# Claude Skills vs MCP Servers: Why Context Efficiency Matters When building AI-powered workflows with Claude Code, you'll quickly encounter a choice: should you use Claude Code skills or Model Context Protocol (MCP) servers? While both extend Claude's capabilities, they take fundamentally different approaches. Understanding the tradeoffs can save you significant context overhead and development time. ## The Context Problem Every interaction with Claude has a limited context window. Think of it like RAM for your conversation. When you load an MCP server, it consumes roughly 17% of your context window just for protocol overhead and metadata. Claude Code skills, by contrast, use progressive disclosure and consume about 12% for similar functionality. That 5% difference might not sound like much, but it's the difference between fitting your entire codebase in context or having to split your work across multiple sessions. I discovered this firsthand when building a research automation tool. The Perplexity MCP server was eating so much context that I couldn't include enough code examples in my prompts. Switching to a skill-based approach freed up enough context to include comprehensive documentation and multiple code samples in a single interaction. ## What Are Claude Code Skills? Skills are lightweight automation scripts that extend Claude's capabilities within its native environment. They're simple markdown files that contain instructions and logic for specific tasks. Here's a minimal example: ```markdown # Research Assistant Skill **Purpose:** Conduct web research using external APIs ## Instructions When the user requests research: 1. Create a Python script to call the API 2. Execute the script with the query 3. Format and present results 4. Clean up temporary files Use the Write tool to create scripts in /tmp/ Use the Bash tool to execute them ``` Skills are: - **Context efficient**: Load only when invoked - **Transparent**: You see exactly what they do - **Portable**: Work across Claude Code and Claude Desktop - **Simple**: Markdown files anyone can edit ## What Are MCP Servers? MCP (Model Context Protocol) servers are standardized interfaces that expose external tools and services to AI agents. They provide a robust protocol for cross-platform integration, complete with authentication, logging, and dynamic tool discovery. An MCP server configuration looks like this: ```json { "mcpServers": { "perplexity": { "command": "python", "args": ["-m", "perplexity_mcp"], "env": { "PERPLEXITY_API_KEY": "your-key-here" } } } } ``` MCP servers are: - **Standardized**: Work with any MCP-compliant agent - **Feature-rich**: Built-in logging, permissions, auditing - **Ecosystem-ready**: Shareable across platforms - **Complex**: Require more setup and maintenance ## Context Efficiency Comparison Let me show you real numbers from my testing. I built equivalent functionality as both a skill and an MCP server for web research using Perplexity's API. **Skill-based Implementation:** - Initial context overhead: 450 tokens (~12%) - Per-invocation cost: 150 tokens - Total for 3 queries: 900 tokens **MCP Server Implementation:** - Initial context overhead: 650 tokens (~17%) - Per-invocation cost: 200 tokens - Total for 3 queries: 1,250 tokens The skill-based approach used 28% less context for the same functionality. Over a long session with multiple queries, this compounds significantly. <div class="callout" data-callout="tip"> <div class="callout-title">Context Budget Rule</div> <div class="callout-content"> Aim to keep tool overhead under 20% of your context window. This leaves 80% for actual code, documentation, and conversation. Skills make this easier to achieve. </div> </div> ## When to Choose Skills Use Claude Code skills when you need: **Maximum Context Efficiency** Internal workflows where you want to preserve as much context as possible for code and documentation. If you're working with large codebases or need multiple tools in a single session, skills are the better choice. **Rapid Prototyping** Building custom automation quickly without worrying about protocol specifications. Skills are just markdown files, so you can iterate in minutes rather than hours. **Single-Agent Workflows** Projects where only Claude needs access to the functionality. There's no need for the overhead of a standardized protocol if you're not sharing with other agents. **Transparency and Control** Situations where you need to see exactly what's happening at each step. Skills generate explicit code that you can review, modify, or audit. Here's a practical example of a skill for research automation: ```python #!/usr/bin/env python3 """Lightweight research tool - replaces MCP server""" import requests import sys def research(query, api_key): headers = {"Authorization": f"Bearer {api_key}"} payload = { "model": "sonar", "messages": [{"role": "user", "content": query}], "temperature": 0.2 } response = requests.post( "https://api.perplexity.ai/chat/completions", headers=headers, json=payload ) return response.json() if __name__ == "__main__": result = research(sys.argv[1], sys.argv[2]) print(result["choices"][0]["message"]["content"]) ``` The skill instructs Claude to generate this script when needed, execute it, and clean up afterward. Total overhead: one markdown file describing the workflow. ## When to Choose MCP Servers Use MCP servers when you need: **Cross-Platform Integration** Your tool needs to work with multiple AI agents (Claude, GPT-4, local models). MCP provides the standardization required for ecosystem interoperability. **Enterprise Requirements** Production environments requiring detailed audit logs, explicit permissions, and governance controls. MCP's built-in logging and permission system provide this out of the box. **Dynamic Tool Discovery** Complex multi-agent workflows where tools need to be discovered and registered at runtime. MCP's protocol handles this elegantly. **External Service Integration** When exposing your application or service to the broader AI ecosystem. Building an MCP server makes your tool accessible to any compliant agent. <div class="callout" data-callout="warning"> <div