Understanding MCP: How to Connect AI to the Systems Your Business Already Uses

Artificial intelligence (AI) tools are becoming a regular part of how businesses work. From developing AI-assisted content workflows to developing a custom AI agent, there are plenty of promising possibilities. 

Unfortunately, you may face a common limitation: AI tools aren't always connected to the systems where your business really runs. Customer information might live in a CRM. Marketing data is spread across Google Analytics, advertising platforms, and other reporting tools. Sales and billing information exist in entirely different systems.

Even when AI can help you make sense of that information, someone may still need to gather the data, upload files, and manually complete the next steps. That's where Model Context Protocol (MCP) comes in.

For businesses looking to make AI more useful in their day-to-day operations, MCP may make a lot of sense. In this article, we’ll help you understand whether MCP makes sense for your organization.

Table of Contents:

• What Is MCP (Model Context Protocol)?
• How Can MCP Help Your Business?
• MCP vs. RAG vs. AI Agents: What's the Difference?
• How Do I Implement MCP?
• How Much Does an MCP Integration Cost?
• Is Your Business Ready for MCP?
• MCP Starts with the Right Strategy

What Is MCP (Model Context Protocol)?

Model Context Protocol (MCP) is a tool that allows AI applications to connect with external data sources, software, and tools. 

Think of MCP as a common interface that helps AI communicate with the technology and structure data your business already uses. Without a connection to your business systems, an AI application may only know what you tell it or what it can access through its existing capabilities. If you want ChatGPT or some other AI agent to analyze customer information, for example, you might need to export a report from your CRM and upload it yourself.

An MCP integration creates a standardized way for AI applications to request that information directly from the connected system, subject to the permissions you've established. MCP can also perform tasks on your behalf instead of just retrieving information. For example, your MCP could allow ChatGPT or some other AI agent to:

• Add a record to a database
• Update information in a CRM
• Bill a vendor
• Download reports
• Place an order on behalf of a customer

Sounds useful? You’re right. What’s even better is that MCP isn’t another AI platform you need to learn. MCP is a way to make existing AI tools more useful by connecting them to the systems where your business information and workflows already live. 

How Does MCP Work?

The beauty of MCP is that you don't need to understand every technical detail to see how MCP could help your business. However, a basic understanding of the different components can help clarify how it actually works.

MCP integration involves a few important pieces:

• The AI agent: The interface where someone interacts with AI, such as Claude, ChatGPT, or any other supported AI assistant or other application.
• The Large Language Model (LLM): The core artificial intelligence that interprets requests made through an agent and parses through massive amounts of information to provide results based on what it determines is the most likely correct answer.
• The MCP client: The part within the AI application that communicates with an MCP server.
• The MCP server: Software that makes specific tools, information, or actions available to the AI application.

In short, the MCP serves as the bridge between the AI agent you use and the external systems it needs to access. For example, let’s imagine that you asked ChatGPT, “What were our top-performing marketing campaigns last month?” If the agent has access to an appropriate MCP integration, the process could look something like this:

1. You ask the AI assistant to review campaign performance.
2. The AI application identifies an available tool that can retrieve the relevant data.
3. Its MCP client sends a request to the MCP server.
4. The MCP server communicates with the connected marketing platform and retrieves the permitted information.
5. The information is returned to the AI application, which uses it to prepare a response.

Instead of downloading multiple reports and manually providing the data, you can potentially access the information and have the agent perform tasks through a conversation with your AI assistant. 

A Simple Way to Think About MCP

If technical explanations of MCP integrations don’t help, think of an AI system like the human body.

The AI agent in this scenario is your nervous system. Let’s say you stubbed a toe on some furniture. Your nervous system swiftly signals that it most certainly did not like that and will reach out to the brain (in this scenario, the LLM) to share its displeasure and ask how it should address this discomfort. The LLM will scan the knowledge it has available and give you steps to attend to your distressed digits. 

The problem is that your brain can think of solutions, but it won’t do anything about them. An MCP integration serves as the arms and legs that an AI application needs to do that work. The AI model figures out what needs to happen and MCP has the tools needed to carry out a specific operation, which in this case is getting you that all-important ice pack. 

How Can MCP Help Your Business?

The ability to give AI tools access to structured data and systems gives businesses a direct path to reducing the manual work required to find information, move it between systems, and complete routine tasks.

Here are a few examples of how organizations can put MCP to work and improve efficiency and spend more time on higher-value work.

1. Access Business Information Without Switching Between Platforms

Most businesses rely on multiple systems to manage customers, marketing, sales, and operations. Finding specific information often means logging into several platforms, exporting reports, and combining the results (and that’s if you can even find what you need in the first place).

MCP gives your AI agent of choice a way to retrieve information from those systems without digging into each platform manually. For example, someone within your company can ask Claude to pull recent invoices, performance data, or some other information from a connected platform. That capability saves users a lot of time, and can be especially valuable for teams that regularly need information from different departments or systems.

2. Make Marketing Reporting and Analysis More Efficient

Marketing teams have access to more performance data than ever. The problem is that there’s not always time to properly turn that information into meaningful insights.

