Understanding MCP Servers (Part 2): MCP Servers vs Claude Skills

By Matt Boyd 28 January 2026
Understanding MCP Servers (Part 2): MCP Servers vs Claude Skills

Introduction

In Part 1 of this series, we explored what MCP servers are and how they enable AI agents to connect to external systems. Now, a common question arises: “How do MCP servers differ from Claude Skills?”

Both MCP servers and Claude Skills are tools for extending AI capabilities. Understanding when to use each and how they complement each other, is key to building effective AI-assisted workflows.

Series Navigation:

  • Part 1: Introduction to MCP Servers
  • Part 2: MCP Servers vs Claude Skills (this post)
  • Part 3: Setting Up MCP Servers in VS Code
  • Part 4: MCP Servers for Infrastructure as Code
  • Part 5: Best Practices and Security Considerations

Claude Skills: Teaching AI How

Claude Skills are self-contained, task-specific modules that encode procedures, workflows, or knowledge for the Claude AI. Think of them as instruction manuals or operating procedures that teach Claude how to perform specific tasks according to your standards and practices.

The concept of skills has been adopted by other coding agent providers into an open standard. Claude Code uses a .claude directory with SKILL.md files for reusable skills. GitHub Copilot uses either .github/skills for project skills or ~/.copilot/skills for personal skills, which is in addition to the .github/copilot-instructions.md file.

Structure:

  • Folder with a SKILL.md file that includes metadata and instructions
  • Optional scripts, templates, or resources

Purpose:

  • Encode business procedures and workflows
  • Define formatting standards and conventions
  • Provide domain-specific knowledge
  • Teach best practices and methodologies

Example Use Cases:

  • “How to format infrastructure documentation for our team”
  • “The process for creating a new Azure resource following company standards”
  • “Code review checklist specific to our Bicep templates”
  • “Naming conventions for Azure resources”

MCP Servers: Providing AI What

MCP servers provide connectivity and access to external systems. They don’t teach the AI how to use tools; they simply make tools and data available.

Structure:

  • Server process implementing MCP protocol
  • Exposed tools with JSON Schema definitions
  • Authentication and authorization mechanisms
  • Connection to external systems

Purpose:

  • Access real-time data from external sources
  • Execute actions in third-party systems
  • Query databases or APIs
  • Integrate with development tools

Example Use Cases:

  • “Retrieve the latest Bicep schema for Azure Container Apps”
  • “Query Azure subscription for existing resources”
  • “Fetch recent deployment history from Azure DevOps”
  • “Access Azure Policy definitions”

The Key Difference

The fundamental distinction is:

  • Claude Skills teach methodology: “Here’s HOW to do something according to our standards”
  • MCP Servers provide capability: “Here’s WHAT data and tools you can access”

How They Work Together

The real power emerges when you combine MCP servers with Claude Skills. Together, they create a comprehensive AI-assisted workflow that’s both knowledgeable and capable.

Token Efficiency Considerations

Claude Skills Are Token-Efficient:

  • Content is local and loaded into context as needed
  • No API calls or external requests required
  • Minimal context window usage for static knowledge

MCP Servers Have Moderate Token Cost:

  • Tool schemas loaded upfront
  • Actual data fetched on demand
  • Results consume context window
  • Caching can improve efficiency

Best Practice: Use Skills for static knowledge (standards, procedures) and MCP servers for dynamic data (schemas, configurations). This balances capability with efficiency.

Practical Recommendations

1. Start with Skills for Foundations

Document your core standards, procedures, and workflows as Claude Skills first. These form the “how” foundation.

2. Add MCP for Dynamic Data

Integrate MCP servers for data that changes frequently or requires external access.

3. Create Reference Documents

Maintain a mapping that shows which Skills use which MCP tools

4. Version Control Both

  • Skills: Version control the skill files in your repository
  • MCP Configuration: Version control MCP server configurations
  • Keep them in sync with your infrastructure code

Key Takeaways

  • Skills teach methodology; MCP servers provide capability
  • Skills are token-efficient and Claude-specific
  • MCP servers are cross-platform and provide real-time access
  • Together they create comprehensive AI workflows
  • Use Skills for static knowledge, MCP for dynamic data
  • The synergy of both delivers the best results

Further Resources