Skills vs. MCP: Understanding the Tools Powering the Next Generation of AI Agents

If you’ve been following the latest developments in AI engineering, you’ve likely heard two terms thrown around frequently: Skills and MCP (Model Context Protocol).

At a glance, they might seem like they are trying to do the same thing: help AI agents perform tasks. This has led to a bit of a debate in the developer community about which is “better.” However, pitting them against each other is a competition category error. They are not competing tools solving the same problem—they are complementary pieces of a larger puzzle.

In this post, we’ll break down what each technology does, how they differ, and why the future of AI agents relies on using them together.

What are Skills? (The “Recipe”)

Think of a Skill as a cheat sheet or a standard operating procedure (SOP) for an AI agent.

Technically, Skills are often lightweight Markdown files that contain procedural knowledge. They describe how to do a particular task, almost like a senior developer explaining a workflow to a junior developer. They capture domain knowledge, best practices, and team conventions so an agent doesn’t have to guess how you like things done.

Vercel recently released a “skills” ecosystem, allowing developers to install these packages with a simple command like npx skills add.

Key Characteristics of Skills:

• Format: usually text-based (Markdown) prompts.
• Purpose: To provide “procedural knowledge”—specific instructions on workflows or best practices.
• Pros: They are low-friction and easy to create. Because they utilize “progressive disclosure” (loading instructions only when relevant), they are great for guiding an agent through a complex business process without overwhelming its context window.
• Cons: They struggle with things like authentication and keeping state. They are also harder to version control once copied into a project.

Example: Imagine you want an agent to build a React component. You could install the vercel-react-best-practices skill. This skill doesn’t give the agent new software tools; rather, it gives the agent the ==knowledge== of how to write code that aligns with Vercel’s specific standards. Other popular examples include seo-audit for marketing or frontend-design guidelines.

What is MCP? (The “Power Tool”)

MCP (Model Context Protocol) is a standard for interoperability.

If Skills are the “recipe,” MCP is the “appliance.” MCP allows an AI agent to connect to an external service—like GitHub, Sentry, or a database—and actually use it. It acts as a universal translator that exposes a service so that any agent can interact with it without needing a custom integration.

One expert described MCP as “a User Interface for AI agents”. Just as a UI makes a database usable for a human, MCP makes a service usable for an AI.

Key Characteristics of MCP:

• Format: A client-server protocol. MCP servers are installable, stateful applications.
• Purpose: To provide access to tools, data, and services.
• Pros: It handles authentication (like OAuth) and security permissions securely, which Skills cannot do easily. It is designed for scale and broad interoperability.
• Cons: It has a higher barrier to entry than writing a Markdown file. For simple, one-off personal tasks, building an MCP server might feel like overkill.

Example: An agent needs to check a specific error in your application. It uses a Sentry MCP server to authenticate with your account, fetch the error logs, and retrieve the data. It isn’t just reading about how to debug (which would be a Skill); it is actually connecting to the Sentry platform to get the real-time data.

The Comparison: How Do They Differ?

To understand the difference, it helps to look at the scope of the problem they solve.

The Power Combo: Using Them Together

The reason the “Skills vs. MCP” debate is misleading is that the most powerful agents use both.

You can use a Skill to act as an orchestration layer—a manager that tells the agent which MCP servers to use and in what order.

A Real-World Scenario:

Imagine you are a developer fixing a bug.

1. The Skill: You have a bug-fix-workflow Skill. It tells the agent: “First, check Sentry for the error. Then, check GitHub for related issues. Finally, propose a fix using our team’s coding standards.”
2. The MCP: The agent follows these instructions:
   • Use Sentry MCP to securely log in and get the crash report
   • Use GitHub MCP to search your repository
3. The Result: The agent combines the ==data== retrieved via MCP with the ==process== defined by the Skill to solve the problem exactly how you want it solved.

The Verdict

• Use Skills when you need to teach the agent a process, a convention, or a “business recipe.”
• Use MCP when you need the agent to connect to a tool, read a database, or perform actions in an external system securely.

We are in the early days of this ecosystem. While Skills currently have some growing pains regarding versioning and clutter, and MCP is still being adopted by service providers, both are essential building blocks. Instead of choosing one, savvy engineers are learning how to make them dance together.