Imagine a call center agent asking an AI assistant, “Why is there a discrepancy in this customer’s latest invoice?” and getting a clear answer instantly.
Answering an operational question – accurately, in real time, and grounded in trusted enterprise data – is no trivial task. Because that basic question-answer process poses many different challenges, like:
Parsing the natural language prompt
Understanding the business context (a customer query)
Identifying which systems contain the relevant data (CRM, finance, support)
Querying those systems in real time
Harmonizing the results into a clean, coherent view
Injecting that context into a prompt that a Large Language Model (LLM) can understand
This 6-step orchestration layer is increasingly referred to as MCP AI, a foundational pattern for enterprises implementing AI.
The MCP client-server approach is essentially the missing link that makes LLMs useful in real-world business environments. But, MCP must work in tandem with a data layer, often enriched by generative AI (GenAI) frameworks – like Retrieval-Augmented Generation (RAG) or Table-Augmented Generation (TAG) – which fetch fresh enterprise data to enable more informed LLM responses to user queries.
To understand how MCP works, let’s have a look at the full AI context stack behind LLM-powered interactions:
In the above diagram, steps 2 and 4 – intent recognition and task planning - and - prompt construction and injection – are handled by an MCP server, while step 3 – context retrieval and harmonization – is handled by the data layer. For example:
A user asks a question.
The MCP server interprets it.
Context is retrieved from enterprise systems.
The data is transformed into a usable prompt.
The LLM responds with an answer grounded in trusted enterprise data.
Now, let’s walk through the process step by step.
The process starts when a user enters a prompt, such as: Why is ACME Corp at risk of churn? This input could come from a chatbot, agent console, app interface, or backend API.
The MCP server kicks in here to:
– Determine user intent (diagnose, summarize, query)
– Identify the target business entity (customer, device, order)
– Extract relevant parameters (customer ID = ACME123)
The next step is pulling context from the enterprise’s underlying systems – often all at once. In our example, relevant systems might include:
– Salesforce: for account ownership and notes
– ServiceNow: for open support cases
– Amdocs: for product subscriptions and usage data
But this data is fragmented, with different schemas, IDs, and formats for each system. The data layer must route sub-queries, join the results, and normalize them into a single, coherent Customer entity.
Once context is retrieved, the MCP server assembles the final prompt using a task-specific template. For example:
Customer: ACME Corp
Account owner: Jane Doe
Open tickets:
– Login error, opened 7 days ago
– Billing discrepancy, opened 3 days ago
This step involves prioritizing what information to include, trimming based on token limits, and structuring the prompt for the LLM.
Finally, the constructed prompt is sent to the model. The LLM processes the input and returns a grounded, personalized, and actionable response.