Artificial intelligence (AI) promises to redefine workflow efficiency, and it’s taking the business world by storm. It should come as no surprise that integrating private AI—AI that works and learns exclusively from internal company data—is a high-priority item for organizations around the world.
The obvious challenge lies in making private AI both safe and usable across the company, allowing it to connect and communicate across distributed data environments that vary by department, region, and sector. At the heart of that challenge is communication: your systems need to talk to each other to stay in sync and aligned.
This is where communication protocols come in: they define how different parts of your AI ecosystem talk to each other, set boundaries for scope, and help keep queries focused and efficient. Among these, Model Context Protocol (MCP) stands out as a candidate for a universal standard, offering your organization a consistent way to connect AI models with data, tools, and people across your entire environment.
Let’s explore why communication protocols are critical to enterprise AI and how MCP can serve as the connective tissue that helps organizations scale AI reliably, securely, and efficiently over time.
Communication Protocols: Building AI That Understands Business Context
At a basic level, communication protocols govern how systems exchange information.
APIs enable the physical transfer of data between systems. But in AI-driven environments, simple data exchange isn’t enough. AI needs to understand the data and act on it within a specific context.
Communication protocols build on top of APIs by adding structure, intent, and meaning. They define what to say, when to say it, and how to interpret what’s said. In an enterprise AI setting, this means guiding how AI models interact with your data, tools, and users.
Without clear protocols, AI tools can lose sight of or completely fail to understand the context of a request, misinterpret intent, or pull incorrect or incomplete data. These guardrails are crucial in enterprise environments where data lives all over the place: on site, in the cloud, across departments—or even continents—and decisions need to be made quickly and accurately.
Communication protocols give AI a shared language and a clear framework so it can identify the right data, understand its relevance, and take the right action within your business context.
What is MCP? The Universal Adapter
MCP stands for Model Context Protocol. It’s a specific communication protocol designed as a “universal adapter” that allows easier integration between tools and systems.
Most organizations work with a variety of systems—databases, tools, cloud platforms—all built at different times by different vendors. MCP was created as a flexible, extensible architecture to make it easy to integrate systems and get them to talk to each other.
MCP structures inputs so the AI knows what tools to use, why to use them, and how to frame the query. It also imposes guardrails to ensure the AI accesses only the relevant and requested data.
In an enterprise setting, this structured coordination is key, which we’ll explain in the “Examples and Use Cases: Why MCP?” section with a few examples imagining MCP used in a private enterprise LLM setting.
4 Reasons MCP is Cloudera’s Choice for a Universal Communications Protocol
There are several communication protocols that could, in theory, replicate some of what MCP offers. So why does Cloudera advocate for MCP? The simple answer is contextual orchestration and tool calling.
MCP provides the structure and context AI models need to access the right tools at the right time reliably, securely, and in alignment with business logic.
Here are four ways we’ve seen MCP positively impact enterprise AI environments:
1. Keeps AI Models and Humans on Task
One of the most challenging parts of building an enterprise AI system is making sure that the AI stays on task. You don’t want outputs that are just technically correct. You want outputs that are relevant to your specific operations.
MCP acts as a contract between the user, the model, and the business logic. It structures communication with clear intent, scope, and constraints, keeping your private AI aligned with your goals so it doesn’t drift into irrelevant or overly generic responses.
MCP also defines how your team works with AI. When using MCP as a standardized protocol, it’s easier to build user-friendly interfaces where your various departments and teams can ask specific questions or make decisions through AI agents without needing to understand how everything works behind the scenes, as the examples later in this article illustrate.
2. Improves LLM Context Management
Think of MCP like a USB-C plug: a standard and universal connector that works on different devices, even if they have different manufacturers. Because large language models ( LLMs) are stateless (in other words, vendor agnostic), MCP takes on a similar role.
Enterprises looking to feed their proprietary or operational data into LLMs need standardization like this because it creates the link that connects an LLM and the chosen internal data storage location (like a local or cloud server).
It acts as a consistent layer that injects the right context—like user roles, prior interactions, or task-specific data—into each interaction regardless of source. This prevents fragmentation, ensures the model responds appropriately across use cases, and helps maintain reliable performance.
3. Supports a Modular, Scalable Infrastructure
MCP offers a universal adapter that works across environments, allowing on-premises, hybrid cloud, and globally distributed systems to work together. This is critical for enterprises that don’t have a single, unified tech stack today as well as for those planning to adopt new technologies over time.
MCP lets you scale AI adoption across teams, regions, and tools without rebuilding your infrastructure or reinventing the wheel with every change. It ensures interoperability between systems and provides a consistent framework that can also support governance, security, and compliance needs as AI scales.
4. Future-Proofs Your AI Strategy
Because MCP is vendor- and platform-agnostic, it offers flexibility as your tech stack evolves. You can update, swap out, or add new tools without rewriting how everything communicates. Its extensibility also supports custom additions and integrations that go beyond just LLMs, helping you connect AI capabilities across your broader enterprise.
Put simply, this means the investments you make today won’t become immediately outdated the minute something new comes along.
Examples and Use Cases: Why MCP?
To better cement this concept, we’re going to outline a few examples that demonstrate how MCP can positively impact workflows across teams and departments.
First, imagine a director of marketing needs a report that shows all EMEA channel metrics to decide on next quarter’s strategy. They input the following into their enterprise search model:
Prompt: "Summarize last month’s campaign performance by channel for the EMEA region."
Tool called: Marketing analytics platforms
How MCP helps:
Defines the reporting period and scope (by channel and region)
Normalizes data from multiple sources
Ensures consistent metrics (click-through rates, conversions, etc.)
Produces a user-friendly output that can immediately guide next steps
Next, imagine an IT director auditing high-level employee user accounts to ensure any loose ends are closed before the end of the month.
Prompt: "List all user accounts with admin access that haven't logged in for 30 days."
Tool called: Internal identity and access management (IAM) framework
How MCP helps:
Applies security constraints (admin-level query)
Specifies filtering logic (last login > 30 days ago)
Enforces role-based access control so only authorized staff can see results
Produces a user-friendly output that allows immediate action to be taken with confidence
Now, picture an inventory manager with warehouses distributed around the world who needs to know how much inventory of a certain item they have in stock and where it’s located.
Prompt: "What’s the current inventory level of “Item X” across our regional warehouses?"
Tool called: Inventory management system
How MCP helps:
Clarifies entity (“Item X”), timeframe (“current”), and locations (“regional warehouses”)
Prevents irrelevant or overly broad queries
Connects to the right real-time tool or dataset securely
Produces an easy-to-read report that satisfies the query in seconds
In each use case, the AI tool used MCP for guidance, ensuring the query responses were relevant, clear, and fulfilled with correctly sourced data.
Keep Reading: MCP and Cloudera
Now that you understand what MCP is, how it works, and what it offers your organization, it’s time to dig a little deeper. Head to our next blog, Bringing Context to GenAI with Cloudera MCP Servers to learn in a bit more detail how MCP and Cloudera work together.
