MCP to Skill Converter: Achieving 90% Context Savings Through Progressive Disclosure

The MCP to Skill Converter is a groundbreaking tool that transforms any Model Context Protocol (MCP) server into a Claude Skill while achieving dramatic context token savings. This innovative solution addresses one of the most pressing challenges in AI agent development: the massive context consumption of traditional MCP server implementations.

Overview

The Problem: Context Token Explosion

When you integrate MCP servers into Claude, all tool definitions are loaded immediately at startup, regardless of whether they'll be used. This creates several critical problems:

• Massive Token Overhead: A typical MCP server with 20+ tools consumes 30,000-50,000 tokens just for initialization
• Context Window Waste: Up to 25% of Claude's context capacity is consumed before any actual work begins
• Scaling Limitations: Adding more tools linearly increases the context burden
• Economic Impact: Higher token usage translates to increased API costs
• Performance Degradation: Large context windows slow down response times

The Solution: Progressive Disclosure

The MCP to Skill Converter applies a progressive disclosure pattern that fundamentally reimagines how tools are loaded and exposed to Claude:

Real-World Impact: GitHub MCP Server Example

A practical example demonstrates the dramatic efficiency gains:

Traditional MCP Approach (GitHub server with 8 tools):

• Idle state: 8,000 tokens consumed
• Active state: 8,000 tokens consumed
• Context availability: Significantly reduced

Converted Skill Approach:

• Idle state: 100 tokens (98.75% savings)
• Active state: 5,000 tokens (37.5% savings)
• Context availability: Maximized for actual work

Project Architecture

Core Components

The converter generates a complete Claude Skill structure with three key elements:

Component Breakdown

1. SKILL.md - The Progressive Disclosure Interface

The generated SKILL.md file serves as Claude's primary interface to the skill:

Content Structure:

• Metadata section: Skill name, description, and category
• Tool catalog: Minimal metadata listing available tools
• Usage instructions: How to identify and call tools
• Executor invocation: Commands for dynamic tool loading

Token Efficiency:

• Initial load: ~100 tokens (just the catalog)
• Full load: ~5k tokens (when skill is activated)
• Deferred loading: Detailed tool schemas loaded on-demand

2. executor.py - The Dynamic MCP Bridge

The Python executor script handles all MCP server communication:

Key Functions:

# Three core operations
--list              # Enumerate all available tools
--describe <tool>   # Load only one tool's full schema
--call <json>       # Execute tool with provided arguments

Architecture:

• Async communication: Non-blocking MCP server interaction
• On-demand schema loading: Tool details fetched only when needed
• External execution: Runs outside Claude's context window
• Error handling: Graceful failures with actionable messages

3. mcp_config.json - Server Configuration

Preserves the original MCP server configuration:

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "<YOUR_TOKEN>"
      }
    }
  }
}

Technical Deep Dive

The Conversion Process

How Progressive Disclosure Works

The converter implements a three-tier loading strategy:

Available tools:
- create_issue: Create a new GitHub issue
- search_code: Search for code across repositories
- get_pull_request: Retrieve pull request details

$ python executor.py --describe create_issue
{
  "name": "create_issue",
  "description": "Create a new GitHub issue with title and body",
  "parameters": {
    "repo": {"type": "string", "description": "Repository in format owner/name"},
    "title": {"type": "string", "description": "Issue title"},
    "body": {"type": "string", "description": "Issue body text"}
  }
}

$ python executor.py --call '{"tool": "create_issue", "args": {"repo": "owner/repo", "title": "Bug fix", "body": "Details..."}}'
{
  "success": true,
  "issue_number": 123,
  "url": "https://github.com/owner/repo/issues/123"
}

Key Implementation Patterns

Async MCP Communication

async def _get_mcp_tools(self):
    """
    Start the MCP server process
    Send tools/list request
    Parse and return tool catalog
    """
    # Implementation handles:
    # - Server process management
    # - Async I/O communication
    # - Tool metadata extraction
    # - Error recovery

Deferred Schema Loading

Instead of loading all tool schemas upfront, the executor provides on-demand access:

# Traditional MCP: All schemas loaded immediately
all_tools = load_all_schemas()  # 30-50k tokens

# Converter approach: Load on-demand
tool_schema = load_schema_for(specific_tool)  # 200-500 tokens

Context Separation

The converter separates three concerns to minimize context usage:

1. Discovery Layer (SKILL.md): What tools exist
2. Schema Layer (executor --describe): How to use a specific tool
3. Execution Layer (executor --call): Actually running the tool

This separation ensures Claude only pays the context cost for what it actually uses.

