The Model Context Protocol, usually called MCP, is an open standard for connecting AI assistants to other software and data. Instead of writing a separate custom connection every time you want an AI model to read your order sheet, search your documents or create a task in your project tool, a developer builds an MCP "server" for that tool once, and any AI application that supports MCP can use it. Anthropic, the company behind Claude, first published it, and a growing range of AI apps and developer tools now support it.

The problem MCP solves

On its own, an AI model knows only what it learned in training and what you type into the conversation. It does not know today's stock, this week's orders or your latest price list. To be useful in a business, it needs two things: access to your information and a safe way to take actions.

Before a shared standard, every pairing of an AI app with a business tool needed its own custom code. Each new AI app or tool multiplied the number of integrations to build and maintain, each one done slightly differently. MCP replaces that with one agreed method. People often compare it to a standard charging port: once devices share the same plug, any charger that follows the standard works.

How MCP works

MCP describes three roles.

  • Host: the AI application a person actually uses, such as Claude's desktop app, an AI-enabled code editor, or a custom assistant built for your team.
  • Client: the part inside the host that holds the connection to one server.
  • Server: a small program that gives the AI access to one tool or data source, such as a shared drive, a database or your own inventory system.

A server can offer three kinds of things:

  • Tools: actions the AI can ask to perform, such as "search orders" or "create a quotation draft".
  • Resources: information the AI can read, such as files, records or pages.
  • Prompts: ready-made instruction templates for common tasks.

A server can run on your own computer or as a remote service over the internet. Either way, the host decides which servers are connected, and a well-designed host asks the user before a tool takes an action.

What this can look like in a business

Some hypothetical examples:

  • Stock questions in plain language. A distributor in Rajshahi keeps stock in a database. An MCP server with a read-only "check stock" tool lets staff ask an assistant how many cartons of a product are at the Natore warehouse, without opening the inventory software.
  • Answers from your own documents. An office keeps its SOPs and HR policies in shared folders. A server that exposes them as resources lets an internal assistant answer staff questions and point to the source. The company knowledge AI assistant solution describes this kind of set-up.
  • Preparing for a sales call. A sales team connects its CRM so the assistant can summarise a lead's history before someone picks up the phone.

The business benefits are practical. A connection built once can be reused by different AI apps. You can change AI models later without rebuilding every integration. And many common services already have MCP servers, published either by the service itself or by the wider developer community.

MCP and APIs: not a replacement

MCP does not replace APIs. Most MCP servers call a tool's existing API behind the scenes. What MCP adds is a standard way for an AI assistant to discover what a tool can do and to use it. If you are new to how software systems talk to each other, the article on API integration is a good starting point. MCP is also what many AI agents use to reach their tools, which is covered in AI agents explained.

Safety questions to ask before connecting anything

Connecting AI to real systems is where most of the risk lies, so go slowly.

  1. Who built the server? Prefer official servers from the service provider, or ones a developer you trust has reviewed. A server is code running with access to your data.
  2. What can it see and do? Start with read-only access. Use a separate account with limited permissions rather than an owner or admin login.
  3. Does it ask before acting? Require confirmation before anything is written, sent or deleted.
  4. What text will the AI read? Emails, web pages and documents can contain hidden instructions written to mislead an AI. Narrow tools limit the harm a misled assistant can do.
  5. Where are the keys kept? Passwords and API keys belong in secure settings, never in prompts or shared chats.
  6. Is there a record? Keep a log of which tools were used and with what inputs.

Do you need MCP?

MCP is probably worth it if you want an AI assistant to work with several of your systems, your team already uses AI apps that support it, or you want to avoid being tied to one AI provider.

It is probably not your first step if you have one simple automation (a direct integration may be simpler), or if your key information still lives in notebooks and scattered files. In that case, organise the data first.

A good next step is to list the three systems your team checks most often during the day, and note which questions about them people ask repeatedly. That list shows where a connected assistant would help. For help planning a safe set-up, see AI and workflow automation.

Related service AI and workflow automation Claude-powered assistants and automated workflows that take repetitive work off your team, while people keep the final say.