Comparison
MCP vs Agent Skills
MCP (Model Context Protocol) vs Agent Skills (SKILL.md), both are open standards. Learn which to use for tools/data vs reusable instructions, and how they work together.
Short answer
MCP is for connecting AI tools to external systems (databases, APIs, file systems). Agent Skills (SKILL.md) is for reusable natural-language instructions. They're complementary, most teams use both, with the skills-hub MCP server exposing skills as MCP prompts.
MCP (Model Context Protocol)
Anthropic (open standard)
Wire AI to tools, data, and capabilities
Best for: Exposing databases, APIs, file systems, and custom capabilities to AI clients.
Visit MCP (Model Context Protocol) →Agent Skills (SKILL.md)
Open Agent Skills standard
Reusable, portable, versioned AI instructions
Best for: Codifying recurring workflows: code review, deploy, security audit, content authoring.
Visit Agent Skills (SKILL.md) →Feature comparison
| Feature | MCP (Model Context Protocol) | Agent Skills (SKILL.md) |
|---|---|---|
| Primary purpose | Tool / data integration | Reusable instruction sets |
| Format | JSON-RPC over stdio or HTTP | Markdown + YAML frontmatter |
| Runtime | Long-running server process | Static text, loaded into context |
| Versioning | Server-controlled | EdgeSemVer in frontmatter |
| Installable from registry | Yes, npm, Smithery | Yes, skills-hub.ai |
| Capability type | EdgeTools, resources, prompts | Instructions only |
| Open standard | Yes, Anthropic | Yes, community-driven |
| Adoption | Claude, Cursor, Cline, Windsurf, more | Claude, Cursor, Codex, Windsurf, more |
Primary purpose
Format
Runtime
Versioning
Installable from registry
Capability type
Open standard
Adoption
Pick MCP (Model Context Protocol) when
- →You need to wire AI into an external data source (DB, API, file system)
- →You're building custom capabilities that aren't just instructions
- →You want long-running tool execution with side effects
- →You need fine-grained permissioning of what AI can call
Pick Agent Skills (SKILL.md) when
- →You're codifying a recurring workflow that's mostly instruction
- →You want to share a workflow across teammates with version control
- →You want the same workflow to run in Claude Code, Cursor, Codex, Windsurf
- →You want to publish your workflow for others to install
Verdict
Use MCP when you need AI to *do* something external (query a DB, hit an API, scan a Notion workspace). Use skills when you need AI to *follow* a specific workflow consistently. They compose: the skills-hub MCP server exposes the entire skill catalog as MCP prompts, so you can install skills from inside any MCP-compatible client.
Frequently asked questions
Can a skill use MCP tools?
Yes. Skills are instructions that can reference any tool exposed via MCP. A 'deploy' skill can call MCP servers for Vercel, AWS, or your custom infra in its steps.
Is one going to replace the other?
No, they solve different problems. MCP is the standard for tool/data integration; SKILL.md is the standard for reusable instructions. The skills-hub MCP server proves they compose well.
Do all MCP-compatible clients support skills?
Effectively yes, via the skills-hub MCP server. The server exposes installed skills as MCP prompts, so any MCP client (Claude Desktop, Cursor, Windsurf, Cline, Continue, GitHub Copilot Chat) can list and invoke them.
Which has wider adoption?
MCP has broader adoption among AI clients (30+ at time of writing); SKILL.md has more cumulative installs because individual skills are smaller, faster to ship, and easier to compose.
How do I expose my own skill collection over MCP?
Run `npx @skills-hub-ai/mcp` locally, it scans `~/.claude/skills/` and `~/.cursor/skills/` and exposes every installed skill as an MCP prompt. Or connect any MCP client to https://api.skills-hub.ai/mcp to query the full catalog without local installs.
Related comparisons
Install skills that work in both
Skills follow the open Agent Skills standard, install once, use in any AI tool.
Browse 4,900+ skills →