Installation and Usage

Prerequisites

pip install mcp

Basic Workflow

{
  "mcpServers": {
    "my-service": {
      "command": "python",
      "args": ["my_mcp_server.py"],
      "env": {
        "API_KEY": "your-key-here"
      }
    }
  }
}

python mcp_to_skill.py \
  --mcp-config config.json \
  --output-dir ./generated-skills

cd generated-skills/my-service
pip install -r requirements.txt

cp -r generated-skills/my-service ~/.claude/skills/

Advanced Configuration

For complex MCP servers with multiple tools, you can customize the conversion:

python mcp_to_skill.py \
  --mcp-config config.json \
  --output-dir ./skills \
  --skill-name "Custom Name" \
  --description "Custom description" \
  --category "integration"

When to Use MCP to Skill Converter

Ideal Use Cases

When Traditional MCP is Better

Use Traditional MCP When:

• 1-5 tools: Small tool count - the overhead of progressive disclosure isn't justified
• Persistent connections: Tools requiring stateful connections or session management
• Complex OAuth flows: Authentication patterns that need continuous connection
• Real-time data streams: WebSocket or SSE-based tools requiring persistent channels
• Frequent tool switching: Scenarios where most tools are used in every interaction

Comparison: Traditional MCP vs. Converter

Token Consumption Analysis

Performance Characteristics

Traditional MCP:

• ✅ Immediate access to all tool schemas
• ✅ Lower latency for tool switching
• ✅ Better for stateful operations
• ❌ High initial context cost
• ❌ Poor scaling with tool count
• ❌ Wasted context on unused tools

Converted Skill:

• ✅ Minimal initial context cost
• ✅ Excellent scaling with tool count
• ✅ Pay-for-what-you-use pattern
• ✅ Maximizes available context
• ❌ Slight latency for first tool use
• ❌ Additional Python execution overhead

Real-World Applications

Use Case 1: Multi-Service Integration Platform

Scenario: A company wants Claude to access GitHub, Slack, Jira, and PostgreSQL (35+ tools total)

Challenge: Traditional MCP integration would consume 60-80k tokens just for tool definitions, leaving minimal context for actual work.

Solution with Converter:

python mcp_to_skill.py --mcp-config github.json --output-dir skills
python mcp_to_skill.py --mcp-config slack.json --output-dir skills
python mcp_to_skill.py --mcp-config jira.json --output-dir skills
python mcp_to_skill.py --mcp-config postgres.json --output-dir skills

cp -r skills/* ~/.claude/skills/

Outcome: Claude can seamlessly work across all four platforms without context constraints, enabling complex cross-platform workflows like "Create a Jira ticket from this GitHub issue, notify the team in Slack, and update the PostgreSQL tracking table."

Use Case 2: Large-Scale API Integration

Scenario: E-commerce platform with 20+ MCP tools for inventory, orders, customers, shipping, analytics

Traditional Approach Problems:

• 40-50k tokens consumed at startup
• Only 150k tokens available for actual tasks
• Slow response times due to large context
• High API costs from constant token usage

Converter Benefits:

Use Case 3: DevOps Automation

Scenario: Infrastructure management across AWS, Kubernetes, GitHub, monitoring tools (25 tools)

Converter Workflow:

1. Convert all MCP servers to skills for AWS, K8s, GitHub, Datadog
2. Deploy to Claude with ~2,500 tokens idle cost (100 per server)
3. Enable complex workflows like "Deploy this app to K8s, create GitHub release, set up AWS load balancer, configure Datadog monitoring"

Key Advantage: Claude can orchestrate across all platforms without running out of context, because only actively used tools consume significant tokens.

