universal-mcp-orchestration/knowledge-base/quick-reference/IMMEDIATE_INTEGRATION_STEPS.md

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#!/usr/bin/env python3
"""
Orchestration Manager - Works with existing MCP setup
Simplified version that integrates with your current system
"""

import asyncio
import json
import logging
import uuid
from datetime import datetime
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from enum import Enum

# Use the MCP framework from your existing setup
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))

try:
    from fastmcp import FastMCP
except ImportError:
    # Fallback for basic MCP functionality
    class FastMCP:
        def __init__(self, name):
            self.name = name
            self.tools = {}
        
        def tool(self, name):
            def decorator(func):
                self.tools[name] = func
                return func
            return decorator

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("orchestration-manager")

class WorkflowStatus(Enum):
    PENDING = "pending"
    RUNNING = "running"  
    COMPLETED = "completed"
    FAILED = "failed"

@dataclass
class WorkflowTask:
    id: str
    agent: str
    tool: str
    params: Dict[str, Any]
    status: str = "pending"
    result: Optional[Dict] = None
    error: Optional[str] = None

@dataclass  
class Workflow:
    id: str
    user_request: str
    status: WorkflowStatus
    tasks: List[WorkflowTask]
    current_task_index: int = 0
    created_at: str = None
    result: Optional[Dict] = None
    
    def __post_init__(self):
        if self.created_at is None:
            self.created_at = datetime.utcnow().isoformat()

# Create MCP server
mcp = FastMCP("orchestration-manager")

# Storage for workflows
workflows: Dict[str, Workflow] = {}

@mcp.tool
def start_workflow(request: str, workflow_type: str = "sequential") -> str:
    """Start a new multi-agent workflow for feature implementation"""
    workflow_id = str(uuid.uuid4())
    
    logger.info(f"🚀 Starting workflow {workflow_id}: {request}")
    
    # Create simple workflow - just use existing developer agent for now
    tasks = [
        WorkflowTask(
            id=str(uuid.uuid4()),
            agent="developer-agent", 
            tool="implement_feature",
            params={
                "feature_description": request,
                "target_directory": "/mnt/c/Users/ytr_o/Desktop/MCP/mcp-ui-app/src/renderer/src",
                "file_type": "typescript"
            }
        )
    ]
    
    workflow = Workflow(
        id=workflow_id,
        user_request=request,
        status=WorkflowStatus.PENDING,
        tasks=tasks
    )
    
    workflows[workflow_id] = workflow
    
    # Mark as running (would execute in real implementation)
    workflow.status = WorkflowStatus.RUNNING
    
    return json.dumps({
        "workflow_id": workflow_id,
        "status": "started",
        "message": f"Workflow started for: {request}",
        "task_count": len(tasks)
    })

@mcp.tool  
def get_workflow_status(workflow_id: str) -> str:
    """Get current status of a workflow"""
    if workflow_id not in workflows:
        return json.dumps({"error": "Workflow not found"})
    
    workflow = workflows[workflow_id]
    
    return json.dumps({
        "workflow_id": workflow_id,
        "status": workflow.status.value,
        "user_request": workflow.user_request,
        "task_count": len(workflow.tasks),
        "current_task": workflow.current_task_index,
        "created_at": workflow.created_at
    })

@mcp.tool
def list_workflows() -> str:
    """List all workflows"""
    workflow_list = []
    
    for workflow in workflows.values():
        workflow_list.append({
            "id": workflow.id,
            "status": workflow.status.value,
            "request": workflow.user_request[:50] + "..." if len(workflow.user_request) > 50 else workflow.user_request,
            "created_at": workflow.created_at
        })
    
    return json.dumps({
        "workflows": workflow_list,
        "total": len(workflow_list)
    })

@mcp.tool
def analyze_feature_request(request: str) -> str:
    """Analyze a feature request using built-in logic"""
    
    logger.info(f"🔍 Analyzing: {request}")
    
    # Simple analysis logic
    request_lower = request.lower()
    
    if any(word in request_lower for word in ["login", "auth", "password", "user"]):
        feature_type = "authentication"
        placement = "app-wrapper"
        complexity = "high"
    elif any(word in request_lower for word in ["dark", "theme", "light"]):
        feature_type = "theme"  
        placement = "app-level"
        complexity = "medium"
    elif any(word in request_lower for word in ["search", "find", "filter"]):
        feature_type = "search"
        placement = "main-content"
        complexity = "medium"
    else:
        feature_type = "ui_component"
        placement = "contextual"
        complexity = "medium"
    
    analysis = {
        "feature_name": request.title(),
        "feature_type": feature_type,
        "complexity": complexity,
        "placement": {
            "type": placement,
            "description": f"Feature should be placed at {placement} level"
        },
        "recommendations": [
            f"This appears to be a {feature_type} feature",
            f"Complexity level: {complexity}",
            f"Suggested placement: {placement}"
        ]
    }
    
    return json.dumps({
        "analysis": analysis,
        "success": True,
        "message": f"Analyzed {feature_type} feature successfully"
    })

if __name__ == "__main__":
    logger.info("🎼 Starting Orchestration Manager MCP Server")
    mcp.run()