Model context protocol (MCP) is quickly growing in popularity as a means for enabling AI assistants to connect and communicate with a range of data sources, tools, and services that can better inform their actions, recommendations, and decisions. The protocol standardizes this communication, thereby laying a stronger foundation for agentic AI.
Acting similar to APIs, MCP servers typically sit in front of a data store or service, making it easier for agents to pull the information they need, when they need it, without customized integration overhead. Companies can use MCP servers to expose their own data to their own AI processes, or to external users, and they can also use pre-built MCP servers to connect to popular services such as PayPal, Zapier, and Shopify.
But enterprises planning to use MCP servers as part of their AI strategies should be aware of the risks they may bring. And there are a lot of risks and potential vulnerabilities to watch out for. Here are the 10 of the most common issues organizations can encounter when employing MCP.