What Is the Model Context Protocol (MCP)? How Will it Enable the Future of Agentic AI?

Traditional AI inference is based on static large language models (LLMs) that can’t see past their training cutoff dates. If something isn’t in the training dataset, then as far as the model’s concerned, it simply doesn’t exist.

In contrast, AI agents can pull real-time data from multiple external sources. These agents actively seek out new information in response to environmental changes, without the need for human intervention.

Agentic AI opens a wide range of new applications across industries, from real-time patient monitoring in healthcare to high-frequency trading in financial markets. In these use cases, AI agents can connect to data sources to get up-to-date information and then make informed decisions about the next best action to take.

While AI agents can achieve much more than static LLMs, infrastructure and ecosystem complexity could prevent them from reaching their full potential. At a time when multi-model AI hosted on hybrid multicloud infrastructure has become the norm, agents may struggle to connect to data and tools that are distributed across cloud and on-premises environments. This is the problem that the Model Context Protocol (MCP) is intended to solve.

With the help of MCP servers, AI agents can easily pull the latest insights from on-premises databases, access new tools hosted in different clouds, connect with other distributed agents to collaborate on common objectives, and much more. Thus, applying MCP technology can help take agentic AI to the next level.

MCP helps address AI complexity

In years past, the dawn of cloud computing changed the way enterprises access digital infrastructure. Starting with traditional on-premises environments, they transitioned to single cloud, then hybrid cloud, and then finally hybrid multicloud. We’re seeing a similar evolution with AI today. AI ecosystems are proliferating, and enterprises are turning to a wide variety of data, model and infrastructure providers to enable their AI strategies.

Enterprises recognized the need to enable connectivity across distributed AI environments, and some even started writing custom code as a workaround. But this wasn’t a scalable approach, nor was it fully reliable. There needed to be a simple, repeatable way of connecting AI models and assets, and that’s exactly what MCP delivers.

Developed by Anthropic and released as an open-source framework in November 2024, MCP is a standardized approach for providing context to AI models. As the MCP user guide puts it, MCP is doing for AI models what the USB-C standard cable did for devices.[1] Just like USB-C makes it easier to connect any device to any peripheral, MCP makes it easier to connect any AI model to any data source or tool—regardless of where they’re hosted. This includes enabling AI agents to access new or updated tools without having to be reprogrammed.

This begs the question: What are the implications of MCP for enterprises as they mature in their AI strategies? With MCP, enterprises have the opportunity to achieve much more than they could with LLMs alone, and this has to figure into their decision-making. They need to consider when they should progress from:

• Teaching the model what they know (fine-tuning)

to:

• Letting the model find what it needs (retrieval-augmented generation, or RAG)

to:

• Letting the model act on what it finds in real time (agentic AI with MCP)

The tech industry has responded very positively to the release of MCP, showing that there was real demand for an open, standardized AI framework. The MCP ecosystem is growing rapidly, and there are already thousands of servers available today. Big names like Google and OpenAI have adopted the technology, making it clear that there’s much more to MCP than just hype. Dhanji R. Prasana, Chief Technology Officer at Block, put it best:

Choosing the right infrastructure for MCP

MCP is intended to enable a flexible, distributed approach to enterprise AI, and it’s very good at doing that. However, picking the right infrastructure environment is essential to making the most of the MCP opportunity. There are several infrastructure options that will inevitably lead to subpar results:

• You could use a cloud-only model, but this results in vendor lock-in, negating the benefits of using MCP in the first place. You’d only be able to choose the models and tools that reside on that particular cloud.
• You could use a combination of public and private infrastructure connected via the internet, but this will lead to performance issues and data privacy risks.
• You could build a fully private environment for AI. This would meet your data privacy requirements, but it would also cut you off from different tools and partners.

To enable the future of agentic AI, you need infrastructure that’s just as flexible and vendor-neutral as MCP itself. This is what you can get by deploying at Equinix. In addition to our colocation data centers available in 76 strategic markets worldwide, you’ll also be able to easily connect with our ecosystem of AI partners, including hardware providers like NVIDIA and Groq.

Also, Equinix Fabric® provides flexible, scalable networking capabilities to connect different environments and optimize your distributed infrastructure for AI. You’ll get the secure, low-latency connectivity you need to link your AI agents to data, models, tools and other agents.

With Equinix Fabric as the backbone of your AI ecosystem, you can access MCP servers in different environments, thus maximizing the effectiveness of your AI agents. For instance, you can host agents on private infrastructure in an Equinix colocation data center when you need to meet data sovereignty and residency requirements. At the same time, you can set up direct, low-latency virtual connections to hundreds of clouds and other service providers from wherever you’re located. This ensures that no data source or tool is ever out of reach.

Applying the power of MCP at Equinix

In addition to helping our customers get the infrastructure they need to capitalize on MCP, Equinix is integrating MCP into our own solutions. For instance, the Equinix Fabric API offers a variety of tools that make it easier for customers to understand how their virtual connections are performing and adjust them on the fly. We’re using MCP to give our customers on-demand access to these capabilities via a simple chat interface.

The diagram below shows what’s possible when you pair MCP servers with Equinix Fabric connectivity. In this example, a user chats with an AI agent hosted in the cloud. The agent then pulls data from a vector database in an Equinix colocation data center and accesses Equinix Fabric API tools through an MCP server hosted in a different cloud.

The agent can instantly find the data it needs to answer the user’s questions or the tools it needs to perform their requested actions. The user doesn’t need to know the details of what’s happening behind the curtain, and can instead focus on outcomes.

To learn more about how AI-ready data centers are enabling hybrid multicloud integration and other important aspects of future-proof AI infrastructure, read the ESG report Architecting a Data Center Optimized for the AI Era.[3]

To see MCP in action for yourself, request a free proof of concept in an Equinix Solution Validation Center near you.

[1] Introduction: Get started with the Model Context Protocol (MCP).

[2] Introducing the Model Context Protocol, Anthropic, November 25, 2024.

[3] Scott Sinclair and Moyna Keane, Architecting a Data Center Optimized for the AI Era, Enterprise Strategy Group, now part of Omdia, May 2025.