Key takeaways
- MCP is an open standard that lets AI assistants connect to tools and data through one consistent interface.
- One MCP server for a system replaces a separate integration for every AI tool your team uses.
- Treat an MCP server like any API with write access: scoped permissions, confirmations and audit logs.
The problem MCP solves
Your team already uses AI assistants. On their own, those assistants can't see your CRM, your ticketing system or your internal database, so people copy and paste context back and forth.
Building a custom integration for every assistant and every system doesn't scale. The Model Context Protocol (MCP) fixes that with a standard way for AI applications to discover and use external tools and data.
What an MCP server is
MCP is an open protocol, introduced by Anthropic and now supported across many AI clients and developer tools. It has two sides:
- An MCP client is the AI application: a chat assistant, a coding tool or your own agent.
- An MCP server is a small service that sits in front of one of your systems and describes what the AI can do with it.
A server exposes three kinds of capabilities: tools the AI can call ("create a support ticket"), resources it can read ("this customer's order history"), and prompts, reusable templates for common tasks.
Why your business systems need one
- Build once, use everywhere. One MCP server for your CRM works with any MCP-compatible assistant your team adopts.
- Your rules, in one place. Permissions, rate limits and data filtering live in the server, not scattered across tools.
- A foundation for agents. The same server your people use through an assistant can power custom AI agents that run on their own.
- Visibility. Every call goes through one door, so you can log and review what AI did in your systems.
Building one safely
An MCP server with write access is a powerful API. Design it with the same care:
- Authenticate per user. The AI should act with the permissions of the person using it, never a shared admin account.
- Separate read and write tools. Start read-only; add actions one at a time.
- Require confirmation for risky actions like refunds, deletions or emails to customers.
- Guard against prompt injection. Content the AI reads (emails, web pages, tickets) can contain instructions. Don't let retrieved text trigger sensitive tools without a human check.
- Log everything: who asked, which tool ran, with what inputs and what happened.
Where to start
Pick the system your team copies data out of most often, list the five questions they ask it, and expose those as read-only tools. We build MCP servers as part of our AI agents service, and for older platforms as part of legacy modernization.