The "Why Doesn't Our AI Understand Us?" Problem

Artificial intelligence (AI) and large language models (LLMs) are everywhere. They work wonders, write texts, and answer questions. But when it comes to performing a task specific to your company, that brilliant AI can suddenly turn into a forgetful intern. "Which customer are you talking about?", "Which system does this order number belong to?", "How am I supposed to know this email is urgent?"

If you've tried to leverage the potential of AI only to hit this wall of "context blindness," you're not alone. No matter how smart an AI is on its own, it's like a blind giant without the right information and context.

In this article, we're putting the magic formula on the table that gives that blind giant its sight, transforming AI from a generic chatbot into an expert that understands your business: MCPs (Model Context Protocol). Our goal is to explain what MCP is, how it makes AI 10 times smarter, and how we at Dot use this protocol to revolutionize business processes.

What is an MCP? The AI's "Mise en Place”

MCP stands for "Model Context Protocol." In the simplest terms, it's a standardized method for providing an AI model with all the relevant information (the context) it needs to perform a specific task correctly and effectively.

Still sound a bit technical? Then let's imagine a master chef's kitchen. What does a great chef (our AI model) do before cooking a fantastic meal? Mise en place! They prepare all the ingredients (vegetables, meats, sauces), cutting and measuring them perfectly, and arranging them on the counter. When they start cooking, everything is within reach. They don't burn the steak while searching for the onion.

MCP is the AI's mise en place. When we ask an AI model to do a task, we don't just say, "Answer this customer email." With MCP, we provide an organized "counter" that includes:

• Model: The AI that will perform the task, our chef.
• Context: All the necessary ingredients for the task. Who the customer is, their past orders, the details of their complaint, notes from the CRM...
• Protocol: The standardized way this information is presented so the AI can understand it. In other words, the recipe.

Giving a task to an AI without MCP is like blindfolding the chef and sending them into the pantry to find ingredients. The result? A meal that's probably inedible.

An MCP is a much more advanced and structured version of a "prompt." Instead of a single-sentence command, it's a rich data package containing information gathered from various sources (CRM, ERP, databases, etc.) that feeds the model's reasoning capacity.

Use Cases and Benefits: Context is Everything!

Let's see the power of MCP with a simple yet effective scenario. Imagine you receive a generic email from a customer that says, "I have a problem with my order."

• The World Without MCP (Context Blindness):The AI doesn't know who sent the email or which order they're referring to. The best response it can give is, "Could you please provide your order number so I can assist you?" This creates an extra step for the customer and slows down the resolution process.
• The World With MCP (Context Richness):The moment the email arrives, the system automatically creates an MCP package:
  
  • Identity Detection: It identifies the customer from their email address (via the CRM system).
  • Data Collection: It instantly pulls the customer's most recent order number (from the e-commerce platform) and its shipping status (from the logistics provider).
  • Feeding the AI: It presents this rich context package ("Customer: John Smith, Last Order: 12345, Status: Shipped") to the AI model.

Now fully equipped, the AI can generate a response like this: "Hello, John. We received your message regarding order #12345. Our records show your order has been shipped. If your issue is about something else, please provide us with more details."

Even this single example clearly shows the difference: MCP moves AI from guesswork to being a knowledgeable expert. This means faster resolutions, happier customers, and more efficient operations.

MCPs in the Dot World: The Context Production Factory

The MCP concept is fantastic, but who will gather this "context," from where, and how? This is where the DOT platform takes the stage.

We designed DOT to be a giant "MCP Production Factory." Our platform features over 2,500 ready-to-use MCP servers (or "context collectors") that can gather bits of context from different systems. These servers are like specialized workers who can fetch a customer record from Salesforce, a stock status from SAP, or a document from Google Drive on your behalf.

The process is incredibly simple:

• You select the application you want to get context from (e.g., Jira).
• You authenticate securely through the platform.
• That's it! The server now acts as a "Jira context collector" for you.

When you build a complex workflow in our Playground, the system orchestrates these context collectors like a symphony. When a workflow is triggered, the Dot orchestrator sends instructions to various servers, assembles the MCP package in real-time, and gets it ready for the task.

What Makes Us Different? Intelligent Orchestration with Dot and MCPs

There are many automation tools on the market. However, most are simple triggers that lack context and operate on a basic "if this, then that" logic. Dot's MCP-based approach changes the game entirely.

• From Automation to Autonomous Processes: We don't just connect applications; we feed the AI's brain with live data from these applications. This allows you to build agentic processes that go beyond simple automation. An Agent knows what context it needs to complete a task, requests that context from the relevant MCP servers, analyzes the situation, and takes the most appropriate action.
• Advanced Problem-Solving and Validation: When a problem occurs (e.g., a server error), the system doesn't just shout, "There's an error!" It creates an MCP: which server, what's the error code, what was the last successful operation, what do the system logs say? An AI Agent fed with this MCP can diagnose the root cause of the problem and even take action on external applications to resolve it (like restarting a server). This dramatically increases the accuracy (validation) of actions by leveraging the AI's reasoning ability.
• Real World Interaction: Even the most complex workflows you design in the Playground don't remain abstract plans. MCPs enable these workflows to interact with real-world applications (Salesforce, Slack, SAP, etc.), read data from them, and write data to them. In short, they extend the AI's intelligence to every corner of the digital world.

Let's Wrap It Up: Context is King, Protocol is the Kingdom

In summary, the Model Context Protocol (MCP) is the fundamental building block that transforms artificial intelligence from a general-purpose tool into a specialist that knows your business inside and out.

The Dot platform is the factory designed to produce, assemble, and bring these building blocks to life. When our 2,500+ context collectors are combined with the reasoning power of LLMs and the autonomous capabilities of Agents, the result isn't just an automation tool, it’s a Business Orchestration Platform that forms your company's digital nervous system.

You no longer have to beg your AI to "understand me!" Just give it the right MCP, sit back, and watch your business run intelligently and autonomously.

So, what's the first business process you would teach your AI? What contexts would make its job easier?

It all starts small but with the right context, your AI can grow into a teammate you actually trust!

Frequently Asked Questions

How is an MCP different from a regular prompt?
A prompt tells the AI what to do. An MCP gives it the full story, so it can actually do it well.

Do I need to be technical to use MCPs in Dot?
Not at all. You just connect your tools, and Dot takes care of the context in the background.

What kinds of tasks work best with MCPs?
Anything that needs more than a guess like customer replies, reports, or solving real issues. That’s where MCP really shines.