Unlocking the Potential of AI with the Model Context Protocol (MCP)

As artificial intelligence rapidly evolves from experimental applications to core business infrastructure, the ecosystem supporting it must evolve as well. In this transformation, standardisation and interoperability are no longer technical luxuries—they’re strategic imperatives. 

Just as USB-C has become the universal standard for connecting devices, the Model Context Protocol (MCP) is emerging as a foundational layer for enabling AI systems to interact with enterprise data, tools, and processes securely, efficiently, and at scale. 

At ADC, we work with organisations across industries to design and implement next-generation data and AI architectures. In this post, we unpack how MCP is reshaping the AI integration landscape, and what it means for your organisation’s AI strategy. 

Large Language Models (LLMs) like GPT-5 or Claude 4 Sonnet are incredibly powerful, trained on massive amounts of data and capable of producing human-like responses. But their capabilities are not limitless.  

Two core limitations persist:  

1. Knowledge Cut-Off: LLMs can’t access information beyond their training data unless explicitly given real-time context.  

1. Integration Challenges: Most enterprise systems, tools, and proprietary data sources are not natively interoperable with LLMs.  

A common first step to overcoming these limitations is context stuffing – augmenting the prompt with additional information, for example copy-pasting into the chat interface. Beyond this, RAG (Retrieval-Augmented Generation) architectures allow systems to retrieve relevant chunks of context from a larger corpus, and provide that as context in the LLM call.   

Agentic systems go a step further, allowing LLMs to decide on additional context needs through reasoning and tool usage. However, these approaches often prove fragile, hard to scale, and highly bespoke—because developers must write custom integrations for each tool.
Example: Want to query a database? You must ensure every query output matches the database syntax. Switching vendors or tools often means rebuilding integrations from scratch. 

The Model Context Protocol (MCP) is an open‑source standard designed to eliminate these integration headaches. Multiple providers—such as Chroma and Azure—offer open‑source MCP implementations that can plug directly into your agent workflows, making it easy to experiment with different vendors without rewriting tool logic. 

Under the hood, MCP consists of two layers: 

1. Data Layer  

Uses a JSON-RPC-based protocol to specify the structure and semantics of how tool calls, resource requests, and other capabilities are handled between client and server.   

2. Transport Layer  

Specifies how communication occurs:  

• StDIO: Ideal for local or containerised environments. Fast and lightweight—no authentication needed.  

• HTTPS: Designed for networked or production systems, with support for authentication standards such as OAuth.  

With just these two layers defined, you can run your own MCP‑enabled server. 

Example: Suppose your chatbot decides it needs to search the web. It sends a request via the transport layer; formatted according to the data layer specifications—and receives the results. Because the process is standardized, you can benchmark and swap  search providers easily without having to write custom code.

As organisations mature their AI strategies, the focus is shifting from experimentation to operationalisation—deploying AI systems that are explainable, robust, and integrated into business processes. 

MCP directly supports this shift by: 

• Eliminating vendor lock-in: Build LLM applications that work across frameworks. 

• Reducing tool fragmentation: Use a unified interface for integrating external tools and services. 

• Enabling intelligent agents: Orchestrate multi-step reasoning and actions with traceability. 

MCP empowers your organisation to build LLM-native architectures that are modular, secure, and future-proof.

The Model Context Protocol isn’t just another tool—it’s an enabler for the next generation of AI systems. For organisations looking to harness LLMs as enterprise platforms, MCP provides the standards-based foundation needed to integrate, scale, and govern AI intelligently. 

Let’s explore how MCP can drive your AI strategy forward.

At ADC Consulting, we help enterprise teams evaluate, design, and implement robust data and AI strategies that scale. Whether you’re exploring intelligent agents, retrieval-augmented generation (RAG), or LLM orchestration frameworks, MCP is a foundational component worth understanding. 

We offer executive briefings, technical workshops, and solution architecture assessments to help your organisation unlock the full potential of MCP. 

📩 Contact: Matthew Livesey  – Team Lead Engineering & Analytics  to schedule a discovery session. 

Analytics & Engineering Lead