Claude Skills vs MCP - What's the Difference

Tech
ยทDante Chun

Anthropic provides two ways to extend AI agents.

MCP (Model Context Protocol) and Skills.

Both are tools for extending Claude's capabilities, but they have different philosophies and use cases. If you're confused about when to use what, this article will help.

One Sentence Summary

MCP is connection, Skills is instruction.

  • MCP: Lets AI access tools

  • Skills: Tells AI how to use tools

To use an analogy, MCP is "handing over a hammer," and Skills is "explaining how to use this hammer to drive nails."

MCP: Infrastructure Layer

MCP operates at the system level.

Characteristics

  • Handles authentication, network transport, API schema definition

  • Task-agnostic ("can read file X", "can query table Y")

  • JSON schema-based

  • Requires server execution

Advantages

  • Powerful tool integration

  • Connect to various data sources

  • Industry standard (adopted by OpenAI, Google too)

Disadvantages

  • Complex setup

  • Requires web server

  • Overkill for simple tasks

Skills: Knowledge Layer

Skills contain procedural/organizational knowledge.

Characteristics

  • Markdown file (SKILL.md) based

  • YAML frontmatter for metadata

  • No server required

  • Can include helper scripts

Advantages

  • Very simple setup

  • Just need to know markdown

  • Easy version control

  • Easy to share across teams

Disadvantages

  • Cannot directly connect to external systems

  • Limited for complex integrations

Comparison with Real Examples

Database Operations

MCP approach:

# PostgreSQL MCP server setup
claude mcp add postgres "postgresql://..."

Skills approach:

# SKILL.md
---
name: database-query
---

## Database Query Rules

- Use SELECT statements only
- Always include LIMIT 100
- Never query sensitive columns (password, ssn)

MCP connects to the database, Skills tells you how to use it.

Code Review

Skills alone is sufficient:

# SKILL.md
---
name: code-review
triggers:
  - "review the code"
---

## Code Review Checklist

1. Check security vulnerabilities
2. Check performance issues
3. Style guide compliance
4. Test coverage

If external system connection isn't needed, Skills alone is sufficient.

When to Use Which

When to Use MCP

  • Need to connect to external systems like databases, APIs

  • Service integration requiring authentication

  • Complex data processing pipelines

  • Real-time data access

When to Use Skills

  • Defining team SOPs (Standard Operating Procedures)

  • Applying code style guides

  • Checklists for specific tasks

  • Domain-specific rules

When to Use Both

Most real projects use both together.

# Example: Jira integration

# 1. Connect to Jira API via MCP
claude mcp add jira "https://..."

# 2. Define usage rules via Skills
# SKILL.md
---
name: jira-workflow
---

## Jira Ticket Processing Rules

- Process bugs in order: Critical โ†’ High โ†’ Medium
- Comment required when changing ticket status
- QA review required before completion

2026: MCP UI Framework

In January 2026, Anthropic released the MCP UI Framework.

Where MCP previously only exchanged text, it can now provide rich UI components.

For example, a Jira MCP server can render a mini dashboard inside the Claude chat window instead of just showing ticket info as text.

Changing ticket status with a button click is now possible.

This has made the boundary between Skills and MCP clearer.

  • Skills: Procedural knowledge (methodology)

  • MCP: Connection + UI (infrastructure)

Agent Skills: Open Standard

In December 2025, Anthropic released Agent Skills as an open standard.

Now Skills aren't just for Claude.

Supported Platforms

  • Claude Code

  • OpenAI Codex

  • Gemini CLI

  • Cursor

  • VS Code

  • GitHub Copilot

Skills files written once can be reused across multiple AI platforms.

Summary

MCP Skills
Role Connectivity Methodology
Format Server + JSON Schema Markdown file
Complexity High Low
Use Case External system integration Task rule definition
Standardization Linux Foundation AAIF Agent Skills Open Spec

MCP and Skills are not competing but complementary.

Use MCP to access tools, use Skills to define how to use them.

To properly leverage AI agents, you need to know both.

References

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