/mcp-code-execution

name: mcp-code-execution
description: Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.
alwaysApply: false
progressive_loading: true
dependencies:
  hub:
  - context-optimization
  - token-conservation
  modules:
  - mcp-subagents
  - mcp-patterns
  - mcp-validation

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:

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 Progress 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 module results into a single status dict."""

    return {
        "status": "completed",
        "token_savings": calculate_savings(module_results),
        "mecw_compliance": verify_mec