MCP servers vs Claude Skills: when to use which
Skills and MCP servers both extend an AI assistant, but they solve different problems. A short, practical split.
They get lumped together, but they aren't competitors — they compose.
MCP servers = connections
An MCP server gives the model tools with side effects on the outside world: query a database, send a Slack message, create a GitHub issue. It's a running process (local or remote) that the model calls. Use MCP when the model needs to do something in another system.
Skills = packaged know-how
A Skill is instructions plus files — a reusable procedure the model follows, often invoking tools along the way. It encodes how to do a task well: a code-review skill, a research-report skill, a changelog-writing skill. Use Skills when you want repeatable behavior, not a new connection.
Together
The best setups do both: MCP servers for the connections, Skills for the procedures that orchestrate them. A "weekly report" Skill can pull from your Postgres MCP, your Linear MCP, and your analytics MCP — and format the result the same way every time.