class PersonalAssistantMCP:
    def __init__(self):
        self.calendar_api = GoogleCalendarAPI()
        self.email_api = GmailAPI()
        self.task_api = TodoistAPI()
        self.server = McpServer("personal-assistant")
        self.setup_tools()

    def setup_tools(self):
        @self.server.tool("schedule_meeting")
        async def schedule_meeting(
            title: str,
            attendees: list,
            duration: int,
            preferred_times: list
        ):
            """Find optimal meeting time and schedule"""
            # Check attendee availability
            availability = await self.check_availability(attendees, preferred_times)

            # Find best time slot
            optimal_time = self.find_optimal_slot(availability, duration)

            # Create calendar event
            event = await self.calendar_api.create_event({
                "summary": title,
                "attendees": attendees,
                "start": optimal_time,
                "duration": duration
            })

            # Send invitations
            await self.send_meeting_invites(event)

            return {
                "event_id": event.id,
                "scheduled_time": optimal_time,
                "attendees_notified": True
            }

        @self.server.tool("smart_email_response")
        async def smart_email_response(email_id: str, response_type: str = "professional"):
            """Generate contextually appropriate email response"""
            # Get email content and thread
            email = await self.email_api.get_email(email_id)
            thread = await self.email_api.get_thread(email.thread_id)

            # Analyze sentiment and context
            context = await self.analyze_email_context(email, thread)

            # Generate appropriate response
            response = await self.generate_response(context, response_type)

            return {
                "suggested_response": response,
                "tone": response_type,
                "key_points": context.key_points
            }

@mcp_server.tool("control_home_environment")
async def control_home_environment(command: str, rooms: list = None):
    """Control smart home devices with natural language"""
    # Parse natural language command
    intent = await nlp_parser.parse_intent(command)

    # Execute appropriate actions
    if intent.action == "adjust_temperature":
        await hvac_system.set_temperature(
            temperature=intent.temperature,
            rooms=rooms or ["all"]
        )

    elif intent.action == "control_lighting":
        await lighting_system.control_lights(
            brightness=intent.brightness,
            color=intent.color,
            rooms=rooms or ["all"]
        )

    elif intent.action == "play_music":
        await audio_system.play_music(
            playlist=intent.playlist,
            volume=intent.volume,
            rooms=rooms or ["living_room"]
        )

    return {
        "action_completed": True,
        "affected_devices": await get_affected_devices(),
        "status": "success"
    }

class CodeReviewMCP:
    def __init__(self):
        self.github_api = GitHubAPI()
        self.static_analyzer = StaticCodeAnalyzer()
        self.security_scanner = SecurityScanner()
        self.server = McpServer("code-review")
        self.setup_tools()

    def setup_tools(self):
        @self.server.tool("review_pull_request")
        async def review_pull_request(repo: str, pr_number: int):
            """Comprehensive automated code review"""
            # Get PR details and changes
            pr = await self.github_api.get_pull_request(repo, pr_number)
            changes = await self.github_api.get_pr_changes(repo, pr_number)

            # Analyze code quality
            quality_issues = await self.static_analyzer.analyze(changes)

            # Check for security vulnerabilities
            security_issues = await self.security_scanner.scan(changes)

            # Review architecture and best practices
            architecture_feedback = await self.review_architecture(changes)

            # Generate comprehensive review
            review = await self.generate_review_comment({
                "quality": quality_issues,
                "security": security_issues,
                "architecture": architecture_feedback
            })

            # Post review to GitHub
            await self.github_api.create_review(repo, pr_number, review)

            return {
                "review_posted": True,
                "issues_found": len(quality_issues) + len(security_issues),
                "recommendations": len(architecture_feedback)
            }

        @self.server.tool("generate_unit_tests")
        async def generate_unit_tests(file_path: str, functions: list):
            """Generate comprehensive unit tests for functions"""
            # Analyze function signatures and logic
            code_analysis = await self.analyze_code_file(file_path)

            # Generate test cases
            test_cases = []
            for function in functions:
                cases = await self.generate_test_cases(function, code_analysis)
                test_cases.extend(cases)

            # Create test file
            test_content = await self.create_test_file(test_cases)

            return {
                "test_file_content": test_content,
                "test_count": len(test_cases),
                "coverage_estimate": "85%"
            }

@mcp_server.tool("monitor_and_scale")
async def monitor_and_scale(service_name: str, metrics_window: str = "5m"):
    """Monitor service performance and auto-scale if needed"""
    # Get current metrics
    metrics = await monitoring_system.get_metrics(service_name, metrics_window)

