MCP vs Skill vs Plugin

The question I get most: "What's the difference between MCP, Skill, and Plugin? Which should I build?" Fair. The names sound similar, all mean "give AI more capability." They're not the same thing. They live at different layers, solve different problems.

This post breaks down all three, then gives a decision framework so you don't waste time building the wrong one.

Analogy: Three Layers of the Phone World

Quick mental model from the phone world:

• Plugin: like WeChat mini-programs. Platform-exclusive. Only works inside that platform. Build a WeChat mini-program and it won't run inside Alipay.
• Skill: like a phone app. A complete capability package with its own interaction, business logic, and user system. But it depends on a platform—like an app running on iOS or Android.
• MCP (protocol): like the USB-C connector standard. Not a product. A standard. If your tool implements MCP, every MCP-compatible platform uses it directly.

See? These three aren't on the same layer. Plugin is platform-layer, Skill is application-layer, MCP is protocol-layer. They don't compete. They combine.

MCP: Protocol-Layer Standard, Cross-Platform Tool Calling

I've written about MCP before. Short version: Anthropic's open protocol for standardizing how AI calls external tools.

Traits:

• Cross-platform: Claude, Cursor, Windsurf, custom agents—any MCP-compatible client connects to the same MCP server.
• Tool-provider-led: tool providers implement MCP servers. AI clients connect.
• Tool calling focus: solves "how AI calls an external tool." Doesn't handle upper-layer user interaction.
• Stateless state: new Stateless architecture. Lightweight servers, easy deployment.

RollingGo's hotel MCP is an example. It implements hotel search, price lookup, and booking as tools. Any MCP-compatible AI client—Cursor, Claude Code, Windsurf, Coze—connects and uses booking capability directly. No adapter code. Behind it: 2M+ global hotel properties and 110K+ direct-contracted hotels. Direct inventory with real-time price confirmation. What you see is what you can book. Completely free, no call-volume limits. Provides an all-in-one booking skill for ClawHub, Coze, and Qclaw. Developer partners can set country-based markup rates and earn commission.

Thousands of developers have already integrated. The plug-and-play nature of MCP beats both Plugin and Skill for effort. Want to feel it? Grab an API key and run it in ten minutes.

Setup in Cursor:

{
  "mcpServers": {
    "RollingGo-Hotel": {
      "url": "https://mcp.rollinggo.ai/mcp",
      "type": "streamable-http",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY"
      }
    }
  }
}

Restart Cursor and you're searching hotels directly in chat. MCP's protocol-layer value: every AI client plugs in and works. That's the core difference from Skill and Plugin.

Skill: Application-Layer Capability Pack, With Its Own Interaction and Logic

"Skill" has been hot, especially after RED Skill's ecosystem launched on Xiaohongshu. What is it?

A Skill is a complete application-layer capability pack. Not just "a tool call interface." It includes:

• Business logic: what the capability does, what rules run behind it.
• Interaction flow: how the user engages, step by step, what happens at each step.
• Tool orchestration: may call multiple MCP tools internally, chained in specific order.
• User experience design: how results display, how to guide the next step.

Take a Xiaohongshu RED Skill example. An "AI trip planning Skill" might call a hotel MCP, flight MCP, and attraction MCP internally. But it doesn't just pile tools together. It has its own orchestration logic: ask for preferences first, then plan the itinerary, then recommend hotels and flights, then generate the full guide. The whole flow, interaction, and experience is the Skill.

Skill traits:

• Platform-bound: Skills run on specific AI platforms. RED Skills run in the Xiaohongshu ecosystem.
• Complete capability focus: not a single tool interface, but a capability pack that finishes a task end to end.
• Experience design included: Skills aren't just technical interfaces. They have product design and interaction flow.
• Can reuse MCP tools: Skills orchestrate multiple MCP tools into a complete capability.

Plugin: Platform-Layer Extension, Exclusive to One AI App

Plugin was popularized by ChatGPT. 2023, OpenAI launched Plugins so ChatGPT could call external APIs. OpenAI stopped pushing it hard, but the concept stuck.

Plugin traits:

• Platform-exclusive: your ChatGPT Plugin only works in ChatGPT. Different AI platforms don't recognize it.
• Relatively simple capability: most Plugins wrap a single API so ChatGPT can call it.
• Closed ecosystem: distributes only within one platform. Limited reach.

Pure Plugin mode isn't popular anymore. Too closed. Developers spend time on a plugin that works on one platform only. Bad ROI. People prefer MCP—build once, use everywhere.

The "Plugin" concept itself didn't disappear. It evolved. Now many platforms call it "extension" or "integration." Essentially the same idea: a platform-specific plugin system.

Three-Way Comparison

Dimension	MCP	Skill	Plugin
Layer	Protocol (technical standard)	Application (complete capability pack)	Platform (specific platform extension)
Cross-platform?	Yes, all MCP-compatible platforms	No, bound to a specific platform	No, only within one platform
What it is	Standardized tool-calling interface	Complete task capability + interaction flow	Feature extension for one AI app
Who builds it	Tool/data providers	AI app developers / platform owners	Third-party developers (for one platform)
Example	Hotel MCP server, database MCP	Xiaohongshu RED Skill, AI agents	ChatGPT Plugin, GPTs
Value	Makes tools reusable by all AI	Gives users a complete, usable capability	Extends one platform's features

Which Should You Build?

The question everyone asks: what do I actually build?

If you're a data/tool provider (hotel, flights, SaaS)

Build an MCP server. Your core value is data and tool capability. You need every AI app to use it. MCP is cross-platform. Build once, use everywhere. Highest ROI.

RollingGo built a hotel MCP because hotel inventory is core capability. We need every AI travel app to call it directly, not negotiate integrations one by one.

If you're an AI app developer (building assistants, agents)

Build a Skill. You face end users. You need complete product experience—interaction, tool orchestration, result presentation. These are Skill-layer problems.

Your Skill can call MCP tools underneath. No need to integrate hotel APIs and flight APIs yourself. Use ready-made MCP servers. Focus on user experience and business logic.

If you're an AI platform developer

You might need a Plugin system. Let third-party developers build plugins that extend your platform. But ideally, your Plugin system supports MCP underneath. That way, a third-party developer builds one MCP server and it connects to your platform directly. No custom Plugin needed.

Best Practice: MCP + Skill Combo

Industry best practice right now is combining MCP and Skill:

• Bottom layer: vertical tool providers build MCP servers (hotel MCP, flight MCP, weather MCP).
• Middle layer: AI app developers orchestrate these MCP tools into Skills (trip planning Skill, business travel Skill, hotel customer service Skill).
• Top layer: AI platforms (Xiaohongshu, Doubao, Claude) provide Skill distribution and runtime.

This is a layered ecosystem. Bottom standardizes. Top differentiates. Like the phone world: bottom is OS and hardware standards, top is apps, platform is the app store.

This ecosystem is forming fast. Tens of thousands of MCP servers, growing Skill ecosystems. We build hotel MCP and partner with Skill platforms at the same time—opening hotel MCP capability to trip planning Skills. Thousands of developers have integrated, supporting 40+ major LLM clients. Developer partners set country-based markup rates and earn commission. This is the model.

Closing Thoughts

MCP, Skill, and Plugin sound similar but live on completely different layers. Know the difference and you know what to build and what to skip.

Stop asking "should I build MCP or Skill?" If you're a tool provider, build MCP. If you're an app developer, build Skill. If you're a platform, build a Plugin system. They're not either/or. They combine.

How are you connecting AI to tools right now? Hand-written Function Calling? Already on MCP? Drop a comment.