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Monitoring and Logging in LLM-MCP

This comprehensive guide covers monitoring and logging best practices for the LLM Platform Model Context Protocol (LLM-MCP) to ensure optimal performance, reliability, and observability in production environments.

Table of Contents​

Monitoring Overview​

LLM-MCP provides comprehensive monitoring and logging capabilities to help you observe, understand, and debug your AI model orchestration platform. Proper monitoring enables you to:

  1. Detect issues before they affect users
  2. Understand performance characteristics
  3. Track usage patterns and growth
  4. Debug problems efficiently
  5. Plan capacity based on actual usage
  6. Validate SLAs with metrics

Logging Configuration​

Basic Logging Setup​

LLM-MCP uses a structured logging system that outputs JSON-formatted logs by default. Configure logging in your config.json:

{
  "logging": {
    "level": "info",
    "format": "json",
    "destination": "stdout",
    "colors": false,
    "timestamp": true,
    "serviceName": "llm-mcp",
    "correlationIdHeader": "x-correlation-id"
  }
}

Advanced Logging Configuration​

For production environments, consider a more detailed logging configuration:

{
  "logging": {
    "level": "info",
    "format": "json",
    "destination": "stdout",
    "colors": false,
    "timestamp": true,
    "serviceName": "llm-mcp",
    "correlationIdHeader": "x-correlation-id",
    "sensitiveFields": ["password", "token", "apiKey"],
    "includeRequestBody": false,
    "includeResponseBody": false,
    "logRequestHeaders": ["user-agent", "content-type", "accept"],
    "logResponseHeaders": ["content-type", "content-length"],
    "requestIdHeader": "x-request-id",
    "fileLogging": {
      "enabled": true,
      "directory": "/var/log/llm-mcp",
      "maxSize": "100m",
      "maxFiles": 10,
      "compress": true
    }
  }
}

Log Levels​

LLM-MCP supports the following log levels, in order of increasing severity:

  1. trace: Highly detailed information for debugging
  2. debug: Detailed information useful during development
  3. info: General operational information
  4. warn: Warning conditions that don't affect service
  5. error: Error conditions that affect specific operations
  6. fatal: Critical conditions that require immediate attention

Structured Logging​

LLM-MCP logs are structured for easy parsing and analysis:

{
  "level": "info",
  "timestamp": "2025-05-30T12:34:56.789Z",
  "service": "llm-mcp",
  "correlationId": "c1d2e3f4-g5h6-i7j8-k9l0",
  "requestId": "req-1234567890",
  "message": "Request processed successfully",
  "method": "POST",
  "path": "/api/v1/tools/execute",
  "statusCode": 200,
  "responseTime": 127,
  "userId": "user-123",
  "toolName": "example-tool",
  "component": "tool-executor"
}

Correlation IDs​

LLM-MCP automatically propagates correlation IDs through the system to track requests across multiple services:

// Example of using correlation IDs in custom code
import [getLogger] from '@llm-mcp/logger';

async function processRequest(req, res) {
  const correlationId = req.headers['x-correlation-id'] || generateId();
  const logger = getLogger().child({ correlationId });
  
  logger.info('Processing request', { 
    endpoint: req.path,
    method: req.method
  });
  
  // Pass correlation ID to downstream services
  const result = await callDownstreamService({
    headers: {
      'x-correlation-id': correlationId
    }
  });
  
  logger.info('Request completed', { result: 'success' });
}

Metrics and Instrumentation​

Core Metrics​

LLM-MCP exposes the following core metrics:

  1. Request Metrics:

    • Request count (total and by endpoint)
    • Request duration (histogram)
    • Request size (histogram)
    • Response size (histogram)
    • Error count (by type and status code)

  2. Tool Metrics:

    • Tool execution count (by tool)
    • Tool execution duration (histogram)
    • Tool execution errors (by tool and error type)

  3. System Metrics:

    • CPU usage
    • Memory usage
    • Active connections
    • Event loop lag
    • Garbage collection stats

Metrics Configuration​

Configure metrics collection in your config.json:

{
  "monitoring": {
    "metrics": {
      "enabled": true,
      "interval": 15,
      "prefix": "llm-mcp_",
      "defaultLabels": {
        "service": "llm-mcp",
        "environment": "production"
      },
      "prometheus": {
        "enabled": true,
        "port": 9090,
        "path": "/metrics"
      }
    }
  }
}

Custom Metrics​

You can define custom metrics for your specific use cases:

// Example of defining and using custom metrics
import [metrics] from '@llm-mcp/monitoring';