Best Practices

Conversion Strategy

Tool Design Considerations

Optimize Tool Descriptions:

✅ Good: "create_issue: Create a new GitHub issue with title and body"
❌ Poor: "create_issue: A tool for GitHub issue creation"

The description appears in the minimal metadata, so make it count:

• Be specific about what the tool does
• Include key parameters in description
• Use consistent naming conventions
• Group related tools with prefixes (e.g., github_*)

Design for On-Demand Loading:

• Keep tool interfaces simple and focused
• Avoid dependencies between tool schemas
• Ensure each tool can be described independently
• Minimize shared context requirements

Integration Patterns

Multi-Skill Orchestration:

When working with multiple converted skills:

1. Load all skill metadata (~100 tokens each)
2. Claude identifies relevant skills for the task
3. Activate only necessary skills (~5k tokens each)
4. Execute tools from multiple skills
5. Unused skills remain at minimal token cost

Hybrid Approach:

Combine traditional MCP with converted skills:

• Traditional MCP: 1-5 frequently-used core tools
• Converted Skills: Large tool libraries with intermittent usage
• Best of both worlds: Immediate access to essentials + on-demand access to extended capabilities

Common Pitfalls and Troubleshooting

Pitfall 1: Converting Small Tool Sets

Symptom: Marginal or negative performance improvement

Cause: The overhead of progressive disclosure isn't justified for 1-5 tools

Solution: Only convert MCP servers with 10+ tools. Keep small tool sets as traditional MCP.

Pitfall 2: Stateful Tool Issues

Symptom: Tools requiring persistent connections fail or behave unexpectedly

Cause: The executor starts a new MCP server process for each operation

Solution:

• Use traditional MCP for stateful tools
• Or, modify executor to maintain persistent connections
• Or, design tools to be stateless

Pitfall 3: Authentication Complexity

Symptom: OAuth flows or interactive authentication fail

Cause: The executor runs non-interactively and cannot handle complex auth

Solution:

• Use API tokens instead of OAuth for converted skills
• Pre-authenticate and store credentials in config
• Keep OAuth-based tools in traditional MCP

Pitfall 4: Unclear Tool Descriptions

Symptom: Claude doesn't discover or use the right tools

Cause: Minimal metadata doesn't provide enough context for tool selection

Solution:

• Enhance tool descriptions in SKILL.md
• Use consistent naming conventions
• Group related tools with clear prefixes
• Include key use cases in descriptions

Troubleshooting Guide

Issue: Executor script fails to start

# Solution: Check Python dependencies
pip install -r requirements.txt

# Solution: Verify MCP config file
python executor.py --list  # Should show all tools

Issue: Tool execution times out

# Solution: Increase timeout in executor
# Edit executor.py and adjust timeout parameter
TIMEOUT = 60  # Increase from default 30 seconds

Issue: Context savings not as expected

# Solution: Measure actual token usage
# Use Claude's token counter to verify:
# - Idle state usage
# - Active state usage
# - Per-tool loading cost

Advanced Topics

Custom Executor Modifications

The generated executor.py can be customized for specific needs:

Add Caching:

# Cache frequently-used tool schemas
schema_cache = {}

def get_tool_schema(tool_name):
    if tool_name not in schema_cache:
        schema_cache[tool_name] = load_schema(tool_name)
    return schema_cache[tool_name]

Implement Persistent Connections:

# Maintain long-running MCP server connection
class PersistentExecutor:
    def __init__(self):
        self.mcp_server = start_server()

    def execute(self, tool, args):
        return self.mcp_server.call(tool, args)

Add Metrics and Logging:

# Track tool usage for optimization
import logging

def execute_with_metrics(tool, args):
    start_time = time.time()
    result = execute(tool, args)
    duration = time.time() - start_time
    logging.info(f"Tool {tool} executed in {duration}s")
    return result

Integration with Other Skills

The converter works seamlessly with other Claude skills:

With mcp-builder:

1. Use mcp-builder to create a high-quality MCP server
2. Convert it to a skill for production deployment
3. Benefit from both agent-centric design and context efficiency

With skill-creator:

1. Generate custom skills for specific workflows
2. Combine with converted MCP skills
3. Create comprehensive capability sets

With evaluation frameworks:

1. Test converted skills using standard evaluation harnesses
2. Measure context efficiency improvements
3. Optimize tool descriptions based on usage patterns

