If you’ve spoken to any of us at Activate Intelligence recently you’ll know that we can’t stop banging on about MCP Servers for enterprise AI. We can’t get enough of them. Usama actually suggested we make one for our lunch spot rating database so we can ask Claude where we should go each day.
TL;DR: We combine MCP Servers (structured access to your real data) with Skills (contextual prompts that encode domain expertise) to create AI tools that don’t just answer questions — they understand why you’re asking. The result feels less like a chatbot and more like a rational colleague.
We’ve been using skills alongside MCP Servers and we think it’s cool. Really cool. Skills are the backbone, the “why”, and MCP Servers are the knowledge, the “what”. When you put these two elements together, you get something that feels less like a chatbot and more like a rational colleague: one who understands what your organisation does, why it does it, and has access to the data to back it up.
How MCP Servers for enterprise AI work in practice
So what does this look like in practice? A skill is essentially a prompt: a descriptive guide that injects context about who the organisation is, how they operate, and what they need from the data. The MCP Server, on the other hand, is the structured interface that lets an AI model like Claude reach directly into your systems (databases, knowledge graphs, file stores) and retrieve real information during a conversation. Think of it as giving Claude hands to reach into your actual systems, rather than just a brain to think about them.
This combination creates this idea of a “rational colleague”, who has an understanding of what you do and has access to the data to support it. They can provide a context that so often gets lost in amongst all the noise of daily business. That elusive why that has gone missing for so many organisations? We’ve found it, and it lives at the intersection of these two things.
And it doesn’t have to be internal. We’ve built MCP Servers for clients that give their teams access to proprietary datasets — compensation benchmarks, legislative records, media monitoring — paired with skills that encode the domain expertise needed to query that data intelligently. A business consultant can ask nuanced questions about market trends and get answers grounded in their own organisation’s survey data, contextualised by their industry knowledge. A public affairs team monitoring parliamentary activity can surface relevant legislative developments across multiple chambers, filtered through the lens of what actually matters to their clients. None of this requires the end user to write a query or understand the underlying architecture. They just ask.
“That elusive why that has gone missing for so many organisations? We’ve found it, and it lives at the intersection of these two things.”
What is an MCP Server?
So, for the uninitiated, what actually is an MCP Server? MCP (Model Context Protocol) is a standardised protocol that allows AI models like Claude to interact with external systems. An MCP Server is the component that exposes those systems — databases, knowledge graphs, file stores, APIs — to the model through that protocol. Instead of Claude being limited to its training data and what you paste into the chat, MCP Servers let it directly query, retrieve, and modify real-world information during a conversation. That last part is important: this isn’t just read access. Your teams can update records, log insights, and enrich datasets as they work, meaning the system gets smarter with every interaction.
The exciting thing is that it can act as a shared knowledge base that anyone within the organisation can dip into. This has so many advantages. We have an MCP Server for Activate Intelligence that is growing daily, one that will in the future be used to help onboard newcomers who can ask any question they like as to how the business works and why. We’ve already seen this principle at work in projects like transforming corporate media monitoring and measuring the impact of press releases, where MCP Servers for enterprise AI give teams direct access to the data that matters.
The key insight we keep coming back to is this: the MCP Server on its own gives you data. The skill on its own gives you context. But together, they give you understanding. And that’s what we’re building towards — not just tools that answer questions, but tools that understand why you’re asking.
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