MCP-to-Skill Converter Skill

Identity

MCP-to-Skill Converter - Transforms MCP servers into Claude Skills using progressive disclosure to achieve 90%+ context savings while maintaining full functionality.

Capabilities

• MCP Server Introspection: Analyze MCP servers to discover tools and capabilities
• Skill Generation: Generate complete Skill structure (SKILL.md, executor.py, config)
• Progressive Disclosure: Create Skills with metadata-only loading (~100 tokens)
• Automatic Detection: Monitor and detect new MCP servers for conversion
• Validation: Validate generated Skills before installation
• Installation: Install converted Skills to user's Skills directory

The Problem

MCP servers load all tool definitions into context at startup:

• 20+ tools = 30-50k tokens consumed immediately
• Context fills before Claude does any work
• Hard to scale beyond ~100 tools
• Most tools unused in each conversation

The Solution

Convert MCP servers to Skills with progressive disclosure:

• Startup: ~100 tokens (metadata only)
• When used: ~5k tokens (full instructions)
• Executing: 0 tokens (runs externally)
• Savings: 90%+ context reduction

How It Works

1. Introspection: Connect to MCP server and discover all tools
2. Analysis: Calculate token usage and conversion eligibility
3. Generation: Create Skill structure with progressive disclosure
4. Validation: Verify Skill structure and functionality
5. Installation: Install to ~/.claude/skills/

Usage Patterns

On-Demand Conversion

When to Use:

• You have an MCP server with 10+ tools
• Context space is critical
• Most tools aren't used in every conversation
• You want maximum context efficiency

How to Invoke:

"Convert the github MCP server to a Skill"
"Convert all MCP servers with more than 10 tools"
"Convert the custom-server MCP to a Skill"

What It Does:

• Reads MCP server configuration
• Introspects server to discover tools
• Generates Skill structure
• Validates and installs Skill

Automatic Detection

When Enabled:

• Monitor .claude/.mcp.json for changes
• Detect new MCP servers
• Analyze tool count and token usage
• Auto-convert based on rules

Configuration:

auto_convert:
  enabled: true
  threshold:
    tool_count: 10
    estimated_tokens: 5000
  exceptions:
    - github  # Keep as MCP
    - memory  # Keep as MCP

Skill Structure

Generated Skills follow this structure:

skill-name/
├── SKILL.md          # Metadata and instructions (~100 tokens)
├── executor.py       # Dynamic MCP tool execution
└── config.json       # MCP server configuration

SKILL.md (Progressive Disclosure)

Metadata Only (~100 tokens):

• Skill name and description
• Tool categories
• When to use guidance
• Quick reference

Full Instructions (~5k tokens, loaded when used):

• Complete tool documentation
• Usage examples
• Error handling
• Best practices

executor.py

Handles MCP tool calls dynamically:

• Connects to MCP server
• Executes tool calls
• Returns results
• Handles errors

Integration

With Tool Search

Skills work alongside Tool Search:

• Tool Search: Semantic discovery of tools
• Skills: On-demand tool loading with minimal context
• Combined: Optimal context usage for large tool libraries

With Skill Builder

Uses Skill Builder plugin for:

• Validation of generated Skills
• Template-based generation
• Testing and verification
• Installation management

With Marketplace

Integrates with superpowers-marketplace:

• Install marketplace plugins
• Auto-detect MCP servers in plugins
• Convert plugin MCP servers to Skills
• Manage plugin ecosystem

Best Practices

When to Convert

Convert to Skill When:

• MCP server has 10+ tools
• Most tools unused in each conversation
• Context space is critical
• Tools are independent

Keep as MCP When:

• 1-5 tools (minimal context impact)
• Complex OAuth flows required
• Persistent connections needed
• Cross-platform compatibility critical

Critical Tools to Keep as MCP

Keep these as MCP (always loaded):

• Core file operations: read_file, write_file, search_code
• Essential integrations: create_pull_request, get_issue
• Frequently used: take_screenshot, navigate_page

Hybrid Approach

Best Strategy: Use both MCP and Skills

• MCP: Core tools (1-5 tools, always loaded)
• Skills: Extended toolset (10+ tools, on-demand)
• Tool Search: Discovery and semantic matching

Examples

Example 1: Convert GitHub MCP

# On-demand conversion
"Convert the github MCP server to a Skill"

# Result: Creates ~/.claude/skills/github/
# - SKILL.md (100 tokens metadata)
# - executor.py (dynamic tool calls)
# - config.json (MCP configuration)

Example 2: Batch Conversion

# Convert multiple MCP servers
"Convert all MCP servers with more than 10 tools to Skills"

# Analyzes all MCP servers
# Converts eligible servers
# Installs all generated Skills

Example 3: Automatic Detection

# .claude/skills/mcp-converter/conversion_rules.yaml
auto_convert:
  enabled: true
  threshold:
    tool_count: 10
  exceptions:
    - github
    - memory

Error Handling

Common Issues:

• MCP server not responding: Check configuration and environment variables
• Tool introspection fails: Verify MCP server is accessible
• Skill generation errors: Check templates and validation
• Installation fails: Verify Skills directory permissions

Recovery:

• Retry with verbose logging
• Validate MCP configuration
• Check Skill Builder integration
• Review conversion rules

Context Savings

Before (MCP):

20 tools = 30k tokens always loaded
Context available: 170k / 200k = 85%

After (Skills):

20 skills = 2k tokens metadata
When 1 skill active: 7k tokens
Context available: 193k / 200k = 96.5%

Dependencies

• mcp Python package (for MCP server introspection)
• Skill Builder plugin (for validation)
• Existing MCP configuration (.claude/.mcp.json)

Related Skills

• tool-search: Semantic tool discovery
• marketplace-manager: Plugin installation and management
• memory-manager: Context persistence