---
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
model_hint: standard
---

# 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 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

## Exit Criteria

- [ ] Workflow classified into one of the three MECW strategies
  (aggressive/moderate/conservative) with the correct module
  roster (`mcp-subagents`, `mcp-patterns`, `mcp-validation`)
  selected based on tool-chain length and data size
- [ ] Context usage remains at or below 50% of the total window
  throughout the workflow; any breach triggers the hub-level
  emergency response (delegate to mcp-validation, route to
  mcp-subagents, apply compression)
- [ ] `synthesize_module_results` returns a dict with all four
  keys: `status`, `token_savings`, `mecw_compliance`,
  `hallucination_risk`
- [ ] Token savings reported at the end of the workflow are
  greater than 80% compared to running the same workflow via
  direct Bash tool chaining