MCP: the "USB-C port" for AI.

What MCP is

Before MCP, every AI app needed a custom integration for every tool: one connector for ChatGPT and Google Drive, another for Claude and Google Drive, another for Copilot, and so on. The Model Context Protocol replaces that tangle with a single, open standard.

Anthropic introduced MCP as an open-source project in late 2024. It was rapidly adopted across the industry — including by OpenAI, Google, and Microsoft — and in late 2025 moved to a vendor-neutral foundation under the Linux Foundation. Today, MCP support is built into many popular AI assistants, developer tools, and agent platforms.

The common analogy is USB-C: one standard connector that lets many devices work with many accessories.

How it works

MCP has three main pieces:

• Host: the AI app you use — a desktop assistant, a chat app, a code editor, or an agent platform.
• Client: the part of the host that speaks MCP and manages connections.
• Server: a small program that exposes a system — your files, CRM, calendar, database, or ticketing tool — in a standard way.

An MCP server can offer three kinds of things:

Servers can run locally on your computer or remotely as a hosted service. Remote servers typically use standard OAuth sign-in so access can be granted, scoped, and revoked like any other business app.

{
  "mcpServers": {
    "helpdesk": {
      "url": "https://mcp.example-helpdesk.com",
      "auth": "oauth",          // sign in; scopes you can revoke
      "tools": ["search_tickets", "draft_reply"]
    }
  }
}

Why it matters for your organization

• Less lock-in: an MCP integration you build once can work with many AI assistants, so you can switch platforms more easily.
• Faster integrations: many software vendors now publish official MCP servers for their products.
• Custom systems, too: a small MCP server can safely expose your own database or line-of-business app — for example, a church management system or an internal knowledge base.
• Foundation for agents: MCP is how many AI agents reach the tools they use.

Practical use cases

• Ask your assistant questions about documents in SharePoint or Google Drive — with your existing permissions.
• Let staff look up customer, member, or donor records in plain English (read-only).
• Draft help-desk replies grounded in the ticket history.
• Generate weekly reports that pull from several systems at once.
• Give developers AI help that understands your codebase and tickets.

Security: what to check before you connect

MCP makes it easy to connect AI to powerful systems — which is exactly why it needs care.

• Only use trusted servers. Prefer official servers from the software vendor or ones your team has reviewed. A malicious server can steal data or manipulate the AI.
• Scope permissions tightly. Use read-only access where possible and dedicated credentials, not an admin account.
• Keep approvals on. Require a person to confirm actions that write, send, delete, or pay.
• Watch for injection. Content flowing through a server (emails, web pages, tickets) can contain hidden instructions. Treat it as untrusted.
• Pin and review versions. Don't let servers auto-update silently; review changes like any other software.
• Log everything. Keep a record of which tools were called, with what inputs, by whom.

How WHU AI helps

We help organizations choose and configure trusted MCP servers, and we build custom, security-reviewed MCP servers for systems that don't have one yet — with scoped access, approval flows, and audit logging built in. Book a free call about connecting your tools.

Further reading

• Model Context Protocol — official documentation — introduction, concepts, and specification
• Hugging Face AI Agents Course — hands-on agents and tool use
• DeepLearning.AI short courses — including MCP and agent courses