package enterprise

import (
    "context"
    "sync"
    "time"
    "github.com/prometheus/client_golang/prometheus"
)

// Enterprise MCP orchestrator with production features
type MCPOrchestrator struct {
    // Core components
    servers        map[string]*MCPServer
    loadBalancer   *LoadBalancer
    healthChecker  *HealthChecker
    
    // Enterprise features
    rateLimiter    *RateLimiter
    auditLogger    *AuditLogger
    securityPolicy *SecurityPolicy
    
    // Monitoring
    metrics        *MCPMetrics
    tracer         *DistributedTracer
    
    // Concurrency control
    mu             sync.RWMutex
    shutdown       chan struct{}
}

type MCPMetrics struct {
    RequestsTotal     prometheus.Counter
    ResponseTime      prometheus.Histogram
    ErrorRate         prometheus.Gauge
    ActiveConnections prometheus.Gauge
    ThroughputMBPS    prometheus.Gauge
}

func NewMCPOrchestrator(config *EnterpriseConfig) *MCPOrchestrator {
    orchestrator := &MCPOrchestrator{
        servers:      make(map[string]*MCPServer),
        loadBalancer: NewLoadBalancer(config.LoadBalancing),
        healthChecker: NewHealthChecker(config.HealthCheck),
        rateLimiter:   NewRateLimiter(config.RateLimit),
        auditLogger:   NewAuditLogger(config.Audit),
        securityPolicy: NewSecurityPolicy(config.Security),
        metrics:       NewMCPMetrics(),
        tracer:        NewDistributedTracer(config.Tracing),
        shutdown:      make(chan struct{}),
    }
    
    // Start background services
    go orchestrator.healthMonitoring()
    go orchestrator.metricsCollection()
    go orchestrator.securityMonitoring()
    
    return orchestrator
}

// Enterprise tool with comprehensive instrumentation
type EnterpriseTool struct {
    // Core functionality
    name        string
    description string
    handler     ToolHandler
    
    // Enterprise features
    permissions []Permission
    rateLimit   *RateLimit
    cache       *ToolCache
    validator   *InputValidator
    
    // Observability
    metrics     *ToolMetrics
    logger      *StructuredLogger
    tracer      *ToolTracer
    
    // Resilience
    circuitBreaker *CircuitBreaker
    retryPolicy    *RetryPolicy
    timeout        time.Duration
}

type ToolHandler interface {
    Execute(ctx context.Context, input *ToolInput) (*ToolOutput, error)
    Validate(input *ToolInput) error
    GetSchema() *ToolSchema
}

func (t *EnterpriseTool) Execute(ctx context.Context, input *ToolInput) (*ToolOutput, error) {
    // Start distributed tracing
    span := t.tracer.StartSpan(ctx, "tool.execute")
    defer span.Finish()
    
    // Rate limiting
    if err := t.rateLimit.Allow(ctx); err \!= nil {
        return nil, ErrRateLimitExceeded
    }
    
    // Input validation
    if err := t.validator.Validate(input); err \!= nil {
        return nil, err
    }
    
    // Permission check
    if err := t.checkPermissions(ctx, input); err \!= nil {
        return nil, ErrPermissionDenied
    }
    
    // Cache lookup
    if cached := t.cache.Get(input); cached \!= nil {
        return cached, nil
    }
    
    // Circuit breaker protection
    output, err := t.circuitBreaker.Execute(func() (interface{}, error) {
        return t.handler.Execute(ctx, input)
    })
    
    if err \!= nil {
        t.metrics.RecordError(err)
        return nil, err
    }
    
    result := output.(*ToolOutput)
    t.cache.Set(input, result)
    t.metrics.RecordSuccess()
    
    return result, nil
}

// Advanced resource provider with enterprise features
type EnterpriseResourceProvider struct {
    // Core resources
    resources map[string]*EnterpriseResource
    
