What is MCP? Anthropic’s New Way of Making AI Smarter

Explore MCP by Anthropic — a new protocol that makes AI more flexible, accurate, and context-aware using live data sources.

Akash Bhadange

Mar 17, 2025 • 6 min read

AI is growing really fast, and the way we build AI systems is also changing quickly. In the past, everyone focused on making bigger AI models that could remember more and handle longer conversations. But now, things are changing. Instead of just making models bigger, people are finding smarter ways to help AI get the right information when it needs it. One of these new ideas is something called Model Context Protocol, or MCP, introduced by Anthropic.

At its core, MCP is not another model architecture or training breakthrough. It’s a protocol—a standard interface that allows AI models to dynamically augment their context by accessing structured external tools, memories, and knowledge systems during inference. It’s a subtle but profound shift in how we build and scale intelligent systems.

Why Traditional Context Windows Fall Short

Language models, by design, operate within a fixed-length context window. Over the years, this window has expanded from a few thousand tokens to over a million. Yet even these advances cannot solve a fundamental limitation: once the prompt is constructed, the model is essentially closed off from the world. It cannot retrieve fresh data, react to new queries with updated information, or interact with memory systems that evolve with time.

This static approach creates several challenges. Developers must continually fine-tune or retrain models for domain-specific tasks. Large prompts become expensive and fragile. Embedding-based retrieval systems add complexity and still often fail to capture the nuanced, real-time context that users expect.

MCP addresses this problem by enabling language models to augment their reasoning with live, structured context through a standardized interface.

What is MCP?

MCP stands for Model Context Protocol. It provides a mechanism for models to retrieve external context from specialized tools during inference. These tools are modular systems designed to deliver precise, structured information relevant to the task at hand.

Rather than bloating the prompt with data, MCP allows models to dynamically invoke these tools when needed. A tool might be a document retriever, a database interface, a memory store, a search API, or even another domain-specific agent. The model decides which tool to use based on the prompt and task. Once invoked, the tool returns context, which is then integrated into the model’s reasoning pipeline.

What makes MCP transformative is that it decouples the model from the data. The model becomes more like a reasoning engine, and tools become its sensors and memory systems. This modular architecture enables far more scalable and adaptable AI systems.

In short

MCP essentially allows AI models to “query” external systems the way a developer might query an API. Each tool provides a response in a format the model can interpret, and the model uses that context in its reasoning process.

How MCP Works?

Imagine a user submits a prompt to a language model. The model begins processing it but identifies that additional context is needed—perhaps a user’s profile data, a recent news article, or an answer from a knowledge base. Through the MCP interface, the model queries the relevant context tool. The tool returns structured information, which is then fed back into the model’s context pipeline. The model completes its response based on both the original prompt and the dynamically fetched context.

This flow effectively transforms the model into a context orchestrator—one that doesn’t need to “know everything,” but rather “knows how to find everything.”

Let’s say you ask an AI assistant, “What’s my next meeting?”

A normal AI model would struggle because it doesn’t have access to your calendar. But with MCP, the AI can ask a tool that knows your schedule before responding.

Here’s what happens step by step:

You ask a question (e.g., “What’s my next meeting?”).
The AI realizes it needs more information to answer correctly.
It uses MCP to connect to the right tool—in this case, your calendar.
The calendar tool gives the AI the correct information (e.g., “You have a meeting at 3 PM with Sarah”).
The AI combines that information with what it already knows and gives you the final answer.

This process happens instantly, making the AI much smarter and more useful.

The Technical Perspective

From a technical standpoint, MCP introduces several key constructs.

Each tool is defined by a schema that specifies its input type, context scope, and output format. These tools can be invoked explicitly by the developer or implicitly by the model itself. The invocation process happens during runtime and must comply with strict latency requirements to maintain inference speed. Some tools may even implement pre-fetching or anticipatory caching to reduce delays.

Security and privacy become critical in this setup. Since models now interact with external systems, each tool must be sandboxed, access-controlled, and auditable. Any information retrieved must be tracked for origins to ensure accountability and prevent hallucinations rooted in inaccurate context.

Advantages and Limitations of MCP

The biggest advantage of MCP is its modularity. Developers no longer need to retrain large models to specialize them. Instead, they can simply build a new tool and connect it to the model via MCP. This dramatically reduces the time and cost of domain-specific AI development.

Another benefit is transparency. With context tools clearly defined, developers can trace how outputs were influenced and debug AI behavior more effectively. It also enables leaner models that offload domain knowledge to tools, making them more general-purpose and reusable.

However, the system isn’t without trade-offs. The orchestration layer introduces new complexity. Building robust tools that are accurate, fast, and secure is a non-trivial task. Additionally, there’s always the risk that poorly designed tools could inject biased, irrelevant, or manipulated data into the context pipeline. This raises new challenges around reliability and governance.

New Business Opportunities Unlocked by MCP

The introduction of MCP opens up an entirely new layer of infrastructure and value creation, similar to how APIs led to the rise of the API economy.

Startups can now build domain-specific context tools and monetize them as plug-and-play components. A company specializing in legal research could offer a tool that feeds AI models with up-to-date legal precedents. An education platform might build tools for real-time curriculum mapping or student feedback analysis. A fintech startup could develop tools to stream market data into a financial advisory assistant.

Another promising space is tool management. Just as companies use API gateways today, we’ll see platforms emerge for managing, authenticating, and monitoring context tools. These platforms may offer access logs, billing meters, rate limits, and trust scores for every tool plugged into an MCP-compatible model.

There’s also room for infrastructure services like memory layers, retrieval-as-a-service, or tool orchestration engines that rectifies the technical complexity for developers.

MCP hints at a future where language models act more like operating systems and less like standalone apps. They become orchestrators of context, interacting with a network of tools rather than reasoning in isolation. This has major implications for the future of multi-agent systems, personal AI assistants, and domain-specific copilots.

Anthropic’s introduction of the MCP is a signal that the AI industry is beginning to mature beyond raw model size and into architectural sophistication. As we shift from monolithic intelligence to modular cognition, protocols like MCP may well define the next era of AI system design.

Whether MCP becomes an industry-wide standard or inspires competing alternatives, it’s clear that the race is no longer just about bigger models—it’s about better context.