A customer asks whether an order has shipped. A content manager wants a product article prepared from approved catalogue information. An operations manager needs a summary of pending requests. An AI assistant becomes more useful when it can work with the information behind these tasks.
Model Context Protocol, or MCP, provides a standard way to connect AI applications to external systems. For a business website, the practical question is which information and actions an assistant should be allowed to use.
This guide explains MCP in plain English and outlines how I would evaluate an integration for a WordPress website, Shopify store, or custom Laravel application. The examples below are proposed workflows, not claims that every platform includes them by default.
What is MCP?
The official MCP introduction describes an open standard that connects AI applications with external data and capabilities. A compatible application can use an MCP server to access a defined set of tools and information.
Think of a business integration as a menu of clearly described operations. An assistant might be offered a tool to look up an order, search approved help articles, or create a draft support ticket. The integration defines the inputs and returns a result the assistant can use.
An order lookup still needs a real order system behind it. MCP gives the AI application a consistent interface; the developer supplies the connection and business rules.
How an MCP integration works
The MCP architecture documentation separates the AI application, its client connection, and the server that exposes capabilities. Servers can offer tools, resources, and reusable prompts.
- Tools perform defined operations, such as looking up a record or calling an API.
- Resources supply contextual information, such as a document or a set of records.
- Prompts provide reusable templates for structured interactions.
For a proposed order-support assistant, the sequence could be: a verified customer asks about delivery, the application selects an order-status tool, the server checks access, and the commerce backend returns the permitted shipment details. The assistant then explains the result.
The authorization check belongs in the backend. A confident answer from a model is not evidence that the requester owns the order.
MCP and REST APIs: where each fits
In an architecture I would propose, the existing REST API continues to handle the business operation. An MCP server translates a narrow tool request into a permitted API call and returns a structured result.
For example, a custom portal could retain its existing service for appointment availability. The MCP layer would expose an availability tool rather than offering unrestricted access to every portal endpoint.
A direct API integration may be the simpler choice when one application needs one fixed operation. I would consider MCP when compatibility with MCP-capable assistants is an actual requirement. Adding another service should solve a concrete problem.
Three useful business website examples
1. Store support with verified order information
For a Shopify or WooCommerce store, I would start with a staff assistant that retrieves order and shipping information after the backend verifies access. It could prepare a response for a support agent to review.
The initial scope could exclude refunds, address changes, and cancellations. Those operations need their own rules, permissions, and review process if the business later chooses to support them.
Measure whether the pilot helps staff find the correct record and prepare an accurate answer. Do not assume that connecting an assistant will automatically improve customer satisfaction.
2. Content preparation from approved records
A content team could ask an assistant to find approved product facts, prepare an article draft, and suggest relevant internal links. The integration would expose the specific records and draft-creation operation needed for that task.
I would keep publication as a separate editorial decision. A reviewer should check claims, image rights, tone, links, and product details before the article goes live.
This approach gives the assistant useful source material while preserving the team's publishing process.
3. An assistant inside a Laravel company portal
For an intranet, a useful starting point could be summarizing pending requests visible to the signed-in manager. A second tool might retrieve an approved company policy to explain the next step.
In a company portal with multiple organizations, every lookup must enforce both organization membership and the user's role. Request parameters alone should never determine which company's records are returned.
I would avoid beginning with payroll edits or unrestricted database queries. A narrowly defined read-only workflow is easier to assess against known records.
A person reviews a proposed business action on a laptop and selects a glowing confirmation checkmark. Conceptual AI-generated illustration.
Conceptual illustration: an assistant prepares a business action for a person to review.
Design access and review into the integration
The official MCP security guidance covers risks around authorization, token handling, and overly broad access. Its guidance includes validating that credentials are intended for the server and limiting permissions.
For a business website implementation, I would turn those principles into explicit acceptance criteria:
- Every request is tied to a verified user or service identity.
- The backend enforces record ownership, organization boundaries, and role permissions.
- Each tool accepts only the fields needed for its defined operation.
- Secrets remain on the server and are excluded from model responses and application logs.
- Sensitive changes show the exact proposed action for review.
- Operational logs record outcomes without copying unnecessary personal data.
Retrieved documents, customer messages, and product descriptions should also be treated as untrusted content. A sentence inside a support ticket must not become permission to export customer records.
Review prompts need to be specific. Before updating a booking, show the booking reference, current date, proposed date, and affected customer. A vague confirmation leaves too much room for misunderstanding.
How I would plan a first MCP pilot
- Choose one repeated task with a clear owner and an observable result.
- Identify the authoritative data source and confirm that the required API access is available.
- Define a small set of tools with clear inputs, outputs, and failure responses.
- Implement authorization and business rules before connecting the model.
- Test correct requests, missing records, denied access, stale data, and service failures.
- Pilot with a small staff group and compare results with the current workflow.
A useful acceptance test is simple: if two users ask about the same private record, does only the authorized user receive it? Another is whether the assistant admits that a lookup failed instead of inventing a status.
For write operations, test retries too. A temporary timeout should not create two bookings or send the same notification twice.
Frequently asked questions
Does MCP train an AI model on my website?
Connecting an external system through MCP provides an access mechanism. Model training and data retention are separate matters that must be checked in the chosen AI service's terms and settings.
Can any website connect to an AI assistant through MCP?
A working integration needs a compatible client, an MCP server, and a permitted way to reach the website's data or functions. Check those requirements for the specific services before promising a feature.
Do I need MCP for a basic FAQ chatbot?
For a chatbot that only answers questions from a small set of approved documents, I would first assess a simpler retrieval setup. MCP becomes worth evaluating when access to external tools or multiple compatible clients is part of the requirement.
What should a business measure?
For a support pilot, measure lookup accuracy, time to prepare a response, and how often a person must correct it. For content drafting, track review effort and factual errors. Use the results to decide whether to expand the scope.
Planning an AI integration for your website?
I develop WordPress websites, Shopify themes, Laravel applications, and custom REST API integrations. If you are exploring an AI-assisted workflow, start with the task you want to improve and the systems that hold the relevant information.
You can explore my development projects and get in touch through my portfolio to discuss the workflow, access requirements, and a practical first version.
Sources and further reading
MCP: What is the Model Context Protocol?
Official documentation reviewed on 22 October 2026. Business examples and implementation recommendations are my proposed approaches.
