mcp-code-execution

Routes multi-tool workflows through MCP servers for large datasets and pipelines

Night Market Skill — ported from claude-night-market/conserve. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Table of Contents

• Quick Start
• When to Use
• Core Hub Responsibilities
• Required TodoWrite Items
• Step 1 – Assess Workflow
• Workflow Classification
• MECW Risk Assessment
• Step 2 – Route to Modules
• Module Orchestration
• Step 3 – Coordinate MECW
• Cross-Module MECW Management
• Step 4 – Synthesize Results
• Result Integration
• Module Integration
• With Context Optimization Hub
• Performance Skills Integration
• Emergency Protocols
• Hub-Level Emergency Response
• Success Metrics

MCP Code Execution Hub

Quick Start

This skill is an orchestration hub, not a CLI. It activates inside a Claude Code session when one of the trigger keywords below appears, or when invoked explicitly:

text

Skill(conserve:mcp-code-execution)

The hub then routes to the relevant sub-skill modules (mcp-subagents, mcp-patterns, mcp-validation) based on the detected workflow shape. There is no separate install step or CLI entry point.

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)

MCP Tool Search (Claude Code 2.1.7+): When MCP tool descriptions exceed 10% of context, tools are automatically deferred and discovered via MCPSearch instead of being loaded upfront. This reduces token overhead by ~85% but means tools must be discovered on-demand. Haiku models do not support tool search. Configure threshold with ENABLE_TOOL_SEARCH=auto:N where N is the percentage.

Subagent MCP Access Fix (Claude Code 2.1.30+): SDK-provided MCP tools are now properly synced to subagents. Prior to 2.1.30, subagents could not access SDK-provided MCP tools: workflows delegating MCP tool usage to subagents were silently broken. No workarounds needed on 2.1.30+.

Claude.ai MCP Connectors (Claude Code 2.1.46+): Users logged into Claude Code with a claude.ai account may have additional MCP tools auto-loaded from claude.ai/settings/connectors. These tools contribute to the tool search threshold count. If workflows unexpectedly trigger tool search or context inflation, check /mcp for claude.ai-sourced connectors. Known reliability issue: connectors can silently disappear (GitHub #21817).

MCP Prompt Cache Fix (Claude Code 2.1.70+): MCP servers with instructions connecting after the first turn no longer bust the prompt cache. Previously, a late-connecting MCP server would invalidate cached prompt prefixes, increasing token costs for the rest of the session. On 2.1.70+, prompt cache reuse is preserved regardless of when MCP servers connect.

ToolSearch Reliability Fix (Claude Code 2.1.70+): Empty model responses after ToolSearch are fixed. The server was rendering tool schemas with system-prompt-style tags that could confuse models into stopping early. ToolSearch-heavy workflows (many deferred MCP tools) are now more reliable.

When NOT To Use

• Simple tool calls that don't chain
• Context pressure is low and tools are fast

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

python

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:

python

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

python

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

python

def synthesize_module_results(module_results):
    """Combine module results into a single status dict."""

    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

Available Modules

• See modules/mcp-coordination.md for cross-module orchestration
• See modules/mcp-patterns.md for common MCP execution patterns
• See modules/mcp-subagents.md for subagent delegation strategies
• See modules/mcp-validation.md for MECW compliance validation

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