The Model Context Protocol, explained for business and IT leaders

What MCP is, in plain terms

Before MCP, every AI application needed its own custom integration for every system it touched. MCP standardizes that connection. A system is wrapped once in an MCP server, which describes what it offers, and any compatible AI application can discover and use it. The protocol's own documentation compares it to a USB-C port for AI applications.

An MCP server offers three kinds of things. Tools are actions the agent can call, such as looking up a customer or creating a ticket. Resources are data the agent can read, such as file contents or database records. Prompts are reusable templates for common tasks. The AI application, called the host, connects to one or many servers through clients it manages, either locally or over HTTP to a remote server.

When MCP helps a business

MCP earns its place when several agents or AI applications need the same systems. A well-built MCP server for your ERP or your document store can serve an order agent, a finance assistant and an engineer's coding agent alike, with one place to manage access.

It also lowers switching costs. Because the protocol is open and supported across the major AI assistants and developer tools, the integration you build isn't tied to one model vendor. If a better model appears next year, the connections to your systems stay where they are.

When MCP isn't the answer

For a single production agent with one tightly specified write, such as pushing an approved order into a legacy database, a direct, well-tested integration is often simpler. The USCAPE order agent's push into FileMaker needed idempotent retries, exact naming from a nightly catalog mirror and a full audit trail; those are properties of the integration, and a protocol layer doesn't supply them.

MCP also doesn't solve messy data, unclear processes or missing approval rules. Connecting an agent to a system it shouldn't act on, or to data nobody has cleaned, just makes the problem reachable faster.

The security questions to ask

An MCP server is a door into a business system, so it deserves the review any integration gets. The protocol's published security guidance covers attacks specific to MCP, including confused-deputy authorization flaws in proxy servers, passing tokens straight through to downstream APIs, server-side request forgery, and compromised local servers. Its advice on scope is the one to start with: grant the least access a task needs.

On Native builds, the rules about what an agent may do live in a layer between the agents and anything that can send or write, so none of them depends on the model behaving well. Reading paths hold no write tools, which means a document that tries to instruct the agent has nothing to act with. Every write starts switched off and is enabled one action at a time. More on our approach is on Security.

• Who does the server act as? A shared service account hides who did what; per-user authorization keeps the trail.
• Which tools write? List every tool that changes data or sends anything outside the company, and who approves each one.
• Where does the server run? Local servers run with the user's own access to the machine; remote servers need proper authentication and network controls.
• Who vetted it? Treat a third-party MCP server like any dependency: read it, pin its version, and review changes.
• Is every call logged? You should be able to reconstruct which agent called which tool, with what input, and what came back.

How to start with MCP

• Start from a workflow, not a protocol. Pick the process you want an agent to run, then list the systems it needs to read and write.
• Expose reads first. A read-only server for one system lets teams learn the protocol with little risk.
• Add writes one at a time, each behind an approval rule and a switch that starts off.
• Keep servers in your name. Code, hosting and credentials belong to your company, so any agent you adopt later can use them.
• Test with the protocol's Inspector and your own evals before any server reaches production data.