discipline as everything else we ship. If you are wondering what a first AI project should look like in your business, that page is the place to start, or talk to us about the system you wish your AI could see.