Model Context Protocol (MCP)

What is Model Context Protocol (MCP)?

MCP defines how an AI application talks to the systems it needs to work with. On one side is an MCP client, built into an AI assistant, a code editor or a custom AI agent. On the other side are MCP servers, small services that expose a system's capabilities in a standard format: tools the model can call, such as "create a ticket" or "search orders", resources it can read, such as files or records, and reusable prompt templates.

When the AI application starts, it asks each connected server what it offers. The model then sees those tools with their descriptions and can decide to call them while working on a task, with the client passing requests and results back and forth.

Anthropic introduced MCP as an open specification, and many AI applications and developer tools now support it. For a business, it means one server for your CRM or internal database can be reused by several assistants and agents, instead of a separate connector for each.

Why does MCP matter for businesses?

Without a shared standard, every AI tool needs its own custom integration with every system. Connecting a few assistants to a few business systems quickly becomes a pile of one-off connectors to build and maintain.

MCP turns that into a plug-in model. A team builds or adopts an MCP server for a system once, and any compatible AI client can use it. That makes it practical to give assistants access to real company data and actions, which is what moves them from answering general questions to doing useful work.

It also gives a clear place to apply control. The server decides which actions exist, what data is exposed and which credentials are used, so access can be designed and reviewed in one spot rather than scattered across prompts and scripts.

How is MCP different from an API?

An API is how software talks to a system: endpoints, parameters, authentication. MCP does not replace it. An MCP server usually sits on top of an existing API and describes its capabilities in a way a language model can understand and use.

The differences are audience and discovery. A regular API integration is written by a developer who reads the documentation and hard-codes which calls to make. With MCP, the AI client discovers the available tools at runtime, reads their descriptions and decides when to call them based on the task in front of it.

So the two work together. The API provides the capability; the MCP server packages a curated, safe subset of it for AI use, with names and descriptions written for a model rather than a programmer.

What are the security risks of MCP?

Connecting a model to real systems brings real risk. A model with access to email, files and a CRM can be tricked by instructions hidden in content it reads, known as prompt injection, into calling tools it should not. Third-party MCP servers can also be poorly built or malicious, exposing more data than intended.

Sensible controls:

• install servers only from sources you trust, and review the tools each one exposes;
• give each server the narrowest credentials and scopes possible;
• separate read-only tools from tools that change data, and require approval for the latter;
• log tool calls and review them, especially early on.

These controls fit into a broader AI governance approach that sets who may connect which systems to which AI tools.

How do you get started with MCP?

Start by listing the systems your team most often copies information to and from when using AI: the CRM, helpdesk, project tracker, internal database or document store. Check whether a maintained MCP server already exists for each; many common business tools now offer one.

Then decide what the assistant should be able to do. Read-only access, such as searching tickets or looking up an account, is a low-risk first step that shows value quickly. Write actions, such as updating a record or sending a message, can follow once access rules and approval steps are in place.

For internal systems without an off-the-shelf server, building one is usually a modest project on top of the existing API. The real design work is choosing which tools to expose and describing them clearly enough that the model uses them correctly.

How does Vizio AI work with MCP?

MCP sits within the API and systems integration work of our DX Studio. We connect AI assistants, agents and internal copilots to the CRM, finance, marketing, product and support tools a business already uses, building MCP servers where none exist and configuring trusted ones where they do. We decide with the team which actions to expose, keep credentials and scopes narrow, and add approval steps for anything that changes data. These connections then power AI-powered internal workflows such as research, reporting and decision support, and we support them after launch as tools and APIs change.

A first step is usually listing the systems your team needs AI to read from and act on, ranked by value and risk.