MCP (Model Context Protocol)

MCP, short for Model Context Protocol, is an open standard for connecting AI models to tools and data. Anthropic published it in November 2024, and within months other model providers, code editors and automation platforms adopted it. People often compare it to a USB-C port: one connector to plug the assistant into any system that has an MCP server.

What problem does MCP solve?

A language model only knows what it learned in training. To be useful in a business, it needs to read your data and act in your tools: look up an order, find a contract or create a task.

Before MCP, each connection was hand-coded for each assistant. Connecting three assistants to five tools meant up to fifteen separate integrations. With MCP, each tool publishes a server once and any compatible assistant can use it.

How does MCP work?

MCP has two sides:

• MCP client: the application where the assistant lives. For example Claude Desktop, a code editor or an agent you built yourself.
• MCP server: a small program that gives access to one system (your CRM, a database, email, a document repository).

The server declares what it offers, using three kinds of elements:

• Tools: actions the model can run, such as “create contact” or “find invoice”.
• Resources: data the model can read, such as a document or a record.
• Prompts: instruction templates prepared for frequent tasks.

When you ask the assistant for something, the model sees the list of available tools, picks the right one and the client runs it through the server. The result returns to the model, which decides whether it needs another step.

What does a business use MCP for?

MCP matters most in two situations.

Giving your team a connected assistant. With the right MCP servers, a person can ask the assistant “which customers in the north region have not bought this quarter?” and the assistant queries the CRM instead of inventing an answer.

Building agents you can maintain. If an AI agent uses MCP to reach your systems, switching models (from GPT to Claude or Gemini) does not force you to rebuild the integrations. And when you add a new tool, you only connect its server.

Many well-known applications already publish official or community-maintained MCP servers: project managers, databases, design tools, code repositories and office suites. For internal or uncommon software, such as an industry-specific ERP, someone has to build the server.

Example: an accounting firm with a connected assistant

Picture an accounting firm with 15 people. The accountants lose time hunting for the status of each client file, spread across the practice management software, email and a shared folder.

The firm sets up one MCP server with read-only access to its practice software and another for the document folder. It connects them to the AI assistant the team already uses. From then on, an accountant can ask “what is this client missing to file the quarter?” and the assistant cross-checks the software data with the documents received and returns the list of pending items.

Because the permissions are read-only, the assistant cannot change anything. If the firm later wants it to draft and send the reminder email, that tool gets added with human confirmation before each send.

What are the risks and best practices?

An MCP server gives the model real access to your systems, so treat it like a new employee:

• Install only servers from trusted sources and review the permissions they ask for.
• Grant the minimum access: read-only when that is enough.
• Require confirmation before actions that delete, pay or send something to customers.
• Log which tools the agent uses and with what data.

If you want to connect your tools to an assistant or build agents on MCP, we do it within our AI agent development service. When a system has no server available, we build one as part of a custom software development project.