What is MCP? Model Context Protocol is a common way for an AI application to discover and use approved tools, data and services. It can help an assistant move beyond answering from its existing knowledge, but it does not automatically make the assistant accurate, secure or authorized to act.
The easiest comparison is a standard electrical outlet. A manufacturer does not redesign the wall connection for every appliance. The outlet provides an agreed way to connect, while the appliance still determines what happens after power reaches it.
MCP serves a similar purpose for AI applications. It gives applications and external services a shared method for describing what is available, what information an action requires and what it may return. The connected service still owns the underlying business logic, permissions and data.
This distinction matters. I have spent much of my career connecting ERP, analytics, cloud and operational systems. Creating a connection has rarely been the hardest problem. The harder questions are usually what the connection should expose, whose authority it uses, how failures are handled and who remains accountable for the result. MCP does not remove those questions.
What is MCP in plain English?
MCP stands for Model Context Protocol. It is an open protocol for connecting AI applications with external data sources and tools through a standardized interface.
Without a common protocol, developers may need to create a different custom connection between each AI application and each business system. One assistant may need a special connection to a document repository, another to a service desk and another to an order system. Those connections can use different definitions, security approaches and maintenance practices.
MCP provides a consistent language for these interactions. An AI application can ask an MCP server what capabilities it offers. The server can describe those capabilities and accept properly formed requests.
The official Model Context Protocol documentation describes MCP as an open protocol for integrating AI applications with external data sources and tools. Although the underlying implementation is technical, a business reader can understand MCP through three parts:
| Part | Plain-English meaning |
|---|---|
| MCP client | The AI application that wants to use an outside capability |
| MCP server | The service that describes and provides approved capabilities |
| Capability | Something the application can read, request or ask the service to perform |
The client does not need to know every internal detail of the connected system. It needs to understand the agreed interface, provide the required information and process the response.
A simple way to picture MCP
The following visual shows the relationship without the technical terminology. A person makes a request. The AI application identifies an appropriate approved capability. The connected service retrieves information or performs the permitted action.
The plug comparison is helpful, but it has limits. An electrical outlet does not decide which appliance should receive power. An AI application may select a tool based on a request and the descriptions it has been given. A weak description, excessive permission or misunderstood request can lead to the wrong action.
Therefore, the most important design question is not whether an organization can connect a system. It is what the AI should be able to reach, under whose identity and within which boundaries.
Three practical MCP examples
1. An employee needs an answer from several approved sources
Imagine an employee asking, “How many vacation days do I have, and what is the policy for carrying them into next year?”
The policy may live in a document repository, while the employee’s balance sits in an HR system. An AI assistant relying only on its general knowledge cannot safely answer either part. It needs current company policy and user-specific information.
MCP could give the assistant a standard way to discover a policy-search capability and a separate leave-balance capability. The assistant could combine the two results into one response, provided that the employee is authorized to see the information.
However, MCP does not decide whether the employee should have access. The identity, permissions and business rules still need to come from the organization and its systems.
2. A finance team investigates an invoice exception
An accounts-payable analyst may ask why an invoice did not match its purchase order. Answering could require the invoice, purchase-order lines, goods-receipt records and the company’s matching policy.
An approved service could expose read-only capabilities for retrieving those records. The AI application could assemble the evidence and explain that the invoice quantity exceeds the received quantity or that the unit price falls outside the allowed tolerance.
This could save investigation time. Nevertheless, the connection should not automatically approve the invoice merely because it found a plausible explanation. Approval is a separate business decision with its own authority and evidence requirements.
This pattern reflects a lesson I emphasize in AI to ROI for Business Leaders: production value comes from fitting AI into a controlled workflow, not from producing an impressive isolated answer.
3. An operations team investigates a service problem
Suppose a manager asks, “Which customer orders are affected by this warehouse interruption?”
The answer might require incident details from a service platform, delayed shipments from a logistics system and customer commitments from an order system. MCP can provide a reusable way for the AI application to access approved capabilities from each source.
The assistant could create an impact summary and draft a communication. A manager could then verify the affected orders and approve the message. This is more useful than asking a chatbot to speculate from an incomplete description of the incident.
It is also more complex. The systems may use different customer identifiers, timestamps and definitions of “affected.” MCP helps with access, but it does not reconcile inconsistent business meaning. Data definitions and relationships still matter.
