Artificial intelligence is transforming the way companies work, decide and compete. However, as AI models become more powerful, an increasingly evident engineering problem arises: how can they be connected securely and efficiently to real business data?
For years, the answer was to build complex bespoke integrations for every combination of model and system. If a company had five AI models and ten internal tools, it potentially needed fifty different integrations, each with its own logic, authentication and maintenance. Engineers call this the N×M problem: N models by M tools, with complexity that grows exponentially and creates security problems, high operating costs and sluggish processes.
Today, that reality is changing. The Model Context Protocol (MCP), published by Anthropic on 25 November 2024 (official documentation at modelcontextprotocol.io), is the open, universal standard that is redefining how AI models access databases, APIs and business systems. It turns that N×M problem into N+M: each tool is defined once as an MCP server and any compatible model can use it without additional development.
For many industry analysts, this represents a paradigm shift comparable to what HTTP meant for the web or ODBC for databases. The most widespread analogy in the technical ecosystem is USB-C: before that standard, each manufacturer used its own connector; with MCP, integration between an AI model and an external system always follows the same pattern.
How does MCP work?
MCP defines an architecture of three components that work in coordination. The MCP host is the AI application that receives the user's request (for example, a corporate assistant or a customer service agent). The MCP client is the component that manages communication between that host and the external systems. The MCP server is the program connected to each tool or data source (an ERP, a CRM, a database, a document repository) that exposes its capabilities in a standardised way.
When the model needs information or wants to carry out an action, the host queries the corresponding server through the client, receives the data in real time and incorporates it into its answer. Everything follows the same protocol, regardless of the model or the system connected.
What makes MCP different?
The key to MCP is that it acts as a standard bridge between AI and data. Instead of forcing companies to replicate their information in external repositories or to write specific integrations for each model, MCP allows the model to query their systems directly at the right moment, with full guarantees of security and control.
Its main advantages are as follows:
Real-time access. Models no longer work with frozen data or depend on indexes that become outdated within hours. With MCP, the AI directly queries the information it needs, which translates into more accurate, up-to-date and relevant decisions. For example, a customer service assistant can check stock availability in the ERP in real time, instead of relying on data from a week ago.
Greater security and control. One of the biggest concerns in the use of AI is where the data resides. MCP solves this at the root: information never leaves the organisation's perimeter. The company decides what is shared, under what conditions and at what level of granularity. This not only protects the organisation, but also makes it easier to comply with the EU AI Act (in force since August 2026) and with sector regulations in finance, healthcare and public administration.
Plug-and-play modularity. MCP turns integration into a simple, standardised process. Connecting a CRM, an ERP or a database no longer means months of bespoke development. It is enough to implement the protocol once, in a similar way to how applications are connected today through a REST API, but with an additional layer that allows capabilities to be discovered dynamically at runtime.
A note on MCP and RAG. A common question is whether MCP replaces Retrieval-Augmented Generation (RAG). The answer is no: MCP and RAG are complementary and solve different problems. RAG improves the model's answers by retrieving relevant information from a knowledge base before generating text. MCP standardises how the agent accesses external tools and systems to carry out actions or retrieve data in real time. In advanced enterprise architectures both are used at the same time (Google Cloud, What is Model Context Protocol).
A standard the whole industry has already adopted
MCP was born as an Anthropic project, but its adoption has been extraordinarily fast. In March 2025, OpenAI announced full support in its Agents SDK. In April 2025, Google DeepMind confirmed native support in the Gemini models. Microsoft integrated it into Copilot Studio in July 2025. In December 2025, Anthropic donated the governance of the protocol to the Agentic AI Foundation, an initiative under the Linux Foundation co-founded by Anthropic, Block and OpenAI, with the backing of Google, Microsoft, AWS and Cloudflare. By early 2026, the community had published more than 10,000 public MCP servers and the protocol was recording more than 97 million monthly SDK downloads, according to Anthropic data (Anthropic Engineering Blog).
This means that MCP is no longer Anthropic's protocol, but the standard of the entire AI industry. An MCP server that a company implements today for its ERP will work with Claude, ChatGPT, Gemini and any future tool compatible with the standard, without the need to rewrite the integration.
Impact on the business ecosystem
MCP's potential is not limited to the technical layer: it is redefining the business landscape on three key fronts.
For companies, it means being able to harness the full value of their data without sacrificing security or increasing costs, driving more sustainable and auditable AI projects.
For developers, MCP offers a common, open language that avoids reinventing the wheel with every integration, which accelerates innovation and protects investments against a change of model provider.
For the technology ecosystem, it opens the door to more interoperable, universal and scalable AI, where different models and providers can work on the same foundations without integration friction.
In practice, sectors such as healthcare, banking, logistics and industry can deploy AI faster, more cheaply and in a more regulated way, something traditional integration architectures do not allow at that cost and speed.
MCP and trust in AI
One of the great current debates around artificial intelligence is trust. Can AI handle sensitive data with guarantees? Does it comply with data protection regulations? Is it transparent about how it accesses information?
MCP presents itself as part of the answer because it puts companies at the centre of control: data is not handed over, it is managed on the organisation's own terms. This, combined with standardisation and the open governance of the Linux Foundation, creates a new framework in which trust is not built on promises, but on a clear, verifiable protocol that is independent of any provider.
At LurNova we are clear about it
At LurNova we believe that MCP is not just a passing trend, but the future of integration between artificial intelligence and business data. We follow its evolution very closely and work to ensure our solutions integrate it naturally, helping organisations innovate with confidence, reduce infrastructure costs, always keep control of their data and comply with current regulations without holding back innovation.
The future of AI will not be defined solely by more powerful models, but by smarter, more secure and more sustainable connections. MCP is the missing piece of this puzzle, and at LurNova we want to help you take advantage of it starting today.
Co-founder and CTO of LurNova, with more than 20 years' experience in developing digital technologies. Specialised in designing secure, private and governed artificial intelligence architectures and solutions for organisations.
