AI Agents & MCP
Model Context Protocol (MCP) Explained in Plain English
MCP is the standard that lets AI assistants use tools. Here is what it is, how it works, and why it matters for businesses, without the jargon.
Short version
- The Model Context Protocol (MCP) is an open standard, introduced by Anthropic, for connecting AI assistants to tools and data.
- An MCP server exposes tools (like “send email”); an MCP client (like Claude Desktop or Cursor) discovers them and lets the AI call them.
- MCP saves writing a custom integration for every assistant and every tool, but you still need to control which tools an agent can use.
In this article
AI assistants are good at understanding what you want. On their own, though, they can’t do much about it: they can’t send a message, check a calendar or update a database. The Model Context Protocol (MCP) is the standard that fixes that.
This explainer covers what MCP is, how it works and why it matters, without assuming you’re a developer.
What is the Model Context Protocol?
MCP is an open standard for connecting AI applications to tools and data. Anthropic introduced it in late 2024, and it has been adopted widely across AI assistants and developer tools since.
The idea is simple. Before MCP, every AI app needed a custom integration for every tool: one for email in app A, another for email in app B, and so on. With MCP, a tool provider builds one MCP server, and every MCP-compatible assistant can use it. People often compare it to USB-C: one standard plug instead of a drawer full of adapters.
How does MCP work?
There are two sides:
- MCP server: a program that exposes tools (actions, like “send a WhatsApp message”), and optionally resources (data) and prompts. Each tool has a name, a description and the parameters it needs.
- MCP client: the AI application, such as Claude Desktop, Cursor or Windsurf. It connects to servers, discovers their tools and lets the AI call them.
When you ask the assistant to do something, the model looks at the available tools, decides which to use, fills in the parameters and asks the client to call it. The result comes back to the model, which decides what to do next. Most clients show you each tool call and ask for approval.
Local and remote MCP servers: what’s the difference?
- Local servers run on your own computer and usually work with your files, terminal or local apps.
- Remote servers run on the internet and connect the assistant to online services. They use an HTTP-based transport and usually require an API key.
For example, the NexBotix MCP server is remote: it lives at https://nexbotix.online/api/mcp and gives an assistant tools to send and schedule WhatsApp messages and emails. Our WhatsApp MCP setup guide shows the exact configuration for Claude Desktop and Cursor.
What can businesses do with MCP?
MCP turns an AI assistant from something that writes into something that acts. A few examples:
- A sales rep asks the assistant to send a follow-up message to the clients from today’s call notes.
- A developer asks their coding assistant to notify the team on WhatsApp when a deployment finishes.
- An operations manager asks for a list of scheduled reminders and cancels the ones for a closed office.
None of these needed a custom integration: the assistant used tools from an MCP server.
MCP or a regular API?
They solve different problems. A REST API is for your own code: an order system that sends a confirmation, or an app that sends an OTP code. An MCP server is for AI assistants, so a person can ask for an action in plain language and the assistant carries it out. Many services, including NexBotix, offer both.
What should I check before connecting an MCP server?
Giving an AI the ability to act deserves the same care as giving a new employee access:
- Trust the source. Only connect servers from providers you trust.
- Limit the tools. Connect servers that do what you need, not everything possible.
- Protect keys. API keys in config files can send on your behalf; keep them out of shared repositories.
- Require confirmation for actions with consequences, like sending messages or changing data.
- Watch for prompt injection. Content the AI reads, like a web page or email, can contain instructions. Keep a person in the loop for important actions.
The bottom line
MCP is quickly becoming the standard way AI assistants connect to the rest of your software. For businesses, it means AI that can do real work, as long as you choose the tools carefully and keep people in charge of the decisions that matter.