MCP: The Plumbing Behind Production AI

Stephanie Smith • 15 Dec 2025 • 4 min read

You’ve probably heard MCP (Model Context Protocol) described as “USB-C for AI.” The analogy is simple: it’s a standard connector for plugging AI applications into the tools and data they need.

Here’s the practical definition:

MCP standardizes the integration layer between an AI host application and the tools/data it can access, so teams don’t rebuild custom glue for every model, every assistant surface, and every system.

The problem MCP is trying to solve

In real products, AI usually needs to operate inside workflows. That often means it must be able to:

• pull context from repos, docs, tickets, logs, and runbooks
• query systems like databases and internal services
• take actions like creating issues, opening PRs, or running checks
• do it with permissions, approvals, and audit trails

Without a standard approach, these integrations tend to get implemented separately in each AI app. Over time, that creates inconsistency: tool schemas drift, access rules vary by surface, and auditing becomes harder than it should be.

MCP exists to make this layer consistent.

What MCP is (and what it isn’t)

MCP is a protocol: a standard way for a host application to discover capabilities and call them using structured request/response messages.

A few quick clarifications:

• MCP is not a model.
• MCP is not an agent framework.
• MCP does not decide behavior.

MCP doesn’t make models smarter. It makes “model + tools” easier to build, reuse, and operate.

The architecture that makes MCP useful

An MCP setup typically includes:

• a host application (the UI and control plane)
• an MCP client inside the host (the mediator)
• one or more MCP servers (tool/data providers)

The key design choice is simple:

The model doesn’t talk directly to tools.
Requests flow through the host, which is where permissions, confirmations, and logging can live.

MCP is intentionally simple under the hood

MCP is intentionally boring under the hood: structured request/response messages, usually JSON.

That simplicity is exactly why it holds up in production—you can trace what happened, log it cleanly, and audit it later. And when something goes sideways, you can reconstruct the exact sequence of calls instead of guessing.

The three building blocks

MCP organizes capabilities into three primitives:

Tools (actions)

Tools are named operations exposed by a server: search logs, query a database, create a ticket, run a check. The model can request a tool call, but the host controls execution and can apply policy or require approval.

Resources (context)

Resources are fetchable context objects: files, docs, runbooks, schemas, and similar read-only references. This makes context access more explicit and easier to audit.

Prompts (reusable intent)

Prompts are parameterized templates exposed by servers, which helps keep common “how we ask for this” patterns consistent across different AI clients.

What an MCP interaction looks like

At a high level:

1. The host connects to an MCP server and initializes.
2. The host discovers available tools/resources/prompts.
3. The model requests a capability.
4. The host enforces rules and either executes or denies.
5. The server returns a structured result.

How this looks in practice

Consider a support triage assistant. To be useful, it needs access to real context and a small set of controlled actions—without turning into a free-for-all.

With MCP, the host (your support app) can connect to servers that expose:

• resources like ticket history, runbooks, and known-issue notes
• tools like searching similar tickets, checking service status, or drafting an escalation

When the assistant needs context, it fetches specific resources rather than pulling in everything “just in case.” When it suggests an action (like escalating a ticket), the host can require approval before executing it. The end result is a workflow that is easier to trust, easier to audit, and easier to extend as your tooling grows.

Why MCP makes business sense

MCP can sound technical, but the benefits map directly to cost, speed, and risk.

1) Lower integration cost over time
Instead of building and maintaining separate integrations for every AI surface, you invest in one consistent interface. That reduces duplicated effort and makes new AI features cheaper to ship.

2) Faster delivery across teams
When tools and data sources are exposed in a standard way, teams can reuse integrations instead of starting from scratch. That improves throughput and reduces dependency bottlenecks.

3) Better governance and compliance
When access flows through the host, it becomes easier to enforce permissions, require approvals for sensitive actions, and maintain audit logs. That matters in regulated environments—and it matters any time “AI did something” becomes a business risk.

4) Less vendor lock-in
If your tool layer is standardized, swapping models or adding new AI assistants becomes easier. You’re not rewriting the integration stack every time your model strategy changes.

5) More reliable operations
Structured calls and consistent logging make AI behavior easier to debug. That reduces incident time and makes systems more dependable as they scale.

A simple way to decide if MCP is worth it

MCP tends to be a strong fit when you expect growth: more tools, more integrations, more AI surfaces, and higher requirements for governance and auditability.

If you’re building a small prototype with one integration, MCP may be unnecessary overhead at first. In that case, it can make sense to validate the workflow first, then standardize once you know it’s worth operating long-term.

Where this is heading

MCP doesn’t change what a model can think. It changes what your system can reliably do with that model.

As soon as AI needs real access to internal systems, the integration layer stops being a side detail and becomes core infrastructure. MCP is one of the clearest signs that the industry is standardizing that layer—so AI features can scale without turning into a tangle of one-off integrations.