Let’s say a marketing manager who wants to know which website content generated the most leads over the past quarter. Answering that question might require several steps to thoroughly find the information needed, compare data from different systems, track performance, and find patterns. 

The right MCP integration and reliable tracking allow AI applications to retrieve all that relevant information and help organize it for analysis. Your systems still need to capture the right information, but that access through MCP can help teams streamline work with a few specific questions.

3. Reduce Repetitive Administrative Tasks

Complex data analysis is great, but sometime the most notable opportunity is eliminating small, repetitive tasks that take up time throughout the day. MCP integrations allow users to perform specific administrative tasks, such as:

• Entering lead information into a CRM
• Updating customer records
• Retrieving invoices
• Downloading reports

For example, imagine that a salesperson needs to add several new contacts to a CRM. Connecting a CRM through an MCP integration allows that salesperson to provide that information to an authorized AI tool. That tool can then create the records without the need for manually entering each record. 

MCP is a shining example of working smarter, not harder. Using MCP to streamline work won’t replace an entire process, but it can drastically reduce the manual steps to tackle these tedious tasks. 

4. Support Customer Transactions and Business Operations

Want to give customers another way to interact with your business (and ideally give you their money)? MCP can also create opportunities for users to interact directly with the underlying business systems to perform supported actions.

Traditionally, customers place orders, request information, or manage accounts by navigating a website or customer portal. An appropriate MCP integration can give customers the ability to request product information or start an order through an authorized connection. Employees can also use an AI application to look up inventory information or retrieve account details without navigating several different screens.

These capabilities don't remove the need for a website or customer portal, and the feasibility depends on the systems involved, the tools made available, and the permissions set up for each interaction. If it makes sense for your business, MCP is a great tool for introducing another way for AI applications to interact with your business.

MCP vs. RAG vs. AI Agents: What's the Difference?

If you’ve been exploring AI for your business, you’ll know that there are plenty of tools out there. The sheer volume of what’s available can be a tad overwhelming, especially when you don’t quite understand the difference between one acronym and another.

This conundrum can arise when you encounter terms such as retrieval-augmented generation (RAG), AI agents, and MCP. These technologies are related but solve different problems. As such, let’s do a quick breakdown of each to help you decide which tool (or multiple tools) makes sense for your business.

MCP vs. RAG: Accessing Information vs. Retrieving Knowledge

Both MCP and RAG help AI applications access information, but the source of that information differs. 

RAG is designed to help ChatGPT or some other tool retrieve relevant knowledge from a collection of information. For example, a company might build a RAG system using employee handbooks, technical documentation, product specifications, or internal policies. The resulting RAG system searches that knowledge library to provide answers that might not be readily available outside of your organization.

MCP works differently. Rather than focusing on a prepared knowledge collection, MCP connects AI applications to a CRM, analytics platform, or some other external system.

Let’s consider some examples:

• Example A: An employee needs to look up what the employee handbook says about vacation requests
  
  • What to Use: A RAG system connected to the company’s complete catalog of internal policies.
• Example B: An employee needs to retrieve current contact information for the five newest customers
  
  • What to Use: MCP-connected AI application with an authorized CRM integration.

Both users have legitimate goals but may need different means to access what they need. It’s also important to note that MCP and RAG can complement each other. A business could use RAG to make internal documentation searchable while using MCP to connect an AI application with its CRM, analytics platform, or other operational systems. It’s all a matter of what business problems you face, and which solutions address them best.

Want to learn more about RAG systems? Check out our guide on RAG systems and how to turn knowledge into a resource. 

MCP vs. AI Agents: The Connection vs. the Work

AI agents are most widely recognized as ChatGPT, Claude, and any other chat interface that generates answers for everything from streamlining marketing processes to figuring out what to make for dinner. Whether you use a chat interface or a custom, local AI agent, these tools are designed to work toward goals, often by coordinating multiple steps and using available tools.

MCP isn't an AI agent. Instead, it gives an agent access to external tools needed to complete certain tasks.
Imagine asking an AI agent to prepare a monthly marketing performance summary. The agent might need to gather data from several sources, compare results, identify trends, and prepare a report. The problem is that an LLM won’t have access to your internal systems and other private sources.

MCP integrations make this access possible. The agent wants to get into the club, and the MCP is the bouncer that lets them in to communicate with the connected systems. That agent can be 

For a deeper introduction to how agents work, explore Aztek's guide to demystifying AI agents.

How Do I Implement MCP?

Unlike a custom AI agent or a RAG system, you don’t need to build an entirely new system for MCP. Instead, implementing MCP is more like creating a software integration for your existing AI tools.

How complex that integration is really depends on what you're trying to accomplish, which systems need to connect, and what those systems allow. While there isn’t an exact answer for how long this process can take, there are some standard steps that you’ll need to take to implement MCP.

1. Identify a Business Problem

Pursuing AI technology should never start with the goal of adding more tools. Before pursuing anything, identify what challenges you face and determine what you want to improve.

• Are employees spending too much time gathering information from different platforms? 
• Does your marketing team manually compile the same reports every month? 
• Are routine data-entry tasks creating unnecessary work?
• Do you struggle to take advantage of all the information and knowledge you have stored in different systems?

