Guide

MCP servers: what they are and when you actually need one

MCP is an open standard for connecting AI applications to the systems where your data and tools live. Whether you need your own MCP server depends on what you want agents to do, and what you are prepared to let them touch.

The short answer

Short answer

An MCP server is a small service that exposes one of your systems, such as a database, a ticket queue or a document store, to AI applications through the Model Context Protocol, a shared standard.

You need one when an AI tool must reach a system nothing else connects to safely. You may not need one yet.

Today

Definition

MCP server: a service that presents a system's data and actions to AI applications through the Model Context Protocol, so any compatible AI application can discover and use them within the permissions the server allows.

What goes wrong when businesses rush into MCP?

The pitch is that every system should be "agent-ready", so a team builds an MCP server for an internal database and connects it to an AI assistant with broad access. Nobody decided which actions the assistant may take, and the server exposes far more than the job needed. Meanwhile the real need, a single report, could have come from an existing integration or an export.

Who needs to understand this?

Business owners, operations leads and IT leads being asked to invest in MCP, and the developers who would build or run the server. It matters most where the system holds customer data, money or anything that cannot easily be undone.

When do you need an MCP server, and when not?

Situation	Probably not yet	Worth considering
What the AI needs	A periodic report or a file	Live access to look things up during a task
Existing connectors	Your AI tool already connects to the system	Nothing connects, or only with too much access
Who will use it	One workflow	Several AI tools or agents
What it may do	Read only, rarely	Read often, with a few well-defined actions
Who governs it	Nobody assigned	A named owner for the server and its permissions

How it works, step by step

Deciding on an MCP server is a short governance sequence before anyone builds one.

01

Name the job, not the technology

Write down what an AI tool needs to do with the system: look up an order, read a policy, file a ticket. The protocol's own documentation calls MCP an open-source standard for connecting AI applications to external systems, but the standard is only worth adopting when there is a job it serves.

02

Check what already connects

Many AI tools already connect to common systems. If one does the job with the access you are comfortable with, use it.

03

List the actions, smallest first

If you do build a server, list each action it will expose, starting with read-only ones, and leave out anything the job does not need.

04

Plan for hostile input

Anything an agent reads can carry instructions. OWASP's GenAI Security Project warns that prompt injection vulnerabilities exist in how models process prompts, and that input may push a model into violating guidelines, enabling unauthorized access or influencing critical decisions. Consequential actions therefore need a person's approval, whatever the agent was told.

05

Human gate: a named owner approves what the server exposes

A named owner approves the list of actions, the accounts that may use them and which actions require a person each time. Nothing is exposed until they sign off.

06

Run it with its own credentials and logs

The server runs with its own limited credentials, and every call is logged with which AI application made it.

07

Review when the job changes

When someone wants a new action, it goes back to the owner, the same way the first list did.

Keeping it safe to run

What keeps an MCP server safe to run?

The smallest set of actions

Only what the job needs.

Read before write. Read-only actions first, writing only once read has proved useful.

A person on consequential actions

As approval gates describes.

Its own credentials

Never a person's administrator account.

Logs. Every call recorded and reviewable.

Where this fits

Concept

An MCP server is infrastructure under many possible workflows, which is why the Platform page is its natural home. A workflow worth connecting should pass the readiness checklist first. Agents that look things up inside systems are what exception detection and routing across your ops stack and internal request routing and triage depend on. The use case library has the workflows that might use one.

How does an MCP server differ from an API?

Question	An API	An MCP server
Who it is built for	Software written for that API	Any AI application that speaks the protocol
How actions are found	Documentation a developer reads	Described so an AI application can discover them
Relationship	Often already exists	Often built on top of an existing API
What still needs governing	Who can call what	The same, plus which AI tools may connect

Approval gates: keeping consequential actions behind a person.
Exception detection and routing across your ops stack: a workflow that reads many systems at once.
Internal request routing and triage: a workflow that could use one connection per queue.

AAOS Platform

How It Works

Questions, answered

Not necessarily. Many agents work through existing connectors, files or APIs. An MCP server is one way to connect, worth building when it serves a specific job.