Future Potential and Development

Project Status

Current Capabilities:

• ✅ Converts standard MCP servers to skills
• ✅ Supports stdio, SSE, and HTTP transports
• ✅ Generates complete skill structure
• ✅ Provides Python executor for dynamic loading
• ✅ Compatible with major MCP servers (GitHub, Slack, filesystem, PostgreSQL)

Areas for Enhancement:

• 🔄 Persistent connection support for stateful tools
• 🔄 Advanced caching strategies
• 🔄 Tool grouping and batch loading
• 🔄 Improved authentication handling
• 🔄 TypeScript executor option
• 🔄 Enhanced metrics and monitoring

Community Contributions

The project welcomes contributions:

• Testing: Try with different MCP servers and report compatibility
• Documentation: Share usage patterns and best practices
• Features: Implement persistent connections, caching, or other optimizations
• Integration: Create integrations with popular MCP servers

Repository: https://github.com/GBSOSS/-mcp-to-skill-converter License: MIT (open source, permissive)

Research Directions

The converter opens interesting research questions:

Conclusion

The MCP to Skill Converter represents a significant advancement in efficient LLM-tool integration. By applying progressive disclosure patterns, it achieves:

✅ Dramatic Token Savings: 90-99% reduction in idle state context consumption ✅ Scalable Architecture: Linear scaling with tool count instead of quadratic growth ✅ Pay-for-What-You-Use: Context cost only when tools are actually needed ✅ Seamless Integration: Works with standard MCP servers without modification ✅ Production Ready: Used with major MCP implementations (GitHub, Slack, PostgreSQL) ✅ Open Source: MIT licensed with active community development

Key Takeaways

When to Use This Tool

Definitely use when you have:

• 10+ tools across one or more MCP servers
• Context constraints limiting Claude's effectiveness
• Intermittent tool usage patterns
• Multiple services that need simultaneous access
• API cost concerns from high token usage

Consider alternatives when you have:

• 1-5 frequently-used tools
• Stateful operations requiring persistent connections
• Complex OAuth or interactive authentication
• Real-time streaming requirements

Next Steps

Ready to optimize your MCP integration?

git clone https://github.com/GBSOSS/-mcp-to-skill-converter
cd -mcp-to-skill-converter

pip install mcp

python mcp_to_skill.py --mcp-config your-config.json --output-dir ./skills

cp -r skills/* ~/.claude/skills/

Related Resources

• MCP to Skill Converter: github.com/GBSOSS/-mcp-to-skill-converter
• Model Context Protocol: modelcontextprotocol.io
• Claude Skills Documentation: docs.claude.com/en/docs/claude-skills
• MCP Builder Skill: Analyzing mcp-builder
• Claude Skills vs MCP Comparison: Simon Willison's Blog

Summary

This comprehensive analysis covered:

• ✅ The context token explosion problem with traditional MCP integration
• ✅ Progressive disclosure architecture and 3-tier loading strategy
• ✅ Real-world impact: 90-99% token savings with GitHub MCP example
• ✅ Complete conversion process from MCP server to Claude Skill
• ✅ Executor implementation with async MCP communication
• ✅ Installation and deployment workflow
• ✅ When to use converter vs. traditional MCP
• ✅ Detailed comparison: token consumption and performance characteristics
• ✅ Real-world applications across multiple industries
• ✅ Best practices for conversion strategy and tool design
• ✅ Common pitfalls and comprehensive troubleshooting guide
• ✅ Advanced topics: custom executors, caching, persistent connections
• ✅ Future development directions and community contributions

ℹ️ Source Information

Original Project: MCP to Skill Converter

• Author: GBSOSS
• Repository: github.com/GBSOSS/-mcp-to-skill-converter
• Created: October 26, 2025
• Last Updated: November 17, 2025
• Stars: 72 | Forks: 8
• License: MIT License
• Language: Python
• Accessed: 2025-11-18

This article was generated through comprehensive analysis of the project repository, documentation, and real-world usage patterns. The converter represents a practical solution to a pressing problem in AI agent development: efficient tool integration at scale.

Ready to slash your context consumption? Try the MCP to Skill Converter today and experience the power of progressive disclosure in your Claude workflows.