Before MCP, every AI application had to build every tool integration itself. MCP standardises this connection, like a common plug: an MCP server built once can be used by different AI applications.
Architecture
A host is the AI application users work with. Within it, a client connects to one or more MCP servers. A server provides tools, functions the model can call; resources, data it can read; and prompts for recurring tasks. Communication follows a defined protocol, locally or over the network.
What it is suited for
For AI assistants and AI agents that need access to company systems: creating tickets, searching documents, reading data from a CRM. Software vendors increasingly offer MCP servers so customers can use their products from AI applications.
Security risks
An MCP server gives a model access to real systems. Typical risks follow: overly broad permissions letting an agent do more than needed; prompt injection via content a tool returns; manipulated tool descriptions that lead a model to unintended actions; and missing logging, so it cannot be traced what an agent did.
Good practice
Least privilege per server and tool, dedicated credentials instead of shared admin accounts, human confirmation for consequential actions, logging of all calls and only servers from trusted sources. The steps are in our checklist for securing MCP servers; context is in our article on MCP and AI agents.