One standard plug for every tool an agent uses
MCP stands for the Model Context Protocol. It is an open standard that Anthropic published in late 2024 for connecting AI applications to tools and data. The connection runs through small pieces of software called MCP servers. Each server sits in front of one system, like a code repository, an issue tracker or a file store, and tells the AI application what it can read there and what actions it can take.
The simplest way to picture it is a common plug. Before a standard like this, every AI product had to build its own custom connection to every tool. With MCP, a tool can offer one server, and any application that speaks MCP can use it.
For most people who use an agent, MCP is plumbing. You will rarely see it. But it shapes what an agent can reach, and it is worth knowing what to ask about.
Why agents need a way to reach your systems
An AI model on its own can only work with what it is given in the conversation. It cannot see your invoices, your calendar or your shared inbox unless something connects it to them.
That connection is what turns an assistant that gives advice into an agent that does work. An agent chasing invoices needs to read your accounting software and send email. An agent booking appointments needs your calendar. An agent watching ad spend needs your ad accounts.
Each of those is a separate system with its own way of being reached. MCP gives AI applications one shared way to ask a system what it offers and to use it.
How MCP works, in plain terms
There are three parts to keep in mind.
- The AI application. The agent or assistant you use. It decides what needs doing and which tool to call.
- The MCP server. A small program that stands in front of one system. It describes the tools that system offers, like “find a customer” or “create an invoice,” and the data it can share.
- The system itself. GitHub, Linear, your error tracker or your documentation. The server talks to it on the application’s behalf.
When an agent needs to do something, it asks the server what tools are available, picks the right one and calls it. The server carries out the action in the real system and sends the result back.
An example with an AI software engineer
Take an AI software engineer on a product team. Its work spans several systems. The code lives in GitHub, the tickets in Linear, production errors in Sentry, designs in Figma and the team’s decisions in Notion. Without a common connection, someone on the team opens each of those apps in turn to gather what the agent needs, then pastes it in.
With MCP, each of those systems is connected once through its own server, and the agent reaches all of them from the one place the team already talks to it. A product manager writes a single message asking it to fix the checkout bug in this week’s Linear ticket.
- It reads the ticket. Through the Linear server it opens the ticket, the linked customer reports and the acceptance criteria.
- It finds the error. Through the Sentry server it pulls the stack trace and how often the error happens.
- It checks the design. Through the Figma server it reads the frame the fix has to match.
- It changes the code. Through the GitHub server it opens a branch, makes the fix and opens a pull request that links the ticket.
- It reports back. It moves the Linear ticket to review and replies to the product manager in the same thread with the pull request and what it could not confirm.
Nobody opened five apps. Each step still ran with only the access that system’s server granted, so the agent could open a pull request but not merge it, and read errors but not change production. That is the point of MCP for a team. One place to ask, many systems answering, and each connection kept as narrow as its job.
What MCP changes for a business
MCP does not make an agent smarter. It makes it easier to connect. It is the same idea that lets a coding agent reach a codebase, its tests and its issue tracker. That has a few practical effects.
- More tools can be reached. When a system offers an MCP server, any application that speaks MCP can use it. Products do not each need to build their own connection.
- Connections can be swapped. If the team moves its tickets from Jira to Linear, the agent may only need the new system’s server, not a new product.
- Each connection is its own decision. You connect the systems you choose. Connecting your calendar says nothing about your bank account.
- Access is easier to see. Because each server lists what it offers, you can ask exactly which actions an agent has in each system.
What MCP does not change is the need for judgment about access. A standard plug still carries whatever is on the other end of the wire.
MCP compared with other ways to connect
MCP is not the only way to connect software. It helps to see where it sits next to the methods you may already use.
| Method | What it is | What to ask |
|---|---|---|
| A built-in integration | A connection one product builds for one other product | Which systems does it cover, and who keeps it working when either side changes? |
| An automation tool with trigger and action rules | Fixed rules that move data when something happens | Can it handle cases the rules did not foresee, and who updates the rules? |
| A direct API connection | Custom code that calls a system’s interface | Who wrote it, who maintains it and what access does it hold? |
| An MCP server | A standard way for any AI application to use a system’s tools and data | Who publishes the server, what tools does it expose and what access does it need? |
These are not rivals. Many businesses will use several at once. MCP matters most where an AI agent, rather than a fixed rule, decides which action to take.
What to check before you connect a system
You do not need to read the protocol to make good decisions about it. You need to ask the same questions you would ask before giving a new employee a login.
Who made the server
A server is software that can act inside one of your systems. Check whether it comes from the maker of that system, from the agent product you use or from someone else. Ask who keeps it up to date.
What tools it exposes
Ask for the list of actions the server offers. A server for your accounting software might offer reading invoices, creating invoices and recording payments. You may want the first and not the others.
What access it holds
A server usually connects with an account or a key. Check what that account can do in the real system. If it can delete records, the agent may be able to delete records, whatever the tool list says.
Which actions need approval
The agent, not the server, usually decides when to ask you. Check that anything that moves money, speaks to customers or changes records you rely on goes to you first.
What gets recorded
Every tool call should show up in the agent’s record, with what was asked, what was done and what came back. If you cannot see the calls, you cannot check the work.
A simple way to start
Begin with read access to one system. Let an agent read your calendar or your inbox and report what it would do. Look at its record for a week. Then grant one action, like drafting replies, with approval on each send. Widen access only when the record shows the agent using what it already has well.
| Stage | Access | Approval | What you learn |
|---|---|---|---|
| First week | Read only, one system | Not needed | Whether it understands your work |
| Next | One action in that system | On every action | Whether its actions match what you would do |
| Then | A few actions | On anything that matters | Whether it can carry a job on its own |
| Later | A second system | Same rules as the first | Whether it works well across systems |
This is the same way you would bring on a new hire. MCP makes the connections easier to build, and the pace of trust is still yours to set.
Maui is one product that lets an owner connect MCP servers for its agents to use.
