Model Context Protocol, USB-C Moment in AI?

Motivation

AI models are only as effective as the context they are given - the better the context you provide to your AI application, the more powerful and accurate its outputs become. In this article, we will explore the practical implications of this principle and delve into how the Model Context Protocol plays a critical role in enabling more intelligent and context-aware AI solutions.

Introduction

With the rapid advancement and growing adoption of agentic AI applications, there is an increasing need to address how these agents access and interact with data from external systems.

The Model Context Protocol (MCP), developed by Anthropic is an open standard designed to streamline and standardize how applications supply context to large language models (LLMs).

MCP can be thought of as the USB-C port for AI applications. Just as USB-C offers a unified interface for connecting a wide range of devices and peripherals, MCP provides a standardized mechanism for integrating AI models with diverse data sources and tools - enabling seamless, scalable, and consistent context provisioning.

Why MCP?

MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools. Instead of requiring AI systems to rely on custom API integrations, manually structured requests, and authentication per service, MCP provides a unified framework for AI agents to retrieve, process, and act on structured data in a standardized way.

With this advantage, MCP offers

• A growing list of pre-built integrations that your LLM can directly plug into
• The flexibility to switch between LLM providers and vendors

General architecture

MCP is built on a client-server model that structures how AI models retrieve and interact with external data sources.

• MCP clients are AI agents, applications, or any system that requests structured data.
• MCP servers act as intermediaries, fetching data from various APIs, databases, or enterprise systems and returning it in a consistent format.

Instead of AI models making direct API requests, MCP servers manage the complexity of authentication, data retrieval, and response normalization. This eliminates the need for AI agents to handle multiple API credentials, various request formats, or inconsistent response structures.

Practical Demonstration of MCP with Claude Desktop

The actual excitement begins now. Here, we will utilize Claude Desktop (from my personal laptop) and connect Claude with a couple of MCP Servers to demonstrate some exciting examples.

I have configured several servers, including Airbnb, Google Maps, and Perplexity Ask, for my Claude Desktop.

When MCP servers are, all the tools available on those servers become accessible to Claude Desktop.

We are now prepared to present several use cases and videos demonstrating the actual execution.

Use case - Using Perplexity to look for information

Let us request Claude to use Perplexity to inquire about the President of the United States. Claude (MCP client) will initiate a connection with Perplexity (MCP server) and utilize the available tool to find this information.

Use case - Looking for Airbnb accommodation

In this scenario, I am seeking accommodation for two people in Manali. Claude (MCP client) will connect with Airbnb (MCP server) to find suitable accommodation options.

Use case - Web scrapping

Let us extract the data from the website https://modelcontextprotocol.io/introduction in a structured format.

Amazing, isn't it? There are numerous possibilities, such as accessing databases, different systems, and applications. Many MCP servers are being developed by both enthusiasts and official organizations, you can refer these on https://github.com/modelcontextprotocol/servers/

Comparing AI Systems with and without MCP

By now, you can understand what MCP with example demonstrations is. The below infographic provides a comparison between AI systems without MCP and with MCP.

Key Considerations for Adopting MCP

Authentication Complexity

MCP aims to reduce dependency on custom integrations but initially lacked a secure authentication mechanism. OAuth 2.1-based authentication is now being integrated to enable secure, remote data access. However, full adoption of secure auth flows is still evolving and not yet plug-and-play.

Unclear Identity Representation

It’s not always clear who is making a request - AI agent, user, or application. Currently, external systems only see the identity of the user or service account whose credentials are used. There is no native concept of “agent identity” yet; solutions rely on OAuth user tokens.

Authorization & Access Control Challenges

Fine-grained access control must be implemented externally (e.g., through server-side policies or tools like MCP Guardian). The protocol does not dictate how authorization boundaries should be managed in complex multi-user environments.

Vendor-Led Standardization Concerns

MCP is led by Anthropic and integrated first with Claude models, raising questions about vendor neutrality. Early signs show some industry support: Anthropic’s announcement cited companies like Block (Square) and Apollo as early adopters integrating MCP, and developer-tool vendors such as Zed, Replit, Codeium, and Sourcegraph working to enhance their platforms with MCP. Microsoft’s AI team also took note – the Semantic Kernel project published a guide on using MCP, demonstrating compatibility with OpenAI’s models and other LLMs.

These examples indicate that MCP can be implemented outside Anthropic’s walled garden, hinting at real interoperability potential.

Conclusion

At present, MCP is neither a guaranteed panacea nor a trivial fad. It offers a compelling solution to interoperability – one that many developers and researchers are excited about – but it also introduces new complexities and relies on network effects to truly succeed. Experts counsel a pragmatic approach: “None of these limitations are show-stoppers, but it’s wise to start with experimental or non-critical deployments” while the protocol matures. Reference huggingface.co

MCP’s creators have been rapidly improving the spec (e.g. adding OAuth support, registry plans, etc.), and the open-source community is actively contributing tooling and feedback. The coming months will be telling. As one analysis concluded, MCP is technically sound and strategically promising – now the question is whether the industry will rally behind it.

In summary, MCP could become the “USB-C of AI” (a universal connector) if adopted widely, but the path to that outcome requires solving the remaining security pieces, clarifying identity management, and, most importantly, achieving buy-in from other major AI platforms to avoid a fragmented landscape.

A Note to Readers

The purpose of this article is to educate and spread awareness about this evolving topic. While every effort has been made to ensure clarity and accuracy, there is always room for better explanations or more relevant examples. Any misinterpretations are entirely unintentional, as I am also learning alongside you.

The credit for these technological advancements belongs to the brilliant inventors and developers who have made them possible. Let’s appreciate their contributions as we continue to explore these innovations together.