Problem: When the Model Context Protocol (MCP) is designed for a single platform (SAP, Salesforce, SQL Server), it loses business focus. Solution: K2view.
The problem with app-centric MCPs
As generative AI (GenAI) applications become increasingly central to the digital enterprise, a new challenge has emerged: How to give these intelligent agents real-time access to the right enterprise data – securely, at scale, and loaded with business context.
This challenge is exactly what MCP AI is designed to address.
MCP AI provides an open, standardized protocol that enables LLMs and AI agents to access up-to-date, well-governed enterprise data, on demand, while maintaining strict privacy controls.
It’s a powerful concept with a serious pitfall: Most MCPs are designed around a single application – inheriting that app’s structure, logic, and vocabulary.
Take SAP MCP, Salesforce MCP, MCP SQL Server, or any other core enterprise system. If your MCP is built around one of them, it will reflect that system’s internal view of the world – and not that of your business.
This perspective distorts reality, because customers, products, and business processes don’t live in just one application but span dozens. And if your MCP is blind to that broader context, it risks delivering a narrow and fragmented point of view. To truly unlock the power of GenAI, MCP must operate at the level of the business, not the application.
A shift in perspective is needed
Most MCP AI implementations today are app-centric by design. They’re built to serve a specific system and, as such, they speak the language of that system in terms of its data structures, field names, logic, and operational assumptions.
For example, an SAP-based MCP might expose customer data using SAP’s internal terminology, with field names like KUNNR, VKORG, BUKRS, KNVV, and VTWEG.
| Field name | Derivation | Meaning |
| KUNNR | Kunde Nummer | Customer number |
| VKORG | Verkaufsorganisation | Sales organization |
| BUKRS | Buchungskreis | Company code |
| KNVV | Kunde Nummer Vertrieb | Table for master sales data |
| VTWEG | Vertriebsweg | Distribution channel |
Although these field names are standard in SAP's internal schema and highly specific to how SAP structures its data, outside the SAP world they don’t make sense without translation or context.
Now, that’s all well and good if SAP were your only platform. But it’s not. Because your business doesn’t run on SAP alone. Or Salesforce. Or any one application. It runs on a complex web of systems – CRM, billing, support, ERP, data lakes, etc. – each holding a different piece of customer data.
In that reality, an app-centric MCP can’t give GenAI agents, operational teams, or digital apps the complete and consistent view they need. Instead, it delivers a siloed perspective, accurate within its own boundaries but disconnected from the bigger picture.
What’s needed is a shift in perspective: From app-bound MCPs to ones that connect directly to the business entities that matter – like customer, order, or product – regardless of where that data lives.
Closing the customer data gap with business entities
As discussed above, if your MCP AI implementation is app-centric, it can only show you the part of the customer that lives in that single application.
But customer's data doesn’t live in just one system. Its profile might exist in your CRM, invoices in a billing platform, support history in a help-desk tool, orders in an ERP, and digital behavior in a data lake. No single application holds the complete picture and no app-centric MCP can bridge that gap.
That's where the concept of a business entity comes in.
A business entity – such as an individual customer, order, product, or account – is not defined by a single platform, but exists across systems, use cases, and teams. It’s the common denominator that connects the organization.
That’s why the most effective MCP for the enterprise doesn’t organize around applications, but around business entities. These entities provide the right abstraction layer for connecting GenAI agents, digital apps, analytics pipelines, and operational processes to the data they need – regardless of where that data is stored.
Instead of forcing every data consumer to learn the language of each source application, a business entity approach is busy making MCP work with fragmented data – by unifying it under a single, logical, and consistent model. It’s not just cleaner architecture. It’s the only way to enable accurate, real-time, AI-powered experiences across the enterprise.
Enter the entity-based MCP from K2view
Instead of tying the model context protocol to the structure of any one application, K2view MCP data integration connects it to the structure of the business itself through a semantic data layer organized around business entities. This layer unifies all the data for a given entity across systems, delivering a complete, real-time, and governed view that MCP clients can invoke using resources, prompt templates, and tools.
The entity-based approach ensures that your data is exposed along with business context – from prompt to pipeline with MCP. Without it, you wind up with fragmented views, awkward integrations, and AI agents flying blind. With it, you create a foundation that’s scalable, intelligent, and truly aligned with how your business works.
K2view MCP data integration turns fragmented data
into trusted, structured context for your LLMs.