    # Analyze performance patterns
    analysis = await performance_analyzer.analyze(metrics)

    # Check if scaling is needed
    if analysis.cpu_usage > 80 or analysis.memory_usage > 85:
        # Scale up
        new_instance_count = calculate_optimal_instances(analysis)
        await kubernetes_api.scale_deployment(service_name, new_instance_count)

        scaling_action = "scaled_up"
    elif analysis.cpu_usage < 20 and analysis.memory_usage < 30:
        # Scale down
        new_instance_count = max(1, analysis.current_instances - 1)
        await kubernetes_api.scale_deployment(service_name, new_instance_count)

        scaling_action = "scaled_down"
    else:
        scaling_action = "no_action_needed"

    return {
        "service": service_name,
        "action": scaling_action,
        "current_metrics": analysis.summary,
        "instance_count": analysis.current_instances
    }

class CustomerInsightsMCP:
    def __init__(self):
        self.crm_api = SalesforceAPI()
        self.analytics_db = AnalyticsDatabase()
        self.web_analytics = GoogleAnalyticsAPI()
        self.server = McpServer("customer-insights")
        self.setup_tools()

    def setup_tools(self):
        @self.server.tool("analyze_customer_journey")
        async def analyze_customer_journey(customer_id: str, timeframe: str = "30d"):
            """Comprehensive customer journey analysis"""
            # Get customer data from CRM
            customer_data = await self.crm_api.get_customer(customer_id)

            # Get interaction history
            interactions = await self.analytics_db.get_interactions(
                customer_id, timeframe
            )

            # Get web behavior
            web_behavior = await self.web_analytics.get_user_behavior(
                customer_data.web_id, timeframe
            )

            # Analyze journey patterns
            journey_analysis = await self.analyze_journey_patterns(
                interactions, web_behavior
            )

            # Generate insights and recommendations
            insights = await self.generate_customer_insights(
                customer_data, journey_analysis
            )

            return {
                "customer_id": customer_id,
                "journey_stages": journey_analysis.stages,
                "engagement_score": journey_analysis.engagement_score,
                "recommendations": insights.recommendations,
                "next_best_action": insights.next_action
            }

        @self.server.tool("predict_churn_risk")
        async def predict_churn_risk(segment: str = "all"):
            """Predict customer churn risk using ML models"""
            # Get customer data
            customers = await self.get_customers_by_segment(segment)

            # Run churn prediction model
            predictions = await self.churn_model.predict(customers)

            # Identify high-risk customers
            high_risk = [c for c in predictions if c.churn_probability > 0.7]

            # Generate retention strategies
            retention_strategies = await self.generate_retention_strategies(high_risk)

            return {
                "total_customers": len(customers),
                "high_risk_count": len(high_risk),
                "retention_strategies": retention_strategies,
                "model_accuracy": self.churn_model.accuracy
            }

@mcp_server.tool("generate_financial_forecast")
async def generate_financial_forecast(period: str, scenarios: list = None):
    """Generate financial forecasts with scenario analysis"""
    # Get historical financial data
    historical_data = await financial_db.get_historical_data(period)

    # Run forecasting models
    base_forecast = await forecasting_model.predict(historical_data)

    # Generate scenario-based forecasts
    scenario_forecasts = {}
    for scenario in scenarios or ["optimistic", "realistic", "pessimistic"]:
        scenario_params = await get_scenario_parameters(scenario)
        scenario_forecast = await forecasting_model.predict_scenario(
            historical_data, scenario_params
        )
        scenario_forecasts[scenario] = scenario_forecast

    # Generate executive summary
    summary = await generate_executive_summary(base_forecast, scenario_forecasts)

    return {
        "base_forecast": base_forecast,
        "scenarios": scenario_forecasts,
        "executive_summary": summary,
        "confidence_intervals": base_forecast.confidence_intervals
    }

class HealthcareMCP:
    def __init__(self):
        self.ehr_system = EHRSystem()
        self.medical_db = MedicalKnowledgeDB()
        self.drug_db = DrugInteractionDB()
        self.server = McpServer("healthcare-assistant")
        self.setup_secure_tools()

    def setup_secure_tools(self):
        @self.server.tool("analyze_patient_symptoms")
        async def analyze_patient_symptoms(
            patient_id: str,
            symptoms: list,
            duration: str
        ):
            """Analyze symptoms and provide clinical decision support"""
            # Verify authorization
            await self.verify_patient_access(patient_id)

            # Get patient history
            patient_history = await self.ehr_system.get_patient_history(patient_id)

            # Analyze symptoms against medical knowledge
            differential_diagnosis = await self.medical_db.analyze_symptoms(
                symptoms, patient_history
            )

