Claude Skills vs. MCP: The Complete Guide to Extending Claude AI (2025)

Introduction

As AI assistants become more powerful, the challenge isn't just about model capabilities—it's about connecting these models to the real world. How do you give Claude AI access to your company's data? How do you teach it your specific workflows? How do you make it understand your unique business processes?

Anthropic has introduced two distinct approaches to solve these problems: Model Context Protocol (MCP) launched in November 2024, and Claude Skills introduced in October 2025. While both extend Claude's capabilities, they solve fundamentally different problems.

Understanding which approach to use—or when to use both—can dramatically improve how you work with Claude AI. This guide will walk you through everything you need to know.

What is Model Context Protocol (MCP)?

Model Context Protocol is an open standard that connects AI applications to external systems. Think of it as USB-C for AI—a universal connector that lets any AI model access data sources, tools, and services.

How MCP Works

MCP operates through a client-server architecture with three key components:

1. MCP Clients (Hosts)
These are AI applications like Claude Desktop or custom implementations that want to access external data.

2. MCP Servers
These expose data, tools, and capabilities through a standardized interface. Pre-built servers exist for popular services like Google Drive, Slack, GitHub, and Postgres.

3. Transport Layer
Communication happens via JSON-RPC over stdio (for local processes) or HTTP with Server-Sent Events (for remote connections).

The Three Core Primitives

MCP provides three types of capabilities:

Tools: Functions the AI can call (APIs, database queries, actions)
Resources: Data sources the application controls (files, database records)
Prompts: Reusable templates users can invoke

Key Features of MCP

Vendor Neutrality: MCP isn't limited to Claude—any AI model can use MCP servers, making it a true industry standard.

Persistent Connections: MCP maintains ongoing connections to external services, enabling real-time data access.

Ecosystem: A growing library of pre-built servers means you can connect to popular tools without writing integration code.

Multiple Language Support: SDKs available in Python, TypeScript, Java, and Kotlin make implementation straightforward for most development teams.

When MCP Shines

MCP excels when you need:

Real-time access to external databases
Integration with business tools (CRM, project management, communication platforms)
Multi-tool workflows that span several services
Enterprise-wide data access patterns
Vendor-neutral solutions that work across AI platforms

The Challenge with MCP

While powerful, MCP comes with complexity. The GitHub MCP server, for example, can consume tens of thousands of tokens just in its initial context. As you add multiple MCP connections, your available context window shrinks rapidly, leaving less room for actual work.

Learn more about available MCP servers.

What are Claude Skills?

Claude Skills represent a radically different approach. Instead of connecting to external systems, Skills teach Claude how to perform specific tasks through organized instructions and resources.

The Core Concept

A Skill is essentially a folder containing:

A SKILL.md file with YAML metadata and Markdown instructions
Optional scripts that Claude can execute
Supporting resources (templates, examples, data files)

Think of Skills as onboarding materials for a new team member—they package your expertise into a format Claude can understand and apply.

How Skills Work: Progressive Disclosure

Skills use an elegant design principle called progressive disclosure:

Step 1: At startup, Claude scans all available Skills and loads only their metadata (name and description) into its system prompt. This takes just a few dozen tokens per Skill.

Step 2: When you give Claude a task, it reviews the metadata to determine which Skills are relevant.

Step 3: Only if a Skill matches the task does Claude load its full content—and only the specific parts it needs.

This means you can have an effectively unlimited library of Skills without overwhelming Claude's context window.

Built-in Anthropic Skills

Claude comes with professionally developed Skills for common tasks:

Microsoft Word: Document creation, editing, and formatting
Microsoft Excel: Spreadsheet generation with formulas and analysis
Microsoft PowerPoint: Presentation creation and editing
PDF: Document manipulation and form filling

These Skills power Claude's document creation capabilities and are automatically invoked when relevant.

Creating Custom Skills

Custom Skills let you encode your organization's specific knowledge:

Brand guidelines and style standards
Email and communication templates
Meeting notes formats
Task creation workflows (Jira, Asana, Linear conventions)
Data analysis procedures
Coding standards and patterns

Creating a Skill is simple—you can even use the built-in "skill-creator" Skill, which guides you through the process interactively.

