MCP Git Worktree Workflows

An MCP (Model Context Protocol) server that implements automated parallel development using git worktrees.

🚀 Key Features

• Voting Workflow: Intelligently ranks and selects the highest-quality implementation

• Ad Hoc Workflow: Quick single-worktree creation for simple tasks

• Orchestration Workflow: Break complex tasks into subtasks with coordinated execution

• Clean Workflow: Automatic cleanup of unsuccessful variants

Related MCP server: GitHub MCP Server

Installation

1. Install dependencies:

uv add fastmcp  # or pip install fastmcp

2. Add the server to Claude Code:

Option A: Using Claude Code CLI (Recommended)

# For project-specific use
claude mcp add worktree-workflows python /path/to/mcp-servers/worktree_workflows.py

# For global use across all projects
claude mcp add --scope user worktree-workflows python /path/to/mcp-servers/worktree_workflows.py

Option B: Manual Configuration

Add to your MCP configuration file:

{
  "mcpServers": {
    "worktree-workflows": {
      "command": "python",
      "args": ["/path/to/mcp-servers/worktree_workflows.py"]
    }
  }
}

3. Restart Claude Code or use /mcp command to reconnect

🔄 Workflow Options

1. Voting Pattern (Multiple Implementations)

create_voting_worktrees(
  task="Rewrite the README.md with better examples",
  num_variants=5,
  target_repo="lombardi"  # Optional: specify repository
)

2. Ad Hoc Single Worktree

create_adhoc_worktree(
  task="Fix the login bug in auth.js",
  target_repo="my-app"  # Optional
)

3. Orchestrated Subtasks

create_orchestrated_worktrees(
  task="Implement user authentication system",
  subtasks=[
    "Create database models for users and sessions",
    "Build authentication API endpoints",
    "Implement JWT token generation and validation",
    "Create login/logout UI components",
    "Add authentication middleware and route protection"
  ],
  target_repo="my-app"  # Optional
)

📊 Evaluation Metrics

The system automatically evaluates implementations using:

• Code Changes (30 points): Has meaningful modifications

• Test Success (50 points): Tests pass successfully

• File Impact (up to 20 points): Number of files modified

• Quality Heuristics: Additional scoring based on implementation patterns

🛠️ Available Tools

Core Voting Workflow

• create_voting_worktrees: Creates worktrees and starts automated execution

• list_sessions: Monitor all active sessions and their progress

• evaluate_implementations: Get detailed ranking and evaluation of all variants

• auto_select_best: Automatically choose and finalize the best implementation

• finalize_best: Manually select a specific implementation

• cleanup_session: Force cleanup of sessions

Additional Workflows

• create_adhoc_worktree: Single worktree for quick tasks

• create_orchestrated_worktrees: Multiple worktrees for coordinated subtasks

Utility Functions

• get_worktree_info: Inspect specific worktree details

• mark_implementation_complete: Manually mark implementations as done

💡 Example Use Cases

Voting Pattern

Perfect for:

• Architecture Exploration: Try different design patterns simultaneously

• Library Comparison: Implement with various frameworks/libraries

• Algorithm Optimization: Test multiple approaches to performance problems

• UI/UX Variants: Create different interface implementations

Ad Hoc Tasks

Great for:

• Bug Fixes: Quick isolation and resolution

• Small Features: Rapid implementation without overhead

• Experiments: Try ideas without affecting main branch

Orchestrated Development

Ideal for:

• Large Features: Break down and parallelize development

• System Refactoring: Coordinate multiple related changes

• API Development: Build endpoints, models, and tests in parallel

🎯 Quick Start Examples

Example 1: Voting Pattern

# 1. Start automated voting session
create_voting_worktrees(
    task="Add Redis caching to the user service with error handling",
    num_variants=3
)

# 2. Check progress (Claude is working automatically)
list_sessions()
# Returns: "2/3 complete, 3/3 executed"

# 3. View results and rankings  
evaluate_implementations(session_id="xyz789")
# Shows ranked implementations with scores

# 4. Auto-select winner and merge
auto_select_best(session_id="xyz789", merge_to_main=true)
# Merges best implementation, cleans up others

Example 2: Quick Fix

# Create single worktree for a bug fix
create_adhoc_worktree(
    task="Fix null pointer exception in user profile component"
)
# Claude starts immediately in new Terminal window

Example 3: Feature Development

# Break down complex feature into subtasks
create_orchestrated_worktrees(
    task="Add real-time notifications",
    subtasks=[
        "Set up WebSocket server infrastructure",
        "Create notification database schema and models",
        "Build notification API endpoints",
        "Implement frontend notification component",
        "Add notification preferences to user settings"
    ]
)
# 5 Terminal windows open, each with Claude working on a subtask

⚡ Performance Notes

• Concurrent Execution: All Claude instances run in parallel

• Automatic Cleanup: Failed/low-quality implementations are removed

• Resource Efficient: Only keeps the winning implementation

• Fast Evaluation: Uses git diff stats and automated test detection

• Smart Naming: Worktrees include task description for easy identification

🔧 Advanced Features

Worktree Naming

• Branches and directories now include task descriptions

• Format: {session-id}-{task-slug}-{variant}

• Example: abc123-fix-login-bug-var1

Origin/Main Support

• Ad hoc worktrees always branch from origin/main

• Ensures clean starting point for isolated tasks

• Automatically fetches latest changes

Terminal Integration

• Automatically spawns Terminal windows (macOS)

• Each worktree gets its own Claude session

• No manual terminal management required