A few years ago, Sarah*, a project manager, would have spent a busy Monday morning manually gathering information from multiple applications, searching through customer records, opening spreadsheets, checking project management tools, and writing updates herself.
While an AI assistant could help draft an email, it could not directly understand or interact with the different systems where her information was stored. Today, Sarah can simply ask her AI assistant: “Prepare a summary of our latest customer feedback, update the project tracker with the new tasks, and draft an email to the team about the changes,” allowing the AI to coordinate these tasks across her connected tools.
The AI assistant connects securely with the tools Sarah already uses. It retrieves customer insights from the company’s feedback platform, reviews tasks from the project management system, updates the relevant records, and creates a personalised email draft, all from a single conversation.
The technology making this type of interaction possible is known as the Model Context Protocol (MCP).
What Is Model Context Protocol (MCP)?
MCP is an open protocol that defines a standard way for AI models to discover, request, and use external tools and data, think of it as a universal adapter that lets an AI assistant plug into services like Google Drive, Slack, or a company's internal database without a developer having to hand-build a separate connection for each one.
How Does MCP Work?
An MCP "server" exposes a set of tools or data sources in a standardized format; an MCP "client", typically the AI assistant, can then discover what's available and call those tools as needed to complete a task. Instead of an AI vendor writing bespoke code to connect to every calendar app, project tracker, or database on the market, both sides only need to speak the same protocol once.
Why Does MCP Matter?
Before shared standards like MCP, connecting an AI assistant to outside tools required custom, one-off integrations that didn't transfer between AI platforms. MCP reduces that duplication of effort, making it easier for developers to build AI agents that reliably read and act on real, live data, emails, tickets, spreadsheets, rather than relying only on what the model was trained on.
Why Is MCP Gaining Traction?
As AI agents take on more autonomous, multi-step tasks, the need for a consistent way to hand them tools has become urgent. Broader industry adoption of MCP-style standards signals a shift toward interoperable AI ecosystems, where the same assistant can plug into many different services without vendor-specific rework.
What Should Businesses Know About MCP?
Because MCP effectively gives an AI model the ability to read and act on connected systems, security and permissioning matter just as much as functionality, organizations adopting MCP-based integrations need to control which tools an AI can access and what actions it's allowed to take without human review.
MCP is part of a broader trend of standardizing how AI systems connect to the real world, turning isolated chatbots into assistants that can actually get things done across the apps people already use.
As organisations increasingly adopt AI to improve productivity, the ability for assistants to securely access and work with existing business systems will become essential. Model Context Protocol provides a foundation for this next generation of AI interaction, where users can communicate naturally with intelligent assistants while those assistants connect seamlessly with the digital world around them.
By allowing AI models to communicate with the applications people rely on every day, MCP moves AI assistants closer to becoming practical digital collaborators rather than just question-and-answer systems.
