Claude Skills vs MCP - What's the Difference
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 coverageIf 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 completion2026: 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