For operational use, LLMs must be fed enterprise data in real time. Combining an AI data layer with the Model Context Protocol (MCP) is their meal ticket..
In the era of generative AI (GenAI) apps powered by Large Language Models (LLMs), the combination of an AI data layer and the Model Context Protocol (MCP) is quickly becoming one of the most important generative AI (GenAI) assets out there.
While powerful LLMs – like Gemini, GPT-4o, or Claude – can reason, write, and converse, they’re basically floating in the clouds and frozen in time (on the date their training ended).
The only way to ground them and educate them (about your customers, for example) is to integrate fresh enterprise data into their prompts.
This article discusses the value of an AI data layer coupled with MCP is the ideal solution for enabling contextual, grounded, and real-time operational workloads, such as generative AI use cases in customer service.
The Model Context Protocol (MCP) is the runtime mechanism that:
Understands user intent
Identifies what context the model needs
Retrieves and assembles that context dynamically
Injects it into the prompt sent to the LLM
At its core, the MCP server behaves like a context orchestrator composing the final prompt based on:
User request
Data retrieved from enterprise systems
Prior interaction history
Policies like token budgets, access control, or data masking
LLM prompt engineering is a highly complex task, but ultimately enabling:
Real-time responses that are personalized and accurate
System grounding and hallucination prevention
Zero retraining of models for domain-specific behavior
But before you can prompt at scale you first need to address 4 key challenges:
Knowing what context is needed
Deciding where to get it
Retrieving and harmonizing fragmented data
Injecting it efficiently, safely, and cleanly
…and all this at the speed of conversational AI.
In most enterprises, the data needed to fulfill a user query is scattered across dozens of systems with no unified API, schema, or key resolution strategy.
Instead of the MCP server trying to federate across those systems, it makes a single call to the AI data layer, like:
GET /data-products/customer?id=ABC123
AI data layer based on business entities
When the AI data layer is based on business entities (such as customers, loans, orders, or devices) :
Every field in the data product is mapped to the appropriate underlying system.
Transformation logic is applied (status normalization, date formats).
All the relevant data for each instance (a single customer, for example) is secured, managed, and stored in its own Micro-Database™.
Real-time access is supported via API, and updates can be continuous or on-demand.
End-to-end workflow
Here’s how the full stack works:
[User query] > [MCP server] > [AI data layer] > [MCP prompt] > [LLM] > [Response]
MCP server limitations
A common misconception is that the MCP server uses an LLM to construct prompts. It doesn’t – and shouldn’t . AI prompt engineering is handled by deterministic logic, not generative models.
By combining the runtime intelligence of MCP with the real-time data access capabilities of a K2view AI data layer, you benefit from:
Entity-centric APIs
Real-time context
Prompt optimization
No need for retraining
Governance
Scalability
By combining the model context protocol (to dynamically determine what data is needed) with K2view Data Product Platform (to retrieve and augment enterprise data wherever it resides), you unlock a pattern that grounds LLMs in operational truth without retraining or system rewiring.
This combination results in an excellent AI customer experience especially when supplemented with auxiliary GenAI frameworks like Retrieval-Augmented Generation (RAG) and Table-Augmented Generation (TAG).
In summary, it's not enough to have a powerful LLM. You also need an architecture that feeds it the right context, in real time, reliably. The ability to determine context (via MCP) and then fetch the appropriate data (via K2view) is the basis of intelligent, composable, real-time AI services in large organizations.
Discover why K2view GenAI Data Fusion
is the ideal entity-based AI data layer for MCP.