Claude Skills vs MCP: Instructions vs Connections

Skills and MCP are not competing answers to the same question, which is why "which should I use?" usually gets a muddy response. They sit on different axes:

• A Skill is instructions. Markdown that tells Claude how to do a task — the steps, the standards, the format. It changes behavior.
• An MCP server is a connection. A running program that gives Claude access to a system it otherwise can't reach. It changes reach.

The test that resolves almost every real case: ask whether Claude is failing because it doesn't know your process, or because it can't reach your data. Process problems are Skills. Access problems are MCP. If the honest answer is both, you need both, and they compose cleanly.

Quick comparison

Both are now open formats, which is a change worth being careful about. MCP is an open standard adopted by OpenAI, Google, Microsoft and others, with governance in a neutral foundation since December 2025. Agent Skills started at Anthropic and became an open standard of its own; OpenAI documents skills as "a directory with a SKILL.md file plus optional scripts and references" and supports them in Codex and the ChatGPT desktop app, and other agents have followed.

The distinction that survives is narrower than "Claude-only." A SKILL.md you write is portable as a file — the effort you put into the instructions isn't locked to one vendor. How you install and run it is not portable: each product has its own location, its own packaging, and its own idea of which bundled scripts and tools a skill may use, so moving one across usually means re-homing it rather than copying it.

What a Skill actually is

A folder. Inside it, a SKILL.md file with YAML frontmatter carrying a name and a description, then markdown instructions. Optional supporting files sit alongside.

There's no server and no long-running process. Claude reads the instructions and follows them.

One qualifier, because it's the detail that gets flattened in most explanations: a Skill can also bundle scripts, and Claude runs those through bash when the instructions call for them. That's a deliberate part of the design — a script is more reliable than asking Claude to regenerate the same code each time, and only its output enters the context window. But it means "a Skill is just markdown" is a description of the common case, not a guarantee about what's in the folder.

The mechanism that makes this efficient is progressive disclosure. Claude holds every installed Skill's name and description cheaply, and loads the full body only when one looks relevant to what you asked. So a dozen Skills cost almost nothing until one fires. Anthropic's Claude Code documentation covers the loading behavior, and the Agent Skills overview covers the format across surfaces.

This design has a consequence people miss: the description field is the most important line in the file. It's the only part Claude sees when deciding whether to load the Skill at all. A vague description produces a Skill that never fires, or one that fires constantly.

Skills are good at: your team's code review standards, a document format you always want, a recurring analysis procedure, house style, a deployment checklist. Anything where the knowledge is how to do it properly and Claude already has everything it needs to act. Our rundown of the Skills worth installing first covers what Anthropic publishes and how to install them.

What an MCP server actually is

A program that runs, speaks the Model Context Protocol, and exposes capabilities: tools Claude can invoke, resources it can read, prompts it can offer.

It's real running software with real access. The filesystem server reads directories you've allowed. A database server holds a connection string. A ticketing server holds an API token. That's the point — and it's also why the evaluation is different in kind from installing a Skill.

MCP is good at: your internal API, a production database, a project management tool, your files, a browser. Anything where the gap is Claude can't see it.

Our guide to the official reference servers covers the seven the MCP project maintains and how to judge third-party ones.

The cost profile is different, and it matters

Both consume context, but not in the same way, and this is the practical reason not to treat them interchangeably.

A Skill is nearly free until it's used. Names and descriptions only, then the body loads on demand. Installing ten Skills you rarely use costs you very little.

An MCP server is usually not free while connected. In the straightforward setup, every tool definition from every connected server sits in the model's context before your question does. A server exposing forty verbose tool definitions can consume a meaningful share of the window and measurably degrade answers on unrelated tasks.

That's the default, not a law. Hosts have started deferring tool definitions rather than loading them all up front: Anthropic's tool search tool lets tools be marked defer_loading: true so they enter context only once Claude searches and finds them — including per-server, for MCP toolsets — and the MCP documentation points clients that federate many servers at the same progressive tool discovery pattern. So whether a connected server costs you context before you've asked anything depends on the host you're using.

The advice survives the nuance, because the deferral isn't universal and tool-selection accuracy still degrades past a few dozen tools: be generous with Skills, be selective with MCP servers. The most common cause of an agent setup getting worse over time is accumulating server connections nobody uses.

How they compose

The clearest way to see they aren't alternatives is a case that needs both.

Say you want Claude to produce your team's weekly metrics report. Two separate failures are possible:

• Claude can't reach the analytics database. That's MCP. A database server gives it the ability to run the query.
• Claude reaches the data and produces something in the wrong shape, missing the segments you always include, with the wrong definition of an active user. That's a Skill. The procedure and the definitions are knowledge, not access.

