Demystifying the Model Context Protocol

(Source: Jane Kelly/stock.adobe.com; generated with AI)

Artificial intelligence (AI) tools are evolving from standalone models into interconnected systems, where chatbots and agents interact with data and resources across various applications as needed. As a result, context has gone from being optional to being essential; that is, it has become more important to ensure that models have the right information at the right time and that they understand this information the same way across tools, sessions, and even platforms. The Model Context Protocol (MCP) plays a key role in this process. In the same vein as protocols like USB Type-C, which standardize device connection, MCP is an emerging standard designed to make AI systems more interoperable by formalizing how context is structured and shared. This blog describes why MCP matters and how it works, and provides a concrete example in action.

Why Does Context Matter in AI?

Anyone who has worked with large language models (LLMs) knows the pain of “prompt drift.” One minute, the model is answering correctly, and the next, it is hallucinating wildly. Often, the missing ingredient is context: past conversations, relevant documents and data, active tools, or even something as simple as the user’s intent. With missing information, this decrease in performance is exacerbated by the move from simple model queries to more complex multistep workflows. For complicated workflows of chained elements (e.g., data lookup for answer retrieval that flows to response generation, which then connects to tool use for automated distribution), the need for shared, structured context becomes critical. Without it, every component starts from scratch, and models lose the thread of what they are doing and why. The difficulty, then, is that context comes in many different forms, from simple text like a user’s general preferences to more complex formats like database interfaces and document repositories or even application programming interfaces (APIs). Fortunately, MCP can help.

What Is MCP?

MCP is a proposed open standard for describing and exchanging contextual information with AI models, particularly language-based foundational models. Organizations like Anthropic and OpenAI, as well as contributors in the open-source tooling ecosystem, are exploring MCP. The goal is to help users build agents and complex workflows on top of models that frequently need to integrate with data and tools.

At a high level, MCP defines a way to package and send structured context to a model so it can make more informed, consistent decisions. It also enables external tools, like file systems, browsers, or calendars, to participate in AI workflows without bespoke, vendor-specific integrations. By providing this standardized shared language and protocol for sending and receiving context, MCP enables cross-model and cross-vendor compatibility, easier tool integration and chaining, and better observability and debugging for model behavior. For example, in agent frameworks, integrated development environments (IDEs), and research workflows, MCP enables code, user intent, and intermediate outputs to flow through the system in a dependable way.

How Does MCP Work in Practice?

MCP follows a host–client–server architecture that standardizes how models interact with external tools and data. In this setup, the MCP host is the application that is integrating AI, such as an IDE, notebook, or enterprise assistant. The host includes one or more MCP clients responsible for coordinating individually with various MCP servers. These servers then use the formalized protocol to expose specific capabilities—like tools, files, databases, or contextual APIs—to the model in a uniform way.

Each server describes its capabilities through codified interfaces, often structured using JavaScript Object Notation (JSON) schema, so that clients and models can interpret tool offerings and context formats repeatably and robustly. This avoids the need for bespoke glue code or model-specific prompts for every integration. For instance, a code editor might expose the current file tree, open buffers, and access recent edits as well-defined schema-backed objects.

Designing MCPs with security in mind is a priority. Rather than models accessing arbitrary tools directly, the host mediates access between the client and external resources via the MCP server, enforcing user permissions and limiting what models can access. Common practices include process sandboxing, explicit path restrictions, encrypted communication, and server authentication. All of these ensure that model access to sensitive tools or data remains safe and auditable.

This architecture lets developers compose rich model-aware environments where components can exchange structured context dynamically. Additionally, because of the standardization, all elements are more easily interchangeable, with the flexibility to choose between different model providers or toolchains.

What Is a Real-World Example of MCP?

To see how MCP might be used, consider a context-aware code assistant. Typically, this kind of application would sit within a programmer’s IDE so it can assist as the programmer develops software. With MCP, the code assistant would likely receive elements such as the file state, which would include elements like the current file, two recently modified files, and user intent codified as instructions, such as, “refactor this function to remove side effects.” A final piece that is vital for development is the execution context, including the call stack, variable types, and breakpoints (Figure 1).

Figure 1: JSON encoding captures context as part of MCP. (Source: Author)

Rather than guessing, the model now has a structured view of the coding session and can tailor its suggestions accordingly. Access to each resource is secure and generalizable through server-mediated communication (Figure 2).

Figure 2: The code assistant use case relies on this MCP host–client–server architecture. (Source: Author)

Conclusion

MCP may sound abstract, but its impact is concrete. Given that AI models are made smarter and more consistent by adding context, it is clearly a necessary ingredient as AI workflows get more sophisticated. By establishing a shared foundation for how models understand and interact with their environment, MCP opens the door to more powerful, transparent, and interoperable AI systems. Whether the application involves building agentic tools, working with multimodal apps, or trying to tame LLMs in production, MCP provides a way to reason about what a model knows and what it should know. Furthermore, the process of cleanly separating models from resources and ensuring secure communications between the two is “baked in”—another strength of MCP. With AI tools becoming more interconnected, a consistent, open format for context becomes increasingly important as the ecosystem matures, rivaling the importance of the model itself.