AI assistants become far more useful when they can retrieve information from the systems we use and perform tasks on our behalf. MCP makes these connections easier to build. Here, we explain what an MCP server is, how it works, and what the technology could mean for your business.
What is an MCP server, and why should you care?
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In brief: What is an MCP server?
An MCP server is software that makes data and tools available to an AI application through the Model Context Protocol. For example, it can give an assistant access to a content management system, a database, or a CRM.
What is MCP?
MCP stands for Model Context Protocol. It is an open standard for connecting AI applications to external data sources and tools. It allows information and functionality from different systems to be accessed through a common interface.
Think of it as a standardised way for AI assistants to work with the systems you already use.
How does an MCP server work?
An MCP setup consists of an AI application, an MCP client that manages the connection, and an MCP server that makes data or functionality available.
- You ask the AI assistant to do something, such as retrieve an article from your website.
- The assistant selects a relevant tool offered by the MCP server.
- The server retrieves information or performs an authorised action through a system or API.
- The result is returned to the AI assistant.
An MCP server can offer tools, resources, and prompt templates. It can run locally or on a remote server.
How can businesses use MCP?
For example, an AI assistant can help retrieve sales data, find project documents, or create content in a CMS, provided the right integrations and permissions are in place.
- Websites and CMS: Find articles and create drafts in Craft CMS.
- CRM: Look up customer data and check the status of sales processes.
- Projects: Bring together information from documents and task management tools.
- Customer service: Find relevant documentation to prepare better responses.
In a well-designed workflow, the AI assistant often prepares a change for a human to approve.
MCP vs. API: What's the difference?
An API defines how software communicates with a particular service. MCP defines how AI applications can discover and use tools and resources from external systems in a standardised way.
An MCP server often uses existing APIs behind the scenes. MCP therefore does not replace APIs. It makes it easier to connect a range of services to AI assistants.
What about security?
When AI is given access to business systems, the same rigorous security standards must apply as with any other integration.
- Grant only the permissions needed and enforce access controls on the server side.
- Use secure authentication and protect access tokens.
- Require approval for publishing, deletion, and other sensitive actions.
- Protect against malicious instructions in external content, often called prompt injection.
- Log important actions and assess the risks of third-party servers.
An MCP integration must never be treated as a shortcut around existing access controls.
How to get started
Start with one specific task. Identify a process where employees spend time gathering information across systems, and explore whether an MCP integration could make it easier.
Build a limited pilot, define who should have access, and measure whether the solution actually saves time. From there, you can expand into more advanced workflows.
At Good Morning, we build bespoke websites and digital solutions. We'd be happy to help you explore how MCP can connect your existing systems to AI assistants in a useful and secure way.
