MCP vs Claude Skills: What's the Difference
Skills teach the model how to act; MCP lets it actually reach live systems. Here's the line I draw between them.
MCP vs Claude Skills come down to one distinction: a Skill is packaged instructions plus files that Claude reads when a task matches; MCP is a live connection to a running server that gives the model callable tools. Skills change what the model knows and how it acts. MCP changes what the model can reach. Pick Skills for repeatable know-how, pick MCP when the model needs to touch a real system, and use both together more often than you'd think.
The confusion is understandable — both are ways to "extend Claude," and both ship as folders you drop somewhere. But they solve different problems, fail in different ways, and cost different amounts. Here's the split I use when deciding.
The one-sentence difference
A Skill is static content; MCP is a running process. A Skill is a SKILL.md file (instructions, plus optional scripts and reference docs) that Claude loads into context when the task looks relevant — no network, no server, no auth. MCP is a client-server protocol: a server advertises tools like read_file or search_facts, and the model calls them at runtime over a transport.
That difference cascades into everything else:
| Claude Skills | MCP servers | |
|---|---|---|
| What it is | Instructions + files (a folder) | A running server exposing tools |
| Runs a process? | No — read into context | Yes — stdio, HTTP, or SSE |
| Touches live systems? | No, unless it calls a Skill script | Yes — that's the point |
| Auth / credentials | None | Often API key or OAuth |
| Best at | Procedures, style, domain know-how | Data access, side effects, actions |
| Where it works | Claude apps + Agent SDK | Any MCP client (Claude, Cursor, etc.) |
| Failure mode | Model ignores or misapplies it | Server down, wrong tool called, blast radius |
If you remember nothing else: Skills teach; MCP acts.
When a Skill is the right tool
Reach for a Skill when the thing you're encoding is knowledge or procedure, not access. Skills shine for "how we do X here" — your team's PR conventions, a spreadsheet-generation recipe, a domain glossary, a multi-step workflow the model keeps getting slightly wrong. The Skill sits idle until a task matches its description, then it loads. No server to keep alive, nothing to authenticate.
The killer property is progressive disclosure. A Skill's short description is always in context, but its full body and bundled files only load when triggered — so a dozen Skills cost you almost nothing until one fires. Compare that to MCP, where every connected server's tools sit in the context window whether you use them or not.
Skills also travel light. There's no transport, no port, no credentials to leak. The trade-off: a Skill can't do anything on its own. It can tell the model exactly how to format a Postgres migration, but it can't run one. The moment the task needs to read live data or cause a side effect, you've hit the wall.
When you need MCP instead
Reach for MCP the moment the model has to touch something real: your filesystem, a database, an API, a browser, a memory store. A Skill can describe your files; it can't read them. That's the job of a server like the official Filesystem reference server, which exposes read/write/search tools confined to directories you explicitly grant. If you want the full menu of what servers can reach, the capabilities index is the fast way to browse by function.
MCP is also the answer when you want the same integration across tools. A Skill is a Claude-side concept; an MCP server works with any MCP client — Claude Desktop, Cursor, Windsurf, the Agent SDK. Build one server, use it everywhere. That portability is a real reason to prefer MCP for anything you'd otherwise reimplement per-tool.
The cost is operational. MCP servers are processes: they can be down, misconfigured, or over-permissioned. Most servers run locally over stdio — like Filesystem, which needs nothing more than this:
{
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/absolute/path/to/dir"]
}
That's the whole config: a local process, no auth, scoped to one directory. Remote servers run over HTTP or SSE instead, where you're now managing URLs and credentials. For the deeper "what even is a server" question, start with what is an MCP server.
The trap: tool budget
The reason to not solve everything with MCP is the tool budget. Clients degrade once too many tools are in context — past some point selection gets unreliable and the model starts calling the wrong thing. Every MCP server you connect spends from that budget, all the time, whether or not you use its tools that session.
Skills don't have this problem. A Skill's body is out of context until it triggers, so ten Skills cost roughly what zero cost until one activates. This is the strongest architectural argument for the hybrid approach: push know-how into Skills, reserve MCP tool slots for genuine access. If you've got a 30-tool server connected purely to describe a workflow the model could learn from a Skill, you're burning budget for nothing.
Concretely: don't connect a heavyweight server just to teach a procedure. Write the procedure as a Skill, and let it call a small set of MCP tools for the parts that need live access. That keeps your tool count lean and your instructions rich.
Memory: the case where they overlap
Persistent memory is where the two approaches genuinely compete, and it's worth being precise. You can encode stable facts in a Skill — but a Skill is read-only at runtime; the model can't write new memories back to it. For memory the model updates, you need a server.
The official Memory (Knowledge Graph) server is the minimal option: file-backed entities and relations, stdio, no auth, persisted to a local JSONL file. For heavier needs, OpenMemory MCP is Mem0's local-first stack (Docker + dashboard, keeps memories on your machine), and Graphiti MCP from Zep does temporal knowledge graphs over Neo4j or FalkorDB — facts with validity windows, not just a flat store. Note the split: Memory is an official reference server; OpenMemory and Graphiti are capable community builds, so vet maintenance and auth before you wire them into anything real.
Rule of thumb: static domain facts → Skill. Facts the model needs to write and update → a memory MCP server. Facts with a time dimension → Graphiti.
How I'd actually decide
Start with the question: does the model need to touch a live system or cause a side effect? If no, it's a Skill — cheaper, safer, no budget cost. If yes, it's MCP. Then check whether the MCP tools you'd add are really pulling their weight against the tool budget; if a server exists only to convey instructions, that's a Skill wearing a server costume.
The best setups use both: Skills carry the judgment and procedures, MCP carries the reach. When you're ready to pick servers, the best MCP servers list is where I'd start, and if you're still deciding between an MCP integration and a plain API call, MCP vs API draws that line.