What MCP is not
MCP is not an AI model
MCP does not generate answers or reason through problems. The AI model or application performs those tasks. MCP provides a way to reach outside capabilities.
MCP is not the same as an AI agent
An AI agent may interpret a goal, choose steps and use tools. MCP can be one way of making tools available to that agent. An agent can also use other forms of integration, and an MCP connection does not automatically create an agent.
My introductory guide to AI agents for business explains agents from a non-technical business perspective.
MCP does not replace APIs
Many MCP servers use existing application programming interfaces underneath. The API remains the system’s actual connection. MCP can describe and present selected capabilities in a form that AI applications understand consistently.
In practical terms, MCP may sit above existing services rather than replacing them.
MCP is not a security system by itself
MCP supports authorization patterns, but organizations still need identity, access controls, consent, audit records and secure implementation. The official MCP authorization guidance recommends authorization when servers access user data, require consent or need to record who performed an action.
The protocol’s security guidance also describes risks such as excessive permissions, token misuse and unsafe proxy behavior. A standardized connection can make reuse easier, but reuse can also spread a badly designed permission model.
When MCP can provide business value
MCP is most useful when a capability should be reusable across several AI applications or workflows. For example, an organization might create one governed service for retrieving approved product information rather than rebuilding product access for every assistant.
Potential benefits include:
- Less duplicated integration work across AI applications
- A consistent description of available tools and information
- Clearer separation between the AI application and the business system
- More reusable controls, logging and access patterns
- Greater flexibility to change the AI application without rebuilding every connection
These benefits depend on implementation. A company can still create dozens of overlapping MCP servers with unclear ownership and inconsistent definitions. Standardization at the protocol level does not guarantee standardization at the operating-model level.
That is why MCP should connect to a broader AI operating model. Someone must decide which capabilities become shared products, who operates them, who pays for them and which business teams may use them.
When MCP may be unnecessary
Not every AI connection needs MCP. A conventional integration may be simpler when one application uses one stable service for a narrow, well-understood task.
MCP may also be premature when the organization has not defined the underlying data, owner or permission boundary. Wrapping an unstable process in a standard protocol makes the instability easier to reuse.
I would ask five questions before selecting MCP:
- Will several AI applications need this capability?
- Can the organization describe the capability clearly?
- Can access be limited to the information and actions the task requires?
- Can the requesting person or service be identified reliably?
- Can requests, responses and resulting actions be monitored?
If the answer to most of these questions is no, the organization probably has business-design work to complete before choosing an integration pattern.
Use the MCP business-fit worksheet
I created a one-page worksheet to help business, technology, data and risk leaders decide whether MCP fits a proposed use case. It deliberately starts with the business task and the person who needs the result, rather than beginning with the protocol.
The worksheet also includes a valid outcome that technology discussions often neglect: a normal application connection may be enough. Choosing the simpler option is not a sign of lower maturity.
Questions business leaders should ask
Business leaders do not need to review protocol messages. They should understand the operating consequences of the connection.
| Question | Why it matters |
|---|---|
| What information can the AI retrieve? | Broad retrieval can expose sensitive or irrelevant data. |
| What actions can it perform? | Reading a record and changing it create different levels of risk. |
| Whose identity and permission are used? | A shared service account can hide who initiated an action. |
| How does a user confirm an important action? | High-impact actions may require explicit approval. |
| What happens when a system is unavailable? | The AI should not invent missing information or continue blindly. |
| Who owns the connection after launch? | Someone must maintain definitions, permissions and reliability. |
These questions also belong in an AI governance framework. Governance should address the actual data, decisions and actions exposed through a connection, rather than treating every MCP server as equally risky.
MCP should make integration reusable, not invisible
MCP can reduce the custom work required to connect AI applications with external capabilities. That is useful, especially as organizations introduce more assistants and agents.
However, easy discovery should not become invisible authority. People should know when an AI application is retrieving company information, which systems it can reach and when it may perform an action.
My view is simple: use MCP when it creates a genuinely reusable and governable capability. Do not adopt it merely because it has become part of the AI vocabulary. Begin with the workflow, define the decision and expose only the capabilities the task requires.
What are you trying to build with AI? Share the business workflow, the data or systems involved, and the point where the connection or permissions are becoming difficult in the comments.