    // Enterprise features
    accessControl  *ResourceAccessControl
    versioning     *ResourceVersioning
    synchronizer   *ResourceSynchronizer
    
    // Performance
    cache          *ResourceCache
    indexer        *ResourceIndexer
    compressor     *ResourceCompressor
    
    // Monitoring
    metrics        *ResourceMetrics
    auditor        *ResourceAuditor
}

type EnterpriseResource struct {
    // Metadata
    URI         string
    MimeType    string
    Size        int64
    Checksum    string
    Version     string
    
    // Content
    Content     []byte
    Metadata    map[string]interface{}
    
    // Enterprise features
    AccessPolicy   *AccessPolicy
    EncryptionKey  []byte
    CompressionAlg string
    
    // Lifecycle
    CreatedAt   time.Time
    UpdatedAt   time.Time
    ExpiresAt   *time.Time
    
    // Observability
    AccessCount int64
    LastAccessed time.Time
}

func (p *EnterpriseResourceProvider) GetResource(ctx context.Context, uri string) (*EnterpriseResource, error) {
    // Check access permissions
    if err := p.accessControl.CheckAccess(ctx, uri); err \!= nil {
        return nil, err
    }
    
    // Try cache first
    if cached := p.cache.Get(uri); cached \!= nil {
        p.metrics.RecordCacheHit()
        return cached, nil
    }
    
    // Load from storage
    resource, err := p.loadResource(ctx, uri)
    if err \!= nil {
        return nil, err
    }
    
    // Decrypt if necessary
    if resource.EncryptionKey \!= nil {
        if err := p.decryptResource(resource); err \!= nil {
            return nil, err
        }
    }
    
    // Decompress if necessary
    if resource.CompressionAlg \!= "" {
        if err := p.decompressResource(resource); err \!= nil {
            return nil, err
        }
    }
    
    // Update access tracking
    resource.AccessCount++
    resource.LastAccessed = time.Now()
    
    // Cache for future access
    p.cache.Set(uri, resource)
    
    // Audit access
    p.auditor.LogAccess(ctx, uri, resource)
    
    return resource, nil
}

#\!/bin/bash
# Enterprise MCP development environment setup

set -euo pipefail

# Configuration
PROJECT_NAME="enterprise-mcp-devops"
PYTHON_VERSION="3.11"
WORKSPACE_DIR="/opt/enterprise-mcp"
VENV_DIR="${WORKSPACE_DIR}/venv"

# Enterprise tools
TOOLS=(
    "docker"
    "docker-compose"
    "kubectl"
    "helm"
    "terraform"
    "vault"
    "consul"
    "prometheus"
    "grafana"
    "jaeger"
)

setup_enterprise_environment() {
    echo "Setting up enterprise MCP development environment..."
    
    # Create workspace
    sudo mkdir -p "${WORKSPACE_DIR}"
    sudo chown "${USER}:${USER}" "${WORKSPACE_DIR}"
    cd "${WORKSPACE_DIR}"
    
    # Setup Python environment
    pyenv install "${PYTHON_VERSION}"
    pyenv local "${PYTHON_VERSION}"
    python -m venv "${VENV_DIR}"
    source "${VENV_DIR}/bin/activate"
    
    # Install enterprise dependencies
    pip install --upgrade pip setuptools wheel
    pip install \
        anthropic-mcp-python==1.3.0rc1 \
        fastapi[all]==0.104.1 \
        uvicorn[standard]==0.24.0 \
        prometheus-client==0.19.0 \
        opentelemetry-api==1.21.0 \
        opentelemetry-sdk==1.21.0 \
        opentelemetry-instrumentation-fastapi==0.42b0 \
        structlog==23.2.0 \
        redis==5.0.1 \
        sqlalchemy[asyncio]==2.0.23 \
        alembic==1.13.0 \
        pydantic-settings==2.1.0 \
        cryptography==41.0.8 \
        pyjwt[crypto]==2.8.0 \
        httpx==0.25.2 \
        aiofiles==23.2.1 \
        python-multipart==0.0.6
    