            # Check for drug interactions
            current_medications = patient_history.medications
            interactions = await self.drug_db.check_interactions(
                current_medications, differential_diagnosis.suggested_treatments
            )

            return {
                "patient_id": patient_id,
                "differential_diagnosis": differential_diagnosis.conditions,
                "recommended_tests": differential_diagnosis.tests,
                "drug_interactions": interactions,
                "urgency_level": differential_diagnosis.urgency
            }

        @self.server.tool("generate_treatment_plan")
        async def generate_treatment_plan(
            patient_id: str,
            diagnosis: str,
            patient_preferences: dict = None
        ):
            """Generate personalized treatment plan"""
            # Get comprehensive patient data
            patient_data = await self.ehr_system.get_complete_profile(patient_id)

            # Generate evidence-based treatment options
            treatment_options = await self.medical_db.get_treatment_protocols(
                diagnosis, patient_data
            )

            # Personalize based on patient factors
            personalized_plan = await self.personalize_treatment(
                treatment_options, patient_data, patient_preferences
            )

            return {
                "patient_id": patient_id,
                "diagnosis": diagnosis,
                "treatment_plan": personalized_plan,
                "monitoring_schedule": personalized_plan.monitoring,
                "patient_education": personalized_plan.education_materials
            }

class AdaptiveLearningMCP:
    def __init__(self):
        self.learning_analytics = LearningAnalytics()
        self.content_generator = ContentGenerator()
        self.assessment_engine = AssessmentEngine()
        self.server = McpServer("adaptive-learning")
        self.setup_tools()

    def setup_tools(self):
        @self.server.tool("create_personalized_lesson")
        async def create_personalized_lesson(
            student_id: str,
            subject: str,
            learning_objective: str
        ):
            """Create personalized lesson based on student's learning profile"""
            # Get student learning profile
            profile = await self.learning_analytics.get_student_profile(student_id)

            # Analyze current knowledge level
            knowledge_gaps = await self.assess_knowledge_gaps(
                student_id, subject
            )

            # Generate adaptive content
            lesson_content = await self.content_generator.create_lesson(
                subject=subject,
                objective=learning_objective,
                learning_style=profile.preferred_style,
                difficulty_level=profile.current_level,
                knowledge_gaps=knowledge_gaps
            )

            # Create interactive exercises
            exercises = await self.create_adaptive_exercises(
                lesson_content, profile
            )

            return {
                "lesson_id": lesson_content.id,
                "content": lesson_content.materials,
                "exercises": exercises,
                "estimated_duration": lesson_content.duration,
                "learning_objectives": lesson_content.objectives
            }

        @self.server.tool("assess_learning_progress")
        async def assess_learning_progress(student_id: str, subject: str):
            """Assess student progress and adapt learning path"""
            # Get recent performance data
            performance = await self.learning_analytics.get_performance(
                student_id, subject
            )

            # Analyze learning patterns
            learning_patterns = await self.analyze_learning_patterns(performance)

            # Adjust learning path
            adjusted_path = await self.adjust_learning_path(
                student_id, learning_patterns
            )

            return {
                "student_id": student_id,
                "progress_score": performance.overall_score,
                "strengths": learning_patterns.strengths,
                "areas_for_improvement": learning_patterns.weaknesses,
                "next_topics": adjusted_path.next_topics,
                "recommended_study_time": adjusted_path.study_time
            }

@mcp_server.tool("optimize_inventory")
async def optimize_inventory(product_category: str = "all", forecast_period: str = "30d"):
    """Optimize inventory levels using demand forecasting"""
    # Get current inventory levels
    current_inventory = await inventory_db.get_current_levels(product_category)

    # Analyze historical sales patterns
    sales_history = await sales_db.get_sales_history(product_category, "1y")

    # Generate demand forecast
    demand_forecast = await demand_model.forecast(
        sales_history, forecast_period
    )

    # Calculate optimal stock levels
    optimal_levels = await calculate_optimal_stock(
        current_inventory, demand_forecast
    )

    # Generate reorder recommendations
    reorder_recommendations = await generate_reorder_recommendations(
        current_inventory, optimal_levels
    )

    return {
        "category": product_category,
        "current_stock_value": sum(item.value for item in current_inventory),
        "forecast_accuracy": demand_forecast.accuracy,
        "reorder_recommendations": reorder_recommendations,
        "potential_savings": calculate_potential_savings(optimal_levels)
    }

@mcp_server.tool("personalize_shopping_experience")
async def personalize_shopping_experience(
    customer_id: str,
    current_session_data: dict
):
    """Create personalized shopping experience in real-time"""
    # Get customer profile and history
    customer_profile = await customer_db.get_profile(customer_id)
    purchase_history = await order_db.get_purchase_history(customer_id)

