MCP, short for Model Context Protocol, is an open standard for connecting an AI model such as Claude to the tools and data your business already uses. Instead of building a custom connection for every system, you expose each system once through MCP, and any MCP-capable AI application can use it. Think of it as a common plug shape between AI and your software.
The problem MCP solves
On its own, an AI model only knows what it learned in training and what you paste into the conversation. It cannot see your customer records, check a ticket, read a shared drive or update a spreadsheet. To be useful in real work, it needs a safe, repeatable way to reach those things.
Before a shared standard, every connection was a one-off project: one integration for the CRM, another for the document store, another for the ticketing tool, each written separately for each AI application. That is slow to build and hard to maintain. MCP standardises the conversation between the AI application and the system it wants to reach, so the connection can be reused.
How it works, without the jargon
There are two sides to an MCP setup:
- The MCP server is a small piece of software that sits in front of a system, such as a database, a file store or an internal service. It describes what that system can do and what information it can share.
- The MCP client lives inside the AI application. It connects to one or more servers, learns what they offer, and lets Claude use those capabilities during a task.
The result is that Claude can look something up or perform an action in a connected system as part of answering a request, rather than relying only on what someone pasted in.
Tools versus Resources: the distinction managers should know
An MCP server can offer different kinds of capability. Two matter most for decision-makers:
- Tools are actions. They do something: create a ticket, send a message, update a record, run a search. Because they change things or trigger work, they deserve the most care.
- Resources are read-only context. They supply information for Claude to read, such as a policy document, a product catalogue or a report, without changing anything.
This split is a useful governance lens. Giving an AI read-only access to reference material is a much smaller risk than letting it take actions in a live system. When your team proposes a connection, ask which side of that line it sits on. The same distinction appears in the Tool Design and MCP Integration material for the Claude Certified Architect exam; see our tool design and MCP domain page and the glossary for the exam-level definitions.
Why it matters for a business
- Less duplicated integration work. A well-built server for one system can serve many AI applications, instead of being rebuilt each time.
- Better answers. Claude can work from your current information rather than a stale copy pasted into a chat.
- Agentic workflows become practical. Agents that carry out multi-step work need to reach real systems. MCP is one common way they do it. Our explainer on what agentic AI is covers the bigger picture.
- A clearer control point. Because access is defined at the server, your technical and security teams have one place to decide what is exposed and what is not.
Questions to ask before connecting Claude to a system
- What exactly will it be able to see? Limit access to the smallest set of information the task needs.
- Is it read-only or can it act? Start with read-only context where you can.
- Who owns the server? Know who built it, who maintains it and who can change what it exposes.
- Where does personal data flow? If customer or employee data is involved, involve your legal and compliance team. Our guide to AI governance and the PDPA in Malaysia is a starting point; it is general information, not legal advice.
- Who approves actions? For anything that changes records or sends messages, decide whether a person should confirm first.
Do managers need to build MCP servers?
No. Building and securing a server is an engineering task. A manager's job is to understand the concept well enough to ask good questions, set boundaries and judge proposals. Engineers and solution architects are the people who design and implement the connections.
If your team is heading that way, the Claude Certified Architect workshop from Agmo Studio, a Select Partner of the Anthropic Claude Partner Network, covers tool design and MCP integration as one of its domains. Certifications are issued by Anthropic; Agmo runs the exam-preparation workshops.
Keep expectations realistic
MCP is plumbing, not magic. It makes it easier to connect Claude to systems, but it does not fix messy data, unclear processes or missing permissions. Pick one concrete task, connect only what it needs, test it with real examples and expand once your team trusts the results.