    # Install development tools
    pip install \
        pytest==7.4.3 \
        pytest-asyncio==0.21.1 \
        pytest-cov==4.1.0 \
        black==23.11.0 \
        isort==5.12.0 \
        mypy==1.7.1 \
        pre-commit==3.6.0
    
    # Setup pre-commit hooks
    pre-commit install
    
    echo "Enterprise MCP environment setup complete\!"
}

install_enterprise_tools() {
    echo "Installing enterprise tools..."
    
    # Docker and Docker Compose
    curl -fsSL https://get.docker.com  < /dev/null |  sh
    sudo usermod -aG docker "${USER}"
    
    # Kubernetes tools
    curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"
    sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl
    
    # Helm
    curl https://baltocdn.com/helm/signing.asc | gpg --dearmor | sudo tee /usr/share/keyrings/helm.gpg > /dev/null
    echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/helm.gpg] https://baltocdn.com/helm/stable/debian/ all main" | sudo tee /etc/apt/sources.list.d/helm-stable-debian.list
    sudo apt-get update && sudo apt-get install helm
    
    # Terraform
    wget -O- https://apt.releases.hashicorp.com/gpg | sudo gpg --dearmor -o /usr/share/keyrings/hashicorp-archive-keyring.gpg
    echo "deb [signed-by=/usr/share/keyrings/hashicorp-archive-keyring.gpg] https://apt.releases.hashicorp.com $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/hashicorp.list
    sudo apt-get update && sudo apt-get install terraform
    
    echo "Enterprise tools installation complete\!"
}

setup_monitoring_stack() {
    echo "Setting up monitoring stack..."
    
    # Create monitoring namespace
    kubectl create namespace monitoring --dry-run=client -o yaml | kubectl apply -f -
    
    # Install Prometheus
    helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
    helm repo update
    helm install prometheus prometheus-community/kube-prometheus-stack \
        --namespace monitoring \
        --set grafana.adminPassword=admin123 \
        --set prometheus.prometheusSpec.retention=30d
    
    # Install Jaeger
    helm repo add jaegertracing https://jaegertracing.github.io/helm-charts
    helm install jaeger jaegertracing/jaeger \
        --namespace monitoring
    
    echo "Monitoring stack setup complete\!"
}

# Execute setup
setup_enterprise_environment
install_enterprise_tools
setup_monitoring_stack

# enterprise_mcp_server.py
"""Enterprise-grade MCP server with production features."""

import asyncio
import logging
import time
from contextlib import asynccontextmanager
from typing import AsyncIterator, Dict, List, Optional

import structlog
from fastapi import FastAPI, HTTPException, Depends
from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.gzip import GZipMiddleware
from prometheus_client import Counter, Histogram, Gauge, generate_latest
from opentelemetry import trace
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor

from mcp.server import FastMCP
from mcp.server.models import InitializationOptions

# Configure structured logging
structlog.configure(
    processors=[
        structlog.stdlib.filter_by_level,
        structlog.stdlib.add_logger_name,
        structlog.stdlib.add_log_level,
        structlog.stdlib.PositionalArgumentsFormatter(),
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.StackInfoRenderer(),
        structlog.processors.format_exc_info,
        structlog.processors.UnicodeDecoder(),
        structlog.processors.JSONRenderer()
    ],
    context_class=dict,
    logger_factory=structlog.stdlib.LoggerFactory(),
    wrapper_class=structlog.stdlib.BoundLogger,
    cache_logger_on_first_use=True,
)

logger = structlog.get_logger()