    # Analyze current session behavior
    session_analysis = await analyze_session_behavior(current_session_data)

    # Generate personalized recommendations
    recommendations = await recommendation_engine.get_recommendations(
        customer_profile, purchase_history, session_analysis
    )

    # Optimize product display order
    optimized_catalog = await optimize_product_display(
        recommendations, customer_profile.preferences
    )

    # Generate personalized offers
    offers = await offer_engine.generate_offers(
        customer_profile, session_analysis
    )

    return {
        "customer_id": customer_id,
        "recommended_products": recommendations,
        "personalized_catalog": optimized_catalog,
        "special_offers": offers,
        "estimated_conversion_lift": "23%"
    }

class PredictiveMaintenanceMCP:
    def __init__(self):
        self.sensor_data = IoTSensorAPI()
        self.maintenance_db = MaintenanceDatabase()
        self.ml_models = PredictiveModels()
        self.server = McpServer("predictive-maintenance")
        self.setup_tools()

    def setup_tools(self):
        @self.server.tool("predict_equipment_failure")
        async def predict_equipment_failure(equipment_id: str, time_horizon: str = "30d"):
            """Predict potential equipment failures"""
            # Get real-time sensor data
            sensor_data = await self.sensor_data.get_real_time_data(equipment_id)

            # Get historical maintenance records
            maintenance_history = await self.maintenance_db.get_history(equipment_id)

            # Run predictive models
            failure_prediction = await self.ml_models.predict_failure(
                sensor_data, maintenance_history, time_horizon
            )

            # Generate maintenance recommendations
            recommendations = await self.generate_maintenance_plan(
                failure_prediction, equipment_id
            )

            return {
                "equipment_id": equipment_id,
                "failure_probability": failure_prediction.probability,
                "predicted_failure_date": failure_prediction.date,
                "critical_components": failure_prediction.components,
                "recommended_actions": recommendations,
                "cost_savings": failure_prediction.cost_savings
            }

        @self.server.tool("optimize_maintenance_schedule")
        async def optimize_maintenance_schedule(facility_id: str):
            """Optimize maintenance schedule for entire facility"""
            # Get all equipment in facility
            equipment_list = await self.get_facility_equipment(facility_id)

            # Predict maintenance needs for all equipment
            maintenance_predictions = []
            for equipment in equipment_list:
                prediction = await self.predict_equipment_failure(equipment.id)
                maintenance_predictions.append(prediction)

            # Optimize schedule considering resource constraints
            optimized_schedule = await self.optimize_schedule(
                maintenance_predictions, facility_id
            )

            return {
                "facility_id": facility_id,
                "total_equipment": len(equipment_list),
                "optimized_schedule": optimized_schedule,
                "resource_utilization": optimized_schedule.utilization,
                "projected_savings": optimized_schedule.savings
            }

class MCPOrchestrator:
    def __init__(self):
        self.servers = {
            "crm": CRMMCPServer(),
            "analytics": AnalyticsMCPServer(),
            "email": EmailMCPServer(),
            "calendar": CalendarMCPServer()
        }
        self.workflow_engine = WorkflowEngine()

    async def execute_workflow(self, workflow_definition: dict):
        """Execute complex workflow across multiple MCP servers"""
        workflow_id = generate_workflow_id()

        for step in workflow_definition["steps"]:
            server_name = step["server"]
            tool_name = step["tool"]
            parameters = step["parameters"]

            # Execute step
            result = await self.servers[server_name].call_tool(tool_name, parameters)

            # Store result for next steps
            await self.workflow_engine.store_step_result(workflow_id, step["id"], result)

            # Check for conditional logic
            if step.get("condition"):
                should_continue = await self.evaluate_condition(
                    step["condition"], result
                )
                if not should_continue:
                    break

        return await self.workflow_engine.get_workflow_result(workflow_id)

class SecureMCPGateway:
    def __init__(self):
        self.auth_manager = AuthenticationManager()
        self.audit_logger = AuditLogger()
        self.rate_limiter = RateLimiter()

    async def secure_tool_call(self, user_id: str, server: str, tool: str, params: dict):
        """Secure wrapper for all MCP tool calls"""
        # Authenticate and authorize
        await self.auth_manager.verify_permissions(user_id, server, tool)

        # Apply rate limiting
        await self.rate_limiter.check_limits(user_id)

        # Log the request
        await self.audit_logger.log_request(user_id, server, tool, params)

        # Execute the tool call
        result = await self.execute_tool_call(server, tool, params)

        # Log the response
        await self.audit_logger.log_response(user_id, server, tool, result)

        return result