In the early days of smartphones, every manufacturer had its own charger, meaning that if you switched phones, you also had to swap out all your accessories. It was frustrating, wasteful, and slowed adoption. The industry eventually solved this with a universal standard: the USB-C connector.
Prior to Model Context Protocol (MCP), every agent needed custom, hard-coded integrations to access external data sources and perform actions. This was time consuming, expensive, and brittle. For example, a change to one tool’s API could break every agent using it. It was hard to adopt AI agents broadly across an organization in the absence of any standardization.
MCP is emerging as the “USB-C of AI,” a universal standard that makes it easier and more efficient for AI agents to plug into the systems where your work gets done, whether that’s Slack, Salesforce, GitHub, or a private SQL database.
In this first part of our two-part series, we’ll explain what MCP is, how it works, why it matters for agentic AI, and what it means for enterprise security and governance.
Breaking It Down: What Exactly Is MCP?
At its core, MCP is a protocol. It’s a set of rules that says, “Here’s how an AI application should talk to an external system.” Developers can either expose their data through MCP servers or build AI applications (MCP clients) that connect to these servers. This is what the architecture looks like: 
- MCP host: The AI-powered application or agent environment that the end user interacts with.
- For example, Claude Desktop or Cursor.
- MCP client: Lives within the host application and calls the MCP server, which contains tools.
- MCP server: Small program that provides the client with tools it can use. Note that an MCP server is not usually a “server” in the traditional sense of the term; instead, it is software that you download and run.
- For example, the Slack MCP Server includes tools to list channels, send messages, etc.
- You can find a list of "official" servers from the MCP Servers org here.
With MCP, the code is standardized, written and maintained by the MCP server provider, and exposed through a single, consistent interface that MCP clients can easily consume.
Why Does MCP Matter for Generative and Agentic AI?
We’re entering a new phase of AI adoption. GenAI is no longer just about passively generating content or answering questions. Increasingly, we want agentic applications with tools that can plan, take action, and collaborate with us across multiple systems.
But here’s the catch: If every agent and every assistant has its own way of connecting to data and tools, enterprises face a tangle of custom integrations, duplication of effort, and vendor lock-in. This slows down innovation and increases costs. Today, each model vendor has its own version of “function calling.” If you switch from one model to another, you often have to rebuild all your integrations. Additionally, if a vendor updates their API, you might need to update your tool code manually (and worse, it’s likely you won’t realize this until the tool breaks one day.)
MCP addresses that challenge head-on by offering a vendor-neutral, reusable layer. Build a connector once and any MCP-compatible AI application can use it, whether it’s powered by GPT, Claude, or a local model. That creates a much stronger foundation for scaling AI across an organization.
For example:
- A marketing team could expose a catalog of “safe actions” like “pull campaign results” or “generate a customer segment.”
- A sales copilot and a data science copilot could both use those same MCP connectors, even if they’re powered by different models.
The Bigger Picture: Building for the Future of AI
MCP shows significant promise as a foundational standard for the future of agentic AI applications. It is designed to make AI systems more dynamic, modular, and composable.
The industry is already moving in this direction. Some operating systems have added MCP support natively, which means AI apps can request access to your files, calendar, or dev environment the same way apps ask for camera access today.
This is the groundwork for a future where AI isn’t just an “app”, but a trusted coworker that can collaborate across your digital workspace. Stay tuned for part two where we’ll dive into the practical side of how MCP compares to alternatives, the real business benefits, and how organizations can get started today.