MCP for business: connecting AI assistants to the systems you already run
The Model Context Protocol is quickly becoming the standard way to give AI secure access to real tools and data. Here's what it is, why it matters, and how to start.
AI assistants are remarkably capable — until they need something that lives inside your business. Your CRM, your order database, your internal documentation, your ticketing system. Without access, even the best model can only guess.
The Model Context Protocol (MCP) is an open standard that solves this. It defines a common way for AI applications ("clients") to connect to "servers" that expose tools, data and prompts.
The three building blocks
An MCP server can offer three kinds of capabilities:
- Tools — actions the AI can take, such as
create_invoiceorget_order_status. Each tool has a typed input schema. - Resources — read-only context like files, records or reports that the AI can pull into a conversation.
- Prompts — reusable, parameterised instructions for common workflows.
Because the protocol is standard, a server you build once works with any compatible client — chat assistants, IDE agents or your own internal tools.
Why it matters
Before MCP, every AI integration was bespoke. Each new model or assistant meant rebuilding the same connectors. MCP turns that into a one-time investment:
- Build once, use everywhere. One server, many AI clients.
- Control stays with you. You decide exactly which actions and data are exposed.
- Auditable by design. Every tool call passes through code you own, so you can log, rate-limit and review it.
Designing a good MCP server
The protocol is the easy part. The craft is in the design:
- Model your domain, not your database. Tools like
refund_orderare safer and more useful than rawUPDATEaccess. - Scope permissions tightly. Authenticate users (remote servers typically use OAuth) and check scopes on every call.
- Make outputs legible. Return concise, structured results that a model can reason about.
- Evaluate with real tasks. Test the server against the workflows people will actually ask an assistant to do.
Where to start
Pick one high-value, low-risk workflow — answering order-status questions, drafting proposals from CRM data, triaging support tickets — and expose just the tools it needs. Measure the result, then expand.
If you'd like help identifying the right first workflow, talk to our AI Lab.