// Define custom metrics
const customMetrics = {
  vectorSearchLatency: metrics.histogram({
    name: 'vector_search_latency',
    help: 'Vector search operation latency in ms',
    labelNames: ['collection', 'dimensions'],
    buckets: [10, 50, 100, 250, 500, 1000, 2500, 5000]
  }),
  
  modelRegistrySize: metrics.gauge({
    name: 'model_registry_size',
    help: 'Number of models in the registry',
    labelNames: ['status']
  }),
  
  toolRegistrationsTotal: metrics.counter({
    name: 'tool_registrations_total',
    help: 'Total number of tool registrations',
    labelNames: ['result']
  })
};

// Using custom metrics
async function handleVectorSearch(collection, query, dimensions) {
  const startTime = Date.now();
  
  try {
    const result = await performVectorSearch(collection, query);
    
    // Record latency
    const latency = Date.now() - startTime;
    customMetrics.vectorSearchLatency.observe(
      { collection, dimensions: String(dimensions) },
      latency
    );
    
    return result;
  } catch (error) [// Handle error...]
}

// Update gauge periodically
function updateModelRegistryMetrics() {
  getModelCounts().then(counts => {
    customMetrics.modelRegistrySize.set({ status: 'active' }, counts.active);
    customMetrics.modelRegistrySize.set({ status: 'pending' }, counts.pending);
    customMetrics.modelRegistrySize.set({ status: 'error' }, counts.error);
  });
}

// Increment counter
function registerTool(tool) {
  try {
    // Register tool...
    customMetrics.toolRegistrationsTotal.inc({ result: 'success' });
  } catch (error) {
    customMetrics.toolRegistrationsTotal.inc({ result: 'failure' });
    // Handle error...
  }
}

Monitoring Dashboards​

Grafana Dashboard​

LLM-MCP provides pre-configured Grafana dashboards for monitoring. Example dashboard configuration:

{
  "dashboards": [
    {
      "name": "LLM-MCP Overview",
      "uid": "llm-mcp-overview",
      "panels": [
        {
          "title": "Request Rate",
          "type": "graph",
          "targets": [
            {
              "expr": "sum(rate(llm-mcp_http_requests_total[5m])) by (status_code)"
            }
          ]
        },
        {
          "title": "Request Latency (95th Percentile)",
          "type": "graph",
          "targets": [
            {
              "expr": "histogram_quantile(0.95, sum(rate(llm-mcp_http_request_duration_seconds_bucket[5m])) by (le))"
            }
          ]
        },
        {
          "title": "Memory Usage",
          "type": "gauge",
          "targets": [
            {
              "expr": "process_resident_memory_bytes{job=\"llm-mcp\"} / 1024 / 1024"
            }
          ]
        },
        {
          "title": "CPU Usage",
          "type": "graph",
          "targets": [
            {
              "expr": "rate(process_cpu_user_seconds_total{job=\"llm-mcp\"}[1m]) * 100"
            }
          ]
        }
      ]
    },
    {
      "name": "LLM-MCP Tools",
      "uid": "llm-mcp-tools",
      "panels": [
        {
          "title": "Tool Execution Rate",
          "type": "graph",
          "targets": [
            {
              "expr": "sum(rate(llm-mcp_tool_executions_total[5m])) by (tool_name)"
            }
          ]
        },
        {
          "title": "Tool Execution Latency (95th Percentile)",
          "type": "graph",
          "targets": [
            {
              "expr": "histogram_quantile(0.95, sum(rate(llm-mcp_tool_execution_duration_seconds_bucket[5m])) by (tool_name, le))"
            }
          ]
        },
        {
          "title": "Tool Errors",
          "type": "graph",
          "targets": [
            {
              "expr": "sum(rate(llm-mcp_tool_execution_errors_total[5m])) by (tool_name, error_type)"
            }
          ]
        }
      ]
    }
  ]
}

Dashboard for Vector Operations​

Example of a specialized dashboard for vector operations:

{
  "dashboards": [
    {
      "name": "LLM-MCP Vector Operations",
      "uid": "llm-mcp-vector-ops",
      "panels": [
        {
          "title": "Vector Embeddings Rate",
          "type": "graph",
          "targets": [
            {
              "expr": "sum(rate(llm-mcp_vector_embeddings_total[5m])) by (model)"
            }
          ]
        },
        {
          "title": "Vector Search Rate",
          "type": "graph",
          "targets": [
            {
              "expr": "sum(rate(llm-mcp_vector_searches_total[5m])) by (collection)"
            }
          ]
        },
        {
          "title": "Vector Search Latency (95th Percentile)",
          "type": "graph",
          "targets": [
            {
              "expr": "histogram_quantile(0.95, sum(rate(llm-mcp_vector_search_duration_seconds_bucket[5m])) by (collection, le))"
            }
          ]
        },
        {
          "title": "Vector Database Size",
          "type": "graph",
          "targets": [
            {
              "expr": "llm-mcp_vector_collection_size_bytes"
            }
          ]
        }
      ]
    }
  ]
}