Key Advantages of Skills

Simplicity: Write instructions in Markdown. Add scripts if needed. Done.

Token Efficiency: Progressive loading means minimal context consumption.

Composability: Multiple Skills work together automatically when needed.

Portability: Skills work across Claude.ai, the Claude API, and Claude Code.

Shareability: Skills are just files and folders—easy to version control and distribute.

Head-to-Head Comparison

Let's break down the key differences:

Architecture & Complexity

MCP: A complete protocol specification with clients, servers, transports, resources, prompts, tools, sampling, and roots. Requires understanding of network protocols and API design.

Skills: Markdown files with YAML frontmatter and optional scripts. If you can write documentation, you can create Skills.

What They Connect

MCP: Connects Claude TO external systems—databases, APIs, cloud services, tools.

Skills: Teaches Claude HOW to perform tasks—procedures, workflows, standards, patterns.

Token Usage

MCP: Can consume thousands to tens of thousands of tokens per server, especially for complex integrations.

Skills: Metadata uses dozens of tokens; full content loads only when needed.

Setup Time

MCP: Requires server configuration, authentication setup, transport configuration, and testing.

Skills: Write instructions, add to Skills folder, Claude auto-detects it.

Maintenance

MCP: Servers need updates when APIs change, authentication needs management, connections require monitoring.

Skills: Update the Markdown file. That's it.

Portability

MCP: Designed as a vendor-neutral standard that works with any AI model.

Skills: Optimized for Claude but technically usable with other models (they're just Markdown files).

Working Together

Here's the crucial insight: MCP and Skills are complementary, not competitive.

MCP gives Claude access to tools
Skills teach Claude how to use those tools effectively

For example:

An MCP server connects Claude to your Jira instance
A Skill teaches Claude your team's ticket creation workflow, priority system, and labeling conventions

When to Use Each Approach

Choose MCP When You Need:

✓ Real-time access to external databases
✓ Integration with existing business tools
✓ A vendor-neutral solution for multiple AI systems
✓ Persistent connections to services
✓ Complex API interactions
✓ Enterprise-wide data access patterns

Choose Skills When You Need:

✓ To teach Claude your specific workflows
✓ Company-specific processes and standards
✓ Document creation with consistent formatting
✓ Simple, fast implementation
✓ Token-efficient solutions
✓ Code execution for specialized tasks
✓ Personal productivity automation

Use Both When You Need:

✓ Complex enterprise workflows
✓ External data access with specific procedures
✓ Comprehensive AI agents
✓ Maximum flexibility and power

Practical Examples

Example 1: MCP for Customer Data Analysis

Scenario: Your support team needs Claude to analyze customer data across multiple systems.

Solution: MCP Server

Connects to your CRM database
Links to support ticket system
Accesses customer communication history
Provides real-time data queries

Result: Claude can answer questions like "What are the top issues reported by enterprise customers this month?" with current data.

Example 2: Skills for Report Generation

Scenario: Your team creates weekly performance reports with specific formatting and analysis.

Solution: Custom Skill

Contains your report template
Includes company branding guidelines
Specifies analysis procedures
Defines chart styles and data visualization standards

Result: Ask Claude to "create the weekly performance report," and it follows your exact format and standards every time.

Example 3: Combined Approach for Customer Support

Scenario: You want an AI assistant that handles customer inquiries with your company's voice and access to customer data.

Solution: MCP + Skills

MCP Server: Connects to Salesforce CRM
MCP Server: Links to your knowledge base
Skill: Teaches your brand voice and tone
Skill: Encodes response protocols
Skill: Defines escalation procedures

Result: Claude accesses real customer data (via MCP) and responds according to your standards (via Skills), creating a powerful, context-aware support agent.

