Without MCP, every AI tool needs a custom integration for every service. With it, a service publishes an MCP server that describes what it offers, and any compatible AI client, such as a coding assistant or chat app, can connect to it. Builders meet MCP when they let Cursor or Claude talk to their database, their GitHub account or their deployment platform.
That convenience is also the risk. An MCP server runs with whatever credentials you give it, and the model decides when to call it. If the server holds an admin database key, then text the model reads, including a malicious issue, web page or document, can steer it into using that key. This is prompt injection with real tools attached. The protocol's own security guidance also covers problems such as confused deputy attacks, token passthrough and session hijacking in servers that handle authorisation badly.
Treat each MCP server like a new employee with system access. Install only servers from publishers you trust, give each one a narrowly scoped credential instead of your most powerful key, prefer read-only access, point development assistants at a development database and keep approval prompts switched on for actions that write, delete or send data.