Alerting​

Alert Configuration​

Configure alerts based on metrics thresholds:

# prometheus-alerts.yml
groups:
- name: llm-mcp-alerts
  rules:
  - alert: HighRequestLatency
    expr: histogram_quantile(0.95, sum(rate(llm-mcp_http_request_duration_seconds_bucket[5m])) by (le)) > 0.5
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "High request latency"
      description: "95th percentile request latency is above 500ms for 5 minutes"

  - alert: HighErrorRate
    expr: sum(rate(llm-mcp_http_requests_total[status_code=~"5.."][5m])) / sum(rate(llm-mcp_http_requests_total[5m])) > 0.05
    for: 5m
    labels:
      severity: critical
    annotations:
      summary: "High error rate"
      description: "Error rate is above 5% for 5 minutes"

  - alert: HighMemoryUsage
    expr: process_resident_memory_bytes[job="llm-mcp"] > 1.5e9
    for: 15m
    labels:
      severity: warning
    annotations:
      summary: "High memory usage"
      description: "Memory usage is above 1.5GB for 15 minutes"

  - alert: ToolExecutionErrors
    expr: sum(rate(llm-mcp_tool_execution_errors_total[5m])) by (tool_name) > 0.1
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "Tool execution errors"
      description: "Tool [$labels.tool_name] has a high error rate"

Alert Notifications​

Configure notification channels for alerts:

# alertmanager.yml
global:
  resolve_timeout: 5m

route:
  group_by: ['alertname', 'severity']
  group_wait: 30s
  group_interval: 5m
  repeat_interval: 4h
  receiver: 'team-email'
  routes:
  - match:
      severity: critical
    receiver: 'team-pager'

receivers:
- name: 'team-email'
  email_configs:
  - to: '[email protected]'
    send_resolved: true

- name: 'team-pager'
  pagerduty_configs:
  - service_key: '<pagerduty-service-key>'
    send_resolved: true

Performance Monitoring​

Key Performance Indicators (KPIs)​

Monitor these KPIs to ensure optimal LLM-MCP performance:

  1. Request Success Rate: Percentage of successful requests (non-5xx responses)
  2. Request Latency: Response time for API requests (p50, p95, p99)
  3. Tool Execution Success Rate: Percentage of successful tool executions
  4. Tool Execution Latency: Time taken to execute tools (p50, p95, p99)
  5. System Resource Utilization: CPU, memory, disk, and network usage
  6. Database Performance: Query latency, connection pool usage
  7. Vector Operations Performance: Vector embedding and search latency

Service Level Objectives (SLOs)​

Define SLOs for your LLM-MCP deployment:

# service-level-objectives.yml
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: llm-mcp-slos
  namespace: monitoring
spec:
  groups:
  - name: llm-mcp-slos
    rules:
    - record: llm-mcp:request_availability:ratio_5m
      expr: sum(rate(llm-mcp_http_requests_total[status_code!~"5.."][5m])) / sum(rate(llm-mcp_http_requests_total[5m]))

    - record: llm-mcp:latency_sli:ratio_5m
      expr: sum(rate(llm-mcp_http_request_duration_seconds_count[le="0.3"][5m])) / sum(rate(llm-mcp_http_request_duration_seconds_count[5m]))

    - record: llm-mcp:tool_execution_availability:ratio_5m
      expr: sum(rate(llm-mcp_tool_executions_total[status="success"][5m])) / sum(rate(llm-mcp_tool_executions_total[5m]))

    - alert: SLOAvailabilityBudgetBurning
      expr: llm-mcp:request_availability:ratio_5m < 0.995
      for: 15m
      labels:
        severity: warning
      annotations:
        summary: "SLO availability budget burning"
        description: "Service availability is below 99.5% for 15 minutes"

    - alert: SLOLatencyBudgetBurning
      expr: llm-mcp:latency_sli:ratio_5m < 0.95
      for: 15m
      labels:
        severity: warning
      annotations:
        summary: "SLO latency budget burning"
        description: "Less than 95% of requests are completing within 300ms for 15 minutes"

Health Checks​

Implementing Health Checks​

LLM-MCP provides built-in health check endpoints:

  1. Liveness Check: /health/live - Indicates if the service is running
  2. Readiness Check: /health/ready - Indicates if the service is ready to accept requests
  3. Startup Check: /health/startup - Indicates if the service has completed startup

