MCP isn't just another tool. MCP ( Model Context Protocol ) is quickly becoming the new standard for connecting language models like ChatGPT, Claude, or Copilot with real-world data, tools, and applications.
What is the MCP (Model Context Protocol)?
MCP is an open protocol developed by Anthropic (creators of Claude) that establishes a standardized, secure, and scalable way for AIs to access external data or perform actions outside of their “closed” environment.
Until now, if you wanted an AI to interact with, say, your c level contact list calendar, your documents, or your database, you had to create custom integrations, many of which were fragile, expensive, and difficult to maintain. MCP changes this paradigm: it offers a universal way to connect intelligent agents with external context sources.
Think of MCP as the USB-C of AI: a single connector for thousands of tools.
How does MCP work?
MCP is based on three key components:
MCP Client : Lives inside the AI assistant (e.g., Claude Desktop or GitHub Copilot). It generates requests and coordinates interactions.
MCP Server : A service that exposes external functions or data (such as your Google Calendar, a CRM, a GitHub repository, or even a custom API).
Tools or Resources : These are the specific functionalities that the server makes available (check availability, obtain sales from your ERP, create a file, etc.).
Communication is done using a standardized protocol , which means that any client can talk to any MCP server without the need for specific integration between them.
What problem does it solve?
Before MCP, connecting AI agents with external tools was a hacky process. If you had n agents and m tools, you needed to create n × m integrations . With MCP, that complexity is reduced to n + m : each agent only needs to communicate with the MCP protocol, and each tool only needs to have one MCP server.
It's such a breakthrough that I can imagine MCP's impact could be comparable to that of TCP/IP in the creation of the Internet: a standard that unleashes the true potential of connection between systems.
Real-life examples of use
This is where things get interesting. Some cases already active in the community include:
Meeting Automation : An AI assistant can access your calendar via MCP, find a time slot, and schedule a meeting, all with a single natural language instruction.
Business Intelligence : Queries like “How did we close the last quarter?” can be resolved directly by connecting to MCP servers for your ERP, BigQuery, etc.
Workflows with other tools : Thanks to an MCP server connected to, for example, Zapier or n8n , an AI can trigger automations on thousands of services.
Integration with development tools : Editors like Cursor or Zed use MCP to query databases, review code on GitHub, or run tests.
Custom Agents : Some developers are already creating long-term memory agents using MCP to connect data, vector databases, or error logs from your apps.