Start with the process so that you can better understand which technology is the right fit. This approach gives your team an opportunity to evaluate whether the integration delivers meaningful value before investing in a shiny new toy that doesn’t really solve any problems.

2. Evaluate Your Existing Systems

If MCP seems to be the right choice for your business problem, it’s time to break down where any relevant information lives and how it can be accessed.

Some MCP integrations are more complicated than others. If a CRM or some other business applications provide an application programming interface (API), creating an MCP integration for that functionality can be more straightforward. Older software, custom-built systems, limited APIs, or workflows involving several platforms may require more development.

Regardless of your system, you’ll need to examine whether the necessary information is accessible, and if that system supports the actions you want the AI application to perform. The answers to those questions will help guide what needs to be done in the next stage.

3. Develop and Configure the MCP Integration

Once business needs and technical requirements are understood, the next step is to make the integration work.

This process may involve using an existing MCP server or building one that connects to your business software. The integration defines which tools are available and how requests are handled.

For example, a CRM integration might expose tools that allow an AI application to retrieve customer information or create a new contact. A reporting integration might provide access to campaign metrics or downloadable reports. Not every integration needs to support actions that modify data. Some may only need permission to retrieve information.

4. Establish Security and Access Controls

As you may expect, giving an AI tool access to business systems introduces important security considerations.

Just because an AI application can connect to a system doesn't mean it should have access to everything within that system. MCP integrations should be designed around appropriate authentication, authorization, and access permissions. Depending on the application and integration, authentication methods may include established standards such as OAuth.

Businesses should also consider which users can access an integration, what information the integration can retrieve, and which actions it can perform. For example, an AI application that retrieves marketing performance data may only need read-only access. An integration that creates customer records or initiates transactions requires additional controls. You can also consider mandating explicit human approval for any major actions with sensitive information.

5. Test, Launch, and Maintain the Integration

Before any tool is introduced into a live business workflow, it should be tested to make sure it behaves as expected. Testing an MCP integration includes verifying that it retrieves the right information, performs only authorized actions, and handles errors appropriately. This step is especially important for integrations that can adjust records or initiate transactions.

Maintenance is another consideration. Like other software integrations, MCP connections will require updates when authentication credentials change, external APIs are modified, or business requirements evolve.

Knowing that something requires maintenance is very different than actually maintaining it. Before the MCP integration launches, businesses should clearly establish who will own maintenance going forward.

How Much Does an MCP Integration Cost?

The answer is everyone’s least favorite response when it comes to costs: it depends. There is no universal price for implanting MCP because the cost depends on the complexity of the integration, the software involved, the tools needed, and the level of customization.

While we can’t put a hard number on it, there are several factors that can influence your investment:

• Existing software capabilities: Systems with accessible, well-documented APIs may be easier to integrate.
• Number of connected systems: Connecting one platform is generally simpler than coordinating several.
• Required functionality: Retrieving information may be less involved than building workflows that modify records or initiate transactions.
• Security requirements: The complexity of authentication, access controls, and approval processes can create more work.
• Testing and maintenance: Integrations must be validated and maintained as the systems change.

Is Your Business Ready for MCP?

You don't need to have a comprehensive AI strategy or an advanced technical team to begin exploring MCP. However, any form of technology is only useful when you have a clear business need and the right systems in place to support it.

Before moving forward, ask yourself a few questions.

Are You Already Using AI in Your Business?

MCP is most relevant when you want AI applications to interact with external tools or business information.
If your team is very new to AI or only uses it for basic tasks, an MCP integration may not be an immediate priority. However, it can be a good next step if you're regularly wondering how AI can help you export data, update another system, or assist with other work.

Do You Have a Repeatable Process That Could Benefit from a Connection?

The strongest opportunities are often tasks your team performs frequently and consistently. Look for processes that require employees to retrieve information, move data between systems, or complete clearly defined actions. 

If you’re struggling to come up with use cases, it may be time to consult with a company that specializes in marketing and web development to see if there are opportunities instead of chasing the latest AI acronym.

Can Your Existing Systems Support an MCP Integration?

MCP doesn't eliminate the technical limitations of your existing software. If a system doesn't offer a practical way to access the necessary information or perform an action, connecting it may require more work. An assessment of your current software and available APIs can help you learn which opportunities are realistic.

Are You Ready to Address Security and Ownership?

Don’t wait until too late to find out you have security concerns or nobody who can properly own your new MCP integration. Before connecting AI applications to important business systems, determine who should have access and what they're allowed to do before they become obstacles during launch.

MCP Starts with the Right Strategy

AI has so much potential to solve serious business problems, but only if you have the right tools in place for the job.

At Aztek, we believe technology decisions should start with strategy. We want to understand your goals, evaluate your existing systems, and find practical opportunities before recommending a solution. That process allows you to shape your solutions around your specific challenges instead of collecting different tools that take up your time and never provide any value.

Interested in exploring how MCP or other AI integrations could support your business? Reach out to Aztek so that we can discuss your goals, current technology, and opportunities to make your workflows more efficient.