What is MCP (Model Context Protocol), and why does it matter?

What is MCP in plain English?

Think of MCP as a standard plug between AI and your systems. Before it, getting an AI assistant to query a database or use a tool meant building a custom integration for that specific assistant and that specific tool. MCP defines a shared language for that connection.

With a standard in place, a tool is exposed once and any compatible AI application can use it. It's a lot like a universal port: the value isn't the cable, it's that everyone agrees on the same way to connect.

MCP isn't an AI model or an app you install and forget. It's a public specification that describes how AI applications and the systems exposing data or actions should talk to each other. Any company or developer can implement it.

How does MCP work?

MCP follows a client-server model. The main pieces are:

• Host: the AI application a person uses or that runs a workflow.
• MCP client: the component inside that application that connects to each server.
• MCP server: a program that exposes a system's capabilities, such as your CRM, a database or a document repository.
• Tools: actions the model can request, like creating a record or looking up an order.
• Resources: information the server makes available as context, such as documents or data.

Why does MCP matter for connecting AI agents to business systems?

An AI agent is only useful if it can read from and act on the systems where work happens: ERP, CRM, email, ticketing or document storage. MCP cuts down on rebuilding the same integration over and over, and makes it easier for different agents to share the same connections.

It also cleans up your architecture. Instead of integrations buried inside each agent, every system is exposed through a server with defined capabilities that you can review, version and control in one place.

It also reduces vendor lock-in: if the connections to your systems follow an open standard, switching AI applications or models doesn't force you to rebuild every integration.

One caveat: MCP doesn't make an agent smart or safe on its own. It's the connection layer; business rules, permissions and oversight are still design work.

What are the security risks of MCP?

Connecting an AI model to real systems means it can trigger actions with real consequences. These risks aren't unique to MCP, but you need to account for them when you use it:

• Over-permissioned servers that expose more actions or data than the workflow needs.
• Prompt injection: an email or document can contain instructions that try to make the model do something unintended.
• Unvetted third-party servers: installing an MCP server is installing software with access to your systems.
• Poorly managed credentials: tokens and keys that leak or carry more scope than they should.
• No audit trail: not knowing which tool was called, with what data and why.

How should a company govern MCP?

Treat every MCP server like any other critical integration. These practices keep it under control:

• Expose only the tools the workflow needs, read-only wherever possible.
• Require human approval before sensitive actions such as payments, customer communications or data changes.
• Log every tool call so it can be audited.
• Use servers you built or reviewed, deployed in your own cloud account.
• Add MCP servers to your AI inventory and risk assessments, in line with frameworks like the NIST AI RMF and ISO/IEC 42001.

How do you get started with MCP?

Start the way you would with any agent: with one specific workflow. Define which systems it needs to read, which actions it should take and which ones need approval. Then design servers with the smallest set of capabilities that gets the job done.

Rival Tech is a nearshore AI team in Bogotá, Colombia, working US Eastern and Central hours. We build AI agents connected to your systems with least-privilege access, human approval and a log of every decision. Want to see one running? Email tech@soyrival.com for a free demo.

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