Fix only the first and you get correct data in a useless format. Fix only the second and you get a beautifully formatted report with no data in it.

In practice the pattern is: MCP provides the raw capability, a Skill tells Claude how to use that capability the way your organization actually needs. Anthropic's engineering write-up on Agent Skills makes the same distinction from the other direction.

There's even a Skill for building the other half: mcp-builder, in Anthropic's public Skills repository, scaffolds MCP servers to a working pattern.

Which to reach for first

Reach for a Skill when you find yourself explaining the same process to Claude repeatedly, the output format keeps being wrong, quality varies between sessions on the same task, or a colleague needs to get the same result you do.

Reach for MCP when Claude asks you to paste something it should be able to fetch, you're copying data from a system into the chat window by hand, or the task needs current state from a live system.

Start with a Skill if you're unsure. It's cheaper to try and cheaper to undo — a folder you add and a folder you delete, with no process to run and no credentials to issue.

Cheaper is not the same as harmless, and one line in particular is worth not repeating: a Skill is not inert. A Skill can bundle executable scripts that Claude runs, and its instructions can direct Claude to use bash, file, and network tools. Anthropic's own security guidance is explicit that a malicious Skill can lead to data exfiltration or unauthorized system access, and that you should treat installing one like installing software. So for anything from a third party, read the SKILL.md, read the bundled scripts, check any external URLs it fetches from, and look at what access Claude will have while it runs.

An MCP server still deserves more scrutiny — it's running code that usually holds credentials — but the gap is one of degree, not a line between safe and unsafe.

The step before either one

Both of these assume you're already running Claude Code or Claude with a place to put a Skills folder and a configuration file to edit. For a lot of people that assumption is the whole obstacle — the interesting setup is visible, and reproducing it isn't.

Taku works on that specific gap: mirroring an AI app, skill, or workflow someone already got running into your own desktop workspace, so you start from a working configuration instead of building one. If you've read plenty about Skills and MCP without ever getting a setup running, the free app library is a more direct route. Taku is in Beta, and the Mac app is available now.

Key points

• Skills are instructions that change how Claude works. MCP servers are connections that change what Claude can reach.
• The diagnostic: is Claude failing on process, or on access? Process means a Skill; access means MCP.
• A Skill is a markdown folder, sometimes with scripts Claude runs. An MCP server is running software — local or remote — usually holding credentials.
• Skills load on demand and cost almost nothing idle. MCP tool definitions occupy context whenever connected, unless your host defers them.
• Be generous with Skills, selective with servers — but audit third-party Skills before installing. They are not inert.
• Both are open formats under neutral or published standards. The SKILL.md file is portable; how you install it is per product.

FAQ

Should I use Claude Skills or MCP?

Whichever matches your failure. If Claude doesn't know your process, write a Skill. If Claude can't reach your data, connect an MCP server. Many real setups need both.

Can a Skill call an MCP server?

Effectively yes. A Skill's instructions can tell Claude to use tools that happen to come from a connected server. The Skill doesn't connect to the server itself — it directs Claude's use of what's already available.

Is MCP replacing Claude Skills?

No. They solve different problems, and Anthropic ships support for both — MCP itself is now governed by the Agentic AI Foundation rather than by Anthropic. The mcp-builder Skill is itself evidence they're designed to work together.

Which is easier to get started with?

Skills, by a wide margin. A Skill is a markdown file in a folder. An MCP server is software to install, configure, and often authenticate.

Do Skills work with other AI models?

The format does. Agent Skills is now an open standard, and OpenAI supports SKILL.md skills in Codex and the ChatGPT desktop app, with other agents adopting it too. What doesn't carry over automatically is installation: each product decides where skills live, how they're packaged, and what a skill is allowed to do while it runs. So the writing is portable; the setup is per tool.

Why did my agent get slower after adding MCP servers?

Usually because each connected server's tool definitions take up context before your prompt does, and because tool-selection accuracy falls as the list grows. Disconnect servers you aren't using for the current task. Some hosts now defer tool definitions until they're searched for, which softens the first problem but not the second.

Are Claude Skills safe to install?

Your own, yes. A third party's, only after you've read it. A Skill can ship executable scripts and can tell Claude to use bash, file, and network tools, so Anthropic's guidance is to audit the SKILL.md, the bundled files, and any external URLs before use, and to treat it like installing software rather than opening a document.

Can I share Skills and MCP servers with a team?

Both. A Skill committed to a repository's .claude/skills/ folder travels with the repo. MCP server configuration can be shared too, though each person supplies their own credentials.