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magine asking your AI assistant to not only draft an email but also send it via your mail app, schedule a meeting on your calendar, and update a spreadsheet – all seamlessly. This vision is becoming reality thanks to a new concept buzzing in AI circles: MCPs. MCP stands for Model Context Protocol, an emerging open standard introduced in late 2024 that lets AI systems connect with tools and data in a uniform way. In simple terms, MCPs are like a common language that allows AI models to talk to external apps and services. Instead of each AI integration being a custom one-off project, MCPs provide a universal interface for AI-to-tool communication. The result? Smarter, more capable AI assistants that can use various tools on your behalf, without a tangle of bespoke code.
Today’s AI models are powerful at generating text and ideas, but they often operate in isolation, unable to take actions in the software we use daily. Each time an AI needs to fetch information or execute a task (like pulling data from a database or sending a message), developers traditionally had to wire it up with custom code or APIs. This approach is fragmented and hard to scale – imagine every AI app reinventing how to connect to Gmail, Slack, or any other service. MCPs solve this by offering a standardized way for AI agents to use tools and retrieve data. It’s analogous to what HTTP did for the web: before HTTP, information exchange was ad hoc; after HTTP, any browser could talk to any website. Similarly, MCPs aim to let any AI agent interface with any tool or service that speaks the protocol. This standardization unlocks richer, more interactive AI experiences. An AI assistant could maintain context and perform multi-step workflows (not just single Q&A responses) because MCP connections are stateful – meaning the AI and the tool maintain a running conversation. Developers benefit too: instead of writing custom integrations for each app, they can rely on a growing library of MCP-compatible tools. In short, MCPs make AI more useful by bridging the gap between intelligent models and the digital actions we want them to take.
The Model Context Protocol emerged from the AI developer community’s quest for a better tooling interface. The idea took inspiration from the success of the Language Server Protocol (LSP) in programming, which standardized how code editors interact with coding tools. In November 2024, leading researchers and engineers – notably at the AI company Anthropic – introduced MCP as an open-source protocol to do for AI what LSP did for coding assistants. Early prototypes showed how an AI model could call functions on a remote “MCP server” representing an app. For example, one of the first demos allowed a chatbot to use a web browser tool via MCP, enabling it to search the web when answering questions. This early success fueled excitement in developer circles. By mid-2025, the protocol gained traction: hackathons and open-source projects built dozens of MCP connectors (servers) for everything from email and calendars to databases and design software. Tech companies also took notice. OpenAI, Anthropic, and other major AI labs began incorporating MCP support into their platforms, so that their AI models could plug into this new ecosystem of tools. In essence, MCPs grew out of a practical need – AI systems needed a consistent way to get external knowledge and take actions – and the community-driven solution quickly evolved into a collaborative standard.
What does an MCP actually do? At its core, it defines a set of rules for how an AI (the “client”) and a tool/service (the “server”) communicate. They exchange messages (often in JSON format) that let the AI ask the tool to do something or retrieve information. Because this interaction is standardized, an AI agent doesn’t need bespoke coding to, say, check your calendar or query a database – it just needs the corresponding MCP server available. For a real-world illustration, consider a software developer using an AI coding assistant in an IDE (integrated development environment). With MCP support, the assistant can have multiple tools at its fingertips: it might use a database MCP server to fetch some test data, a GitHub MCP server to commit code changes, and a Slack MCP server to notify the team – all in one continuous workflow. The developer doesn’t have to leave the IDE or manually hook up these integrations; the AI agent orchestrates them as needed. Another example: a business user could have an AI agent that integrates their work apps. If you ask, “Summarize last quarter’s sales and email the team if any product fell below target,” an MCP-enabled agent could automatically pull data from a sales database, generate a summary, then use an email MCP server to draft and send an email – following the steps just from your single request. These scenarios show how MCPs turn an AI from a static oracle into an active assistant that can carry out tasks in the digital world.
Sovereign AI is more than a buzzword – it’s quickly becoming standard practice for those who can’t afford to lose control over data. Current solutions already make it feasible to “own your AI.” But looking ahead, we can expect even more user-friendly options. Just as HTTP became the backbone of the internet by standardizing communication, MCP could become a foundational layer for the AI-driven software era. Major tech providers are already supporting MCP, and ongoing improvements (from authentication standards to governance tools) are addressing its current limitations. For policy people and business leaders, embracing MCP could be a competitive advantage. It signals to customers and citizens that AI innovation won’t come at the expense of privacy or security. For developers and startups, it levels the playing field – you can harness advanced AI tool integrations on your terms, building unique solutions without surrendering control of your data.
In sum, MCPs are about freedom and interoperability: the freedom to explore AI’s possibilities without having to reinvent the wheel for every tool, and the interoperability that lets your AI systems plug into whatever services you need. As one tech community motto puts it, “Own your data, own your destiny.” In the AI era, that destiny includes the very brains and memories of our AI companions. MCPs, by keeping those under our own roof and standardized across platforms, might just be the key to unlocking AI’s benefits with confidence and trust.
Blue Canvas is an AI consultancy based in Derry, Northern Ireland. We help businesses across the UK and Ireland implement AI that actually delivers results — from strategy to deployment to training.
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Ready to empower your sales team with AI? BlueCanvas can help make it happen. As a consultancy specialized in leveraging AI for business growth, we guide companies in implementing the right AI tools and strategies for their sales process. Don’t miss out on the competitive edge that AI can provide
Ready to empower your sales team with AI? BlueCanvas can help make it happen. As a consultancy specialized in leveraging AI for business growth, we guide companies in implementing the right AI tools and strategies for their sales process. Don’t miss out on the competitive edge that AI can provide
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