MCP (Model Context Protocol) is an open standard, introduced by Anthropic in late 2024, that defines how AI applications connect to external tools and data. Instead of building a custom integration for every model and system, a single MCP server exposes a tool once and any compatible AI client can use it.
Every AI project we take on eventually hits the same wall. The model is capable, but it lives in a box. It cannot see the CRM, it does not know yesterday's orders, it cannot pull a document from the shared drive. So you write an integration. Then another one for the next tool. Six months later you are maintaining a pile of custom connectors that all do roughly the same thing in slightly different ways.
MCP exists to kill that pile. It stands for Model Context Protocol, an open standard Anthropic published in late 2024, and it has quickly become the way AI applications talk to the systems around them.
What MCP actually is
MCP is a protocol, not a product. It defines a common language between an AI application and the tools or data it needs. The comparison that stuck is USB-C: before it, every device had its own cable; after it, one connector fits everything. MCP is trying to be that connector for AI.
The setup has three parts. The host is the AI application, for example an assistant or an agent. The client lives inside the host and speaks the protocol. The server sits in front of a system (a database, a CRM, a file store) and exposes it in the shape MCP expects. Write one server for your CRM and any MCP-capable AI can use it.
Why a standard matters
Without a standard, every combination of model and tool is a bespoke integration. Ten tools across three AI apps is thirty connectors to build and keep alive. With MCP the tool is described once, on the server side, and any client can consume it. The math changes from multiplication to addition.
It also means you are not locked to one model. If you build against MCP and later switch the underlying model, the servers keep working. It is the same reasoning behind designing systems API-first: the contract outlives the implementation.
The three things an MCP server exposes
A server can offer three kinds of things. Tools are actions the model can invoke, like "create an invoice" or "search orders". Resources are data the model can read, like a document or a database record. Prompts are ready-made templates for common tasks. Most business use starts with tools and resources.
Where it fits in a company
Concrete example. A support assistant that needs to check an order status, read the returns policy and open a ticket. Three systems. With MCP you expose each one through a server, and the assistant calls them through the same protocol. Swap the ticketing tool next year and you replace one server, not the assistant.
It is the natural layer under the AI agents we have written about: the agent decides what to do, MCP is how it reaches out and does it. The same goes for an assistant that answers from your documents, where the documents arrive through a server the assistant is allowed to read.
Security: the server decides, not the model
The model never gets a free pass. The MCP server controls exactly what is exposed and what each operation is allowed to do. The model can only call what the server offers, with the permissions the server enforces. Done properly, the AI touches your data through a narrow, auditable door rather than a master key.
When it is worth it, and when it is not
MCP pays off when an AI feature has to touch more than one or two systems, or when you expect those systems to change. For a single, stable integration a direct call is often simpler, and that is fine. The point of a standard is not to use it everywhere, it is to stop reinventing the connector every time the tool count grows. When we build AI into a business, this is usually where the plumbing decisions get made.
The interesting shift is not that AI can now use your tools. It is that connecting it stops being a custom job every single time. That is what turns an AI demo into something you can actually run and maintain.