Security and Governance

MCP Security Considerations

MCP servers handle credentials and access sensitive data, requiring careful security practices:

Credential Isolation: Store secrets in MCP servers, not in prompts
Access Controls: Implement allowlists for which servers Claude can access
Audit Logging: Track all MCP server interactions
Least Privilege: Grant minimal necessary permissions
Known Risks: Be aware of prompt injection vulnerabilities and tool permission issues

Skills Security Considerations

Skills can execute code, making trust essential:

Source Trust: Only install Skills from trusted sources
Code Review: Audit scripts before installation
Organizational Curation: Maintain an approved Skills library
Sandboxing: Anthropic runs Skills in a secured environment
User Approval: Configure when Claude needs permission for actions

The key principle: treat Skills as code you're adding to your system and apply appropriate review processes.

Getting Started

Starting with MCP

Install a Client: Set up Claude Desktop or use the API
Choose Pre-built Servers: Browse available MCP servers
Configure Connections: Follow server-specific setup instructions
Test Integration: Verify Claude can access your data

Resources:

Starting with Skills

Check Availability: Skills require Pro, Max, Team, or Enterprise plans
Enable Code Execution: Required for Skills to function
Explore Built-in Skills: Try Anthropic's document creation Skills
Create Your First Skill: Use the built-in skill-creator or follow the cookbook
Test and Iterate: Refine instructions based on Claude's performance

Resources:

The Future of Both Technologies

MCP's Trajectory

The MCP ecosystem is growing rapidly with adoption by major development tools:

Replit, Sourcegraph, and Zed integrating MCP
Block and Apollo implementing MCP in production
Growing library of community-contributed servers
Ongoing work to address token efficiency concerns

Skills Evolution

Anthropic has outlined exciting plans for Skills:

Agent-Created Skills: Future versions may let Claude create and refine its own Skills
Skills Marketplace: Easier discovery and sharing of community Skills
Enhanced Composability: Better coordination between multiple Skills
Broader Platform Support: Integration beyond Claude ecosystem

Industry Perspective

Developer and AI researcher Simon Willison wrote extensively about Skills, suggesting they "might be a bigger deal than MCP." His reasoning: Skills' simplicity and token efficiency make them more accessible and practical for most use cases.

The prediction is a "Cambrian explosion" of Skills that will exceed the MCP adoption rush—precisely because creating a Skill is so much simpler than building an MCP server.

Real-World Decision Framework

Here's a practical decision tree for choosing your approach:

Question 1: Do you need to access data from external systems in real-time?

Yes → Start with MCP
No → Continue to Question 2

Question 2: Are you teaching Claude how to do something specific to your organization?

Yes → Start with Skills
No → Continue to Question 3

Question 3: Do you need both external data AND specific procedures?

Yes → Use both MCP and Skills
No → Consider if you need either

Question 4: Are you building for multiple AI platforms?

Yes → MCP provides better vendor neutrality
No → Skills offer simpler implementation for Claude

Conclusion

MCP and Claude Skills represent two powerful but distinct approaches to extending AI capabilities:

Model Context Protocol connects Claude to the world—giving it access to your data, tools, and services through a standardized, vendor-neutral protocol. It's ideal when you need real-time external data access and enterprise integrations.

Claude Skills teach Claude your way of working—encoding your procedures, standards, and expertise into reusable, composable packages. It's perfect for organizational workflows, consistent outputs, and rapid implementation.

The most powerful implementations use both: MCP provides the data, Skills provide the know-how.

Key Takeaways

MCP = Connecting TO systems (data access layer)
Skills = Teaching HOW to work (procedural knowledge)
They're complementary, not competitive
Start simple: Begin with Skills for most workflows
Add complexity: Introduce MCP when external data access is essential
Think hybrid: The most powerful solutions combine both approaches

Next Steps

Assess your needs: What problems are you trying to solve?
Start with Skills: They're faster to implement and immediately useful
Explore MCP: As your needs grow, add external data connections
Join the community: Share your Skills and MCP servers
Stay updated: Both technologies are evolving rapidly

Ready to extend Claude's capabilities? Explore our comprehensive MCP server directory to find pre-built integrations, or start creating your first Skill today.

Additional Resources:

Last updated: October 19, 2025