# Metrics
REQUEST_COUNT = Counter('mcp_requests_total', 'Total MCP requests', ['tool', 'status'])
REQUEST_DURATION = Histogram('mcp_request_duration_seconds', 'Request duration')
ACTIVE_CONNECTIONS = Gauge('mcp_active_connections', 'Active connections')
ERROR_RATE = Gauge('mcp_error_rate', 'Error rate')

# Enterprise configuration
class EnterpriseConfig:
    """Enterprise MCP server configuration."""
    
    def __init__(self):
        self.max_concurrent_requests = 1000
        self.request_timeout = 300
        self.rate_limit_per_minute = 100
        self.cache_ttl = 3600
        self.encryption_enabled = True
        self.audit_logging = True
        self.distributed_tracing = True

# Application context
class AppContext:
    """Shared application context."""
    
    def __init__(self):
        self.encoding_client: Optional[EncodingClient] = None
        self.redis_client: Optional[RedisClient] = None
        self.database: Optional[Database] = None
        self.config = EnterpriseConfig()

@asynccontextmanager
async def lifespan(app: FastAPI) -> AsyncIterator[AppContext]:
    """Application lifespan manager."""
    logger.info("Starting enterprise MCP server")
    
    # Initialize application context
    ctx = AppContext()
    
    try:
        # Initialize clients
        ctx.encoding_client = EncodingClient()
        ctx.redis_client = RedisClient()
        ctx.database = Database()
        
        # Setup monitoring
        setup_tracing()
        
        # Start background tasks
        await start_background_tasks(ctx)
        
        logger.info("Enterprise MCP server started successfully")
        yield ctx
        
    finally:
        # Cleanup
        await cleanup_resources(ctx)
        logger.info("Enterprise MCP server stopped")

# Create enterprise MCP server
mcp = FastMCP(
    "enterprise-encoding-manager",
    lifespan=lifespan
)

# Enterprise tools
@mcp.tool()
async def get_job_status(job_id: str, ctx: AppContext) -> Dict:
    """Get comprehensive job status with enterprise features."""
    start_time = time.time()
    
    try:
        # Distributed tracing
        tracer = trace.get_tracer(__name__)
        with tracer.start_as_current_span("get_job_status") as span:
            span.set_attribute("job_id", job_id)
            
            # Rate limiting
            await rate_limit_check(ctx, "get_job_status")
            
            # Cache lookup
            cached_status = await ctx.redis_client.get(f"job_status:{job_id}")
            if cached_status:
                span.set_attribute("cache_hit", True)
                REQUEST_COUNT.labels(tool="get_job_status", status="cache_hit").inc()
                return cached_status
            
            # Fetch from encoding service
            job_data = await ctx.encoding_client.get_job_status(job_id)
            
            # Enrich with additional data
            enriched_data = await enrich_job_data(job_data, ctx)
            
            # Cache result
            await ctx.redis_client.setex(
                f"job_status:{job_id}",
                ctx.config.cache_ttl,
                enriched_data
            )
            
            # Audit logging
            await audit_log(ctx, "get_job_status", {"job_id": job_id})
            
            REQUEST_COUNT.labels(tool="get_job_status", status="success").inc()
            return enriched_data
            
    except Exception as e:
        REQUEST_COUNT.labels(tool="get_job_status", status="error").inc()
        logger.error("Failed to get job status", job_id=job_id, error=str(e))
        raise HTTPException(status_code=500, detail=str(e))
    
    finally:
        REQUEST_DURATION.observe(time.time() - start_time)

@mcp.tool()
async def analyze_encoding_failure(job_id: str, ctx: AppContext) -> Dict:
    """Advanced failure analysis with ML-powered insights."""
    tracer = trace.get_tracer(__name__)
    
    with tracer.start_as_current_span("analyze_encoding_failure") as span:
        span.set_attribute("job_id", job_id)
        
