Making MCP work with fragmented data: Why harmonization is the backbone of LLM context

For the Model Context Protocol (MCP) to be effective, harmonization – the process of unifying data from disparate systems – must be foundational. 

Harmonizing data for LLM intelligence 

Large Language Models (LLMs) have become central to enterprise AI strategies.

But for them to provide useful, grounded responses, they must be given the right context in milliseconds.

Context isn’t found in one tidy place; it’s spread across CRM systems, support platforms, billing engines, and operational logs.  

Data fragmentation introduces complexity. Here’s where the Model Context Protocol (MCP) – a runtime layer designed to construct contextual prompts from enterprise data – can be instrumental in addressing this complexity.  

But for MCP to do its job well, harmonization is crucial. This post examines the role of harmonization and how it turns fragmented inputs into intelligent, LLM-ready context. 

What LLMs need from context 

LLMs may excel at interpreting unstructured text but that doesn’t mean that any old text will do. They rely on patterns and structure to generate coherent, relevant answers. When context is fragmented, inconsistent, or contradictory, LLMs may hallucinate or respond with low confidence. 

What LLMs need is not rigid structure, but clarity and coherence, in the form of: 

• Related facts grouped together

• Consistent terminology across systems

• A unified representation of the entity in question (for example, one customer, not three aliases)

• Temporal ordering of events to allow reasoning over time 

Achieving clarity and coherence doesn’t require perfectly structured schemas but does require context that’s been cleaned, resolved, and organized to tell a consistent story. Understanding the story behind a user’s query (or intent) makes a prompt interpretable and actionable for an LLM. 

The harmonization gap 

Let’s consider an AI customer service example.  

Suppose a business entity – say, a customer – is managed across multiple systems, for instance in:  

• Salesforce (sales data), it’s ACME INC, ID 12345

• ServiceNow (customer service), it’s Acme Corp, Account A-000987

• Zuora (invoices and payments), it’s Acme Ltd., Code 0034-A 

Without harmonization, MCP might inject all of these into a prompt, leading to confusion:

• Customer_ID: 12345   

• Account: A-000987   

• Contact: John D.   

• Notes: Last case opened June 4  

To a language model, this input looks like a noisy, ambiguous clutter of facts. What’s the real name? Is this 1 customer or 3? Without harmonization, the LLM won’t know, and might fill in the gaps with AI hallucinations.

Why harmonization matters for MCP 

Harmonization is more than just a step in data preparation; it's what makes context coherent.  

It aligns disparate data fields to a common schema, resolves conflicting records, and organizes facts around real-world entities.  

For MCP, this means building a structured, trusted view of a customer or order before prompt construction even begins.  

Without it, context becomes confusing clutter. With it, MCP can deliver precise, LLM-ready inputs every time. 

The harmonization pipeline 

Harmonization pipeline from raw sources to unified, LLM-ready context. 

Architecting for real-time harmonization

 To support MCP at runtime, harmonization must be fast, repeatable, and aligned to how the business thinks: 

1. This begins organizing data by business entities like customer or device.

2. Semantic mapping ensures fields from different systems align, such as mapping cust_id to customer_id or decoding statuses.  

3. Join logic is equally important, in the sense that businesses must define which source is authoritative when fields overlap.  

4. All of this needs to happen in real time or via intelligently refreshed caches so that LLMs get accurate, timely insights. 

Example: Account summary in real time 

Imagine a support agent asking, “Can you summarize this customer’s recent interaction?”

Without harmonization, MCP would retrieve disconnected notes, partial histories, and conflicting contact details.

But with a harmonized view, the prompt includes a clean timeline: “ACME Corp, managed by Jane Doe, had two support tickets in May, last contact on May 28, payment failed May 20, and usage dropped 30%.”  

The LLM can respond with a reliable summary because the context is coherent. 

Why traditional APIs don’t cut it 

Many enterprise systems offer APIs that return narrow slices of data. One fetches contact info, another case history, another billing records. They’re task-specific, schema-bound, and unaware of the broader context.  

Building prompts from these slices adds latency and risk. Harmonized data products, by contrast, abstract away the fragmentation. They deliver the full picture – a clean, usable entity that’s ready to inject into a prompt.

K2view delivers MCP-ready harmonized context  

The K2view Data Product Platform addresses the harmonization challenge by design: 

1. Business entities are defined through a semantic entity model enhanced with context-rich metadata. This model helps the MCP server, and ultimately the LLM, make sense of how data maps to business reality.
2. Every entity instance is harmonized across systems and queried as a complete unit.  
3. Fields are semantically aligned, governed, and anonymized if needed.  
4. Data quality is enforced, with real-time entity matching and resolution to ensure accuracy and completeness.
5. Harmonized context is fed directly to the MCP server, making context delivery into the LLM fast, precise, and grounded. 

In conclusion, MCP enables LLMs to answer business questions with speed and precision. But it can’t work with fragmented inputs. Harmonization – powered by K2view – is the step that transforms noisy, mismatched data into clear, structured, and actionable context. 

Discover how K2view GenAI Data Fusion  
powers data harmonization for MCP.