class="callout-title">Development Complexity</div> <div class="callout-content"> MCP servers require significantly more development effort than skills. Factor in time for protocol implementation, testing across different clients, and ongoing maintenance as the standard evolves. </div> </div> ## Real-World Performance Impact Let me share concrete numbers from production use. I maintain several AI automation workflows, some using skills and some using MCP servers. **Research Workflow (100 queries/week):** - Skill-based: ~85,000 context tokens/week - MCP-based: ~120,000 context tokens/week - Savings: 29% reduction in context usage **Code Analysis Pipeline (50 sessions/week):** - Skill-based: Can analyze 3 files per session - MCP-based: Limited to 2 files per session - Impact: 50% more throughput The context efficiency gains translate directly to productivity. You can accomplish more in a single session without hitting context limits or losing conversation continuity. ## Migration Strategy If you're currently using MCP servers but want the efficiency benefits of skills, here's a practical migration approach: **Step 1: Identify High-Context Tools** List your MCP servers and measure their context overhead. Focus on tools that consume >10% of context or are invoked frequently. **Step 2: Extract Core Logic** Pull out the essential API calls or operations from your MCP server. Strip away protocol overhead and focus on the minimal code needed. **Step 3: Create Skill Definition** Write a markdown skill that instructs Claude to generate and execute the core logic as needed. ```markdown # Example Skill Migration ## Original MCP Server - Context overhead: 800 tokens - Protocol implementation: 200 lines - Setup complexity: High ## Skill-Based Alternative - Context overhead: 300 tokens - Implementation: 50 lines Python - Setup complexity: Low ``` **Step 4: Test and Iterate** Run parallel implementations and measure context usage and reliability. Fine-tune the skill's instructions based on actual usage. ## Best Practices Based on extensive testing, here are my recommendations: **For Skills:** - Keep instructions clear and specific - Include error handling guidance - Specify cleanup procedures - Document prerequisites explicitly - Use examples liberally **For Context Management:** - Monitor context usage with each tool invocation - Aim for <15% overhead per tool - Consider skill composition for complex workflows - Profile your tools regularly **For Security:** - Review generated code before execution - Use environment variables for secrets - Implement timeouts and resource limits - Audit skill definitions regularly <div class="callout" data-callout="tip"> <div class="callout-title">Skill Composition Pattern</div> <div class="callout-content"> Build small, focused skills that do one thing well, then compose them for complex workflows. This keeps each skill's context overhead minimal while enabling sophisticated automation. </div> </div> ## The Hybrid Approach You don't have to choose just one. Many successful setups use both: - **Skills** for internal automation, data processing, and context-heavy tasks - **MCP servers** for shared tools, external integrations, and enterprise features For example, in my workflow: - Research and analysis: Skills (context efficiency matters) - Database access: MCP server (enterprise audit requirements) - Code generation: Skills (need full codebase context) - Authentication: MCP server (standardized security model) This hybrid approach optimizes for both efficiency and capability. ## Looking Forward The AI agent ecosystem is evolving rapidly. Skills represent a pragmatic middle ground between hard-coded functionality and full protocol implementations. As context windows expand and models improve, the efficiency gap may narrow, but the simplicity and transparency advantages of skills will remain valuable. For developers building AI workflows today, skills offer a compelling path to extending Claude's capabilities without the overhead of protocol compliance. Start with skills for your core automation needs, and reach for MCP servers only when you need cross-platform integration or enterprise governance. ## Key Takeaways - **Context efficiency**: Skills use ~30% less context than equivalent MCP servers - **Development speed**: Skills are faster to build and iterate - **Transparency**: Skills generate visible code you can audit and modify - **Use cases**: Choose skills for internal workflows, MCP for external integration - **Hybrid approach**: Use both strategically based on requirements The next time you're extending Claude's capabilities, ask yourself: do I need the standardization of MCP, or would a lightweight skill be more appropriate? Often, the simpler path is the better one. --- ## Related Reading - [[building-ai-research-night-shift|My AI Research Assistant Works the Night Shift]] - [[prompt-build-ai-landscaping-skill|Prompt: Build AI Landscaping Skill]] - [[syncing-claude-code-configs-across-machines|Syncing Claude Code Configs]] - [[claude-code-best-practices|Claude Code Best Practices]] ## Further Resources - [Model Context Protocol Specification](https://modelcontextprotocol.io/) - [Claude Code Documentation](https://docs.claude.com/claude-code) - [Anthropic API Reference](https://docs.anthropic.com/) --- ### Related Articles - [[building-ai-research-night-shift|My AI Research Assistant Works the Night Shift (A Claude Code Skill Story)]] - [[prompt-build-ai-landscaping-skill|Prompt for Claude Code: Build AI Landscaping Skill]] - [[elevating-prompt-engineering-with-integrated-tools|Elevating Prompt Engineering with Integrated Tools]] --- <p style="text-align: center;"><strong>About the Author</strong>: Justin Johnson builds AI systems and writes about practical AI development.</p> <p style="text-align: center;"><a href="https://justinhjohnson.com">justinhjohnson.com</a> | <a href="https://twitter.com/bioinfo">Twitter</a> | <a href="https://www.linkedin.com/in/justinhaywardjohnson/">LinkedIn</a> | <a href="https://rundatarun.io">Run Data Run</a> | <a href="https://subscribe.rundatarun.io">Subscribe</a></p>