        # Get job details
        job_data = await ctx.encoding_client.get_job_details(job_id)
        
        # Analyze failure patterns
        failure_analysis = await analyze_failure_patterns(job_data, ctx)
        
        # Get similar failures
        similar_failures = await find_similar_failures(job_data, ctx)
        
        # Generate recommendations
        recommendations = await generate_recommendations(
            job_data, failure_analysis, similar_failures, ctx
        )
        
        # Predict resolution time
        estimated_resolution = await predict_resolution_time(job_data, ctx)
        
        result = {
            "job_id": job_id,
            "failure_analysis": failure_analysis,
            "similar_failures": similar_failures,
            "recommendations": recommendations,
            "estimated_resolution_time": estimated_resolution,
            "confidence_score": calculate_confidence_score(failure_analysis)
        }
        
        # Store analysis for future reference
        await ctx.database.store_failure_analysis(job_id, result)
        
        return result

@mcp.tool()
async def generate_incident_report(job_id: str, include_timeline: bool = True, ctx: AppContext) -> str:
    """Generate comprehensive incident report."""
    # Get job data and analysis
    job_data = await ctx.encoding_client.get_job_details(job_id)
    failure_analysis = await ctx.database.get_failure_analysis(job_id)
    
    # Generate timeline
    timeline = []
    if include_timeline:
        timeline = await generate_incident_timeline(job_id, ctx)
    
    # Create report
    report = f"""
# Incident Report: Encoding Job {job_id}

## Executive Summary
- **Incident ID**: {job_id}
- **Severity**: {failure_analysis.get('severity', 'Medium')}
- **Impact**: {failure_analysis.get('impact_assessment', 'Service Degradation')}
- **Root Cause**: {failure_analysis.get('root_cause', 'Under Investigation')}

## Technical Details
- **Job Type**: {job_data.get('job_type', 'Unknown')}
- **Input File**: {job_data.get('input_file', 'N/A')}
- **Error Code**: {job_data.get('error_code', 'N/A')}
- **Error Message**: {job_data.get('error_message', 'N/A')}

## Timeline
"""
    
    for event in timeline:
        report += f"- **{event['timestamp']}**: {event['description']}\n"
    
    report += f"""

## Resolution Steps
{format_resolution_steps(failure_analysis.get('recommendations', []))}

## Prevention Measures
{format_prevention_measures(failure_analysis.get('prevention_measures', []))}

---
*Report generated automatically by Enterprise MCP DevOps Assistant*
*Generated at: {time.strftime('%Y-%m-%d %H:%M:%S UTC')}*
"""
    
    return report

# Enterprise resources
@mcp.resource("email://enterprise-incident-notification/{job_id}/{severity}")
def incident_notification_template(job_id: str, severity: str) -> str:
    """Enterprise incident notification template."""
    return f"""Subject: [INCIDENT-{severity.upper()}] Encoding Job {job_id} Failed

Dear Operations Team,

An encoding job has failed and requires immediate attention.

**Incident Details:**
- Job ID: {job_id}
- Severity: {severity}
- Timestamp: {{{{ incident_timestamp }}}}
- Environment: {{{{ environment }}}}

**Impact Assessment:**
{{{{ impact_description }}}}

**Immediate Actions Required:**
1. Review job logs and error details
2. Assess customer impact
3. Initiate recovery procedures if necessary
4. Update incident tracking system

**Next Steps:**
- Technical team will investigate root cause
- Customer communications will be sent if needed
- Post-incident review will be scheduled

This is an automated notification from the Enterprise MCP DevOps Assistant.

Best regards,
DevOps Automation Team
"""

@mcp.resource("runbook://encoding-failure-response/{error_type}")
def encoding_failure_runbook(error_type: str) -> str:
    """Enterprise runbook for encoding failures."""
    runbooks = {
        "file_corruption": """
# Encoding Failure Runbook: File Corruption

## Immediate Response (0-15 minutes)
1. **Verify File Integrity**
   ```bash
   ffprobe -v error -show_entries format=filename,size,bit_rate,duration -of csv=p=0 /path/to/file