Build an MCP server only if you need external AI agents — Claude, ChatGPT, Cursor, or a customer's internal agent stack — to call your product directly and take real actions on a user's behalf. If you just want your own product to have an AI feature, you don't need MCP at all; you need a normal integration with an LLM API, which is a different and usually cheaper project.
Model Context Protocol (MCP) is the open standard Anthropic published for connecting AI agents to external tools and data sources (spec and docs). It's gained real traction fast — OpenAI, Google, and most agent frameworks now support it — which is why it's showing up in founder roadmaps this year. But "the industry adopted a standard" and "we should build one" are different questions, and we're seeing teams skip straight past the second one.
What an MCP server actually does
An MCP server is a thin adapter. It exposes a fixed set of your product's actions and data — "create an invoice," "look up a customer's order status," "schedule a shipment" — as callable tools, described in a schema an agent can read and reason about. The agent (running in Claude, a custom app, whatever) discovers your tools, decides which one to call, and gets structured results back.
