Claude Skills vs MCP servers

Skills and MCP servers get mixed up because both make an AI assistant more capable. They solve different problems, and you usually end up using both together.

The short version

A skill teaches the model how to do something. An MCP server gives the model a standard, reusable way to reach something. One is know-how, the other is access.

What a skill covers

A skill is a folder of instructions, saved as files, that shapes how the model behaves: your steps, your rules, your examples. It can bundle scripts, and in Claude Code those scripts can reach the network the same as any other program on your machine, but a skill does not define a live connection to an external system the way MCP does. If the task is "write release notes the way we write them," a skill like Humanizer is a good fit: it packages a house style, not a connection to anything.

What an MCP server covers

MCP is an open protocol for connecting an assistant to outside tools and data. A server sits between the model and a system such as a database, an API, or your files, and exposes actions the model can call on demand. If the task is "read the open issues from our tracker," that needs a server like the GitHub MCP server, because the model has to reach live, changing data it cannot get from a prompt alone.

Using both

The two work well side by side. A skill can tell the model how to triage a bug report, and an MCP server can hand it the actual open issues to triage. Reach for a skill when you are packaging know-how. Reach for a server when you need live access to a system outside the conversation. Plenty of real setups use one of each, and neither replaces the other.