Configure health checks in your config.json:

{
  "health": {
    "enabled": true,
    "port": 8081,
    "checks": {
      "database": true,
      "vectorStore": true,
      "cache": true,
      "externalServices": true,
      "diskSpace": {
        "enabled": true,
        "threshold": 90
      },
      "memory": {
        "enabled": true,
        "threshold": 90
      }
    }
  }
}

Custom Health Checks​

You can implement custom health checks:

// Example of implementing custom health checks
import { HealthCheck, registerHealthCheck } from '@llm-mcp/health';

class CustomServiceHealthCheck implements HealthCheck {
  name = 'custom-service';
  
  async check() {
    try {
      // Check custom service health
      const response = await fetch('https://custom-service.example.com/health');
      
      if (response.ok) {
        return {
          status: 'pass',
          message: 'Custom service is healthy'
        };
      } else {
        return {
          status: 'warn',
          message: `Custom service returned status [response.status]`
        };
      }
    } catch (error) {
      return {
        status: 'fail',
        message: `Custom service check failed: [error.message]`
      };
    }
  }
}

// Register the custom health check
registerHealthCheck(new CustomServiceHealthCheck());

Distributed Tracing​

OpenTelemetry Integration​

LLM-MCP supports OpenTelemetry for distributed tracing:

{
  "tracing": {
    "enabled": true,
    "exporter": "jaeger",
    "serviceName": "llm-mcp",
    "jaeger": {
      "endpoint": "http://jaeger:14268/api/traces",
      "username": "",
      "password": ""
    },
    "samplingRatio": 0.1
  }
}

Implementing Tracing​

Example of using tracing in custom code:

// Example of implementing custom tracing
import [tracer] from '@llm-mcp/tracing';

async function processToolExecution(request) {
  const span = tracer.startSpan('tool-execution', {
    attributes: {
      'tool.name': request.toolName,
      'request.id': request.id
    }
  });
  
  try {
    // Tool execution logic
    const params = request.params;
    
    // Create child span for parameter validation
    const validationSpan = tracer.startSpan('parameter-validation', {
      attributes: {
        'tool.name': request.toolName
      }
    });
    
    const validationResult = validateParameters(params);
    validationSpan.end();
    
    if (!validationResult.valid) {
      span.setAttribute('error', true);
      span.setAttribute('error.message', validationResult.message);
      span.end();
      return { error: validationResult.message };
    }
    
    // Create child span for tool execution
    const executionSpan = tracer.startSpan('execute-tool-handler', {
      attributes: {
        'tool.name': request.toolName
      }
    });
    
    const result = await executeTool(request.toolName, params);
    executionSpan.end();
    
    span.end();
    return result;
  } catch (error) {
    span.setAttribute('error', true);
    span.setAttribute('error.message', error.message);
    span.end();
    throw error;
  }
}

Log Analysis​

Log Aggregation​

Configure log aggregation with tools like ELK Stack (Elasticsearch, Logstash, Kibana) or Loki:

{
  "logging": {
    "aggregation": {
      "enabled": true,
      "type": "elasticsearch",
      "elasticsearch": {
        "nodes": ["http://elasticsearch:9200"],
        "username": "elastic",
        "password": "use_environment_variable",
        "index": "llm-mcp-logs"
      }
    }
  }
}

Log Parsing​

Example of log parsing configuration for Logstash:

# logstash.conf
input {
  file {
    path => "/var/log/llm-mcp/application.log"
    codec => "json"
  }
}

filter {
  # Parse timestamps
  date {
    match => [ "timestamp", "ISO8601" ]
    target => "@timestamp"
  }
  
  # Add environment tag
  mutate {
    add_field => {
      "environment" => "[ENVIRONMENT:production]"
    }
  }
  
  # Parse user agent
  if [userAgent] {
    useragent {
      source => "userAgent"
      target => "user_agent"
    }
  }
  
  # Parse error stack traces
  if [error][stack] {
    grok {
      match => { "[error][stack]" => "(?m)%{JAVASTACK:stack_trace}" }
    }
  }
}

output {
  elasticsearch {
    hosts => ["[ES_HOST:elasticsearch:9200]"]
    user => "[ES_USER:elastic]"
    password => "[ES_PASSWORD]"
    index => "llm-mcp-logs-%{+YYYY.MM.dd}"
  }
}

Log Visualization​

Example Kibana dashboard configuration for LLM-MCP logs:

{
  "attributes": {
    "title": "LLM-MCP Logs Dashboard",
    "hits": 0,
    "description": "Dashboard for LLM-MCP logs analysis",
    "panelsJSON": "[
      {
        \"panelIndex\": \"1\",
        \"gridData\": