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name: mcp-code-execution description: | Transform tool-heavy workflows into MCP code execution patterns for token savings and optimized processing.

Triggers: MCP, code execution, tool chain, data pipeline, tool transformation, batch processing, workflow optimization

Use when: >3 tools chained sequentially, large datasets (>10k rows), large files (>50KB), context usage >25%

DO NOT use when: simple tool calls that don't chain. DO NOT use when: context pressure is low and tools are fast.

Use this skill BEFORE building complex tool chains. Optimize proactively. location: plugin token_budget: 200 progressive_loading: true dependencies: hub: [context-optimization, token-conservation] modules: [mcp-subagents, mcp-patterns, mcp-validation]

MCP Code Execution Hub

Quick Start

Use specialized MCP modules for specific tasks:

• mcp-subagents for workflow decomposition
• mcp-patterns for transformation templates
• mcp-validation for hallucination prevention

When to Use

• Automatic: Keywords: code execution, MCP, tool chain, data pipeline, MECW
• Tool Chains: >3 tools chained sequentially
• Data Processing: Large datasets (>10k rows) or files (>50KB)
• Context Pressure: Current usage >25% of total window (proactive context management)

Core Hub Responsibilities

• Orchestrates MCP code execution workflow
• Routes to appropriate specialized modules
• Coordinates MECW compliance across submodules
• Manages token budget allocation for submodules

Required TodoWrite Items

1. mcp-code-execution:assess-workflow
2. mcp-code-execution:route-to-modules
3. mcp-code-execution:coordinate-mecw
4. mcp-code-execution:synthesize-results

Step 1 – Assess Workflow (mcp-code-execution:assess-workflow)

Workflow Classification

def classify_workflow_for_mecw(workflow):
    """Determine appropriate MCP modules and MECW strategy"""

    if has_tool_chains(workflow) and workflow.complexity == 'high':
        return {
            'modules': ['mcp-subagents', 'mcp-patterns'],
            'mecw_strategy': 'aggressive',
            'token_budget': 600
        }
    elif workflow.data_size > '10k_rows':
        return {
            'modules': ['mcp-patterns', 'mcp-validation'],
            'mecw_strategy': 'moderate',
            'token_budget': 400
        }
    else:
        return {
            'modules': ['mcp-patterns'],
            'mecw_strategy': 'conservative',
            'token_budget': 200
        }

MECW Risk Assessment

Delegate to mcp-validation module for detailed risk analysis:

def delegate_mecw_assessment(workflow):
    return mcp_validation_assess_mecw_risk(
        workflow,
        hub_allocated_tokens=self.token_budget * 0.5
    )

Step 2 – Route to Modules (mcp-code-execution:route-to-modules)

Module Orchestration

class MCPExecutionHub:
    def __init__(self):
        self.modules = {
            'mcp-subagents': MCPSubagentsModule(),
            'mcp-patterns': MCPatternsModule(),
            'mcp-validation': MCPValidationModule()
        }

    def execute_workflow(self, workflow, classification):
        results = []

        # Execute modules in optimal order
        for module_name in classification['modules']:
            module = self.modules[module_name]
            result = module.execute(
                workflow,
                mecw_budget=classification['token_budget'] //
                len(classification['modules'])
            )
            results.append(result)

        return self.synthesize_results(results)

Step 3 – Coordinate MECW (mcp-code-execution:coordinate-mecw)

Cross-Module MECW Management

• Monitor total context usage across all modules
• Enforce 50% context rule globally
• Coordinate external state management
• Implement MECW emergency protocols

Step 4 – Synthesize Results (mcp-code-execution:synthesize-results)

Result Integration

def synthesize_module_results(module_results):
    """Combine results from MCP modules into structured output"""

    return {
        'status': 'completed',
        'token_savings': calculate_savings(module_results),
        'mecw_compliance': verify_mecw_rules(module_results),
        'hallucination_risk': assess_hallucination_prevention(module_results),
        'results': consolidate_results(module_results)
    }

Module Integration

With Context Optimization Hub

• Receives high-level MECW strategy from context-optimization
• Returns detailed execution metrics and compliance data
• Coordinates token budget allocation

Performance Skills Integration

• uses python-performance-optimization through mcp-patterns
• Aligns with cpu-gpu-performance for resource-aware execution
• validates optimizations maintain MECW compliance

Emergency Protocols

Hub-Level Emergency Response

When MECW limits exceeded:

1. Delegates immediately to mcp-validation for risk assessment
2. Route to mcp-subagents for further decomposition
3. Apply compression through mcp-patterns
4. Return minimal summary to preserve context

Success Metrics

• Workflow Success Rate: >95% successful module coordination
• MECW Compliance: 100% adherence to 50% context rule
• Token Efficiency: Maintain >80% savings vs traditional methods
• Module Coordination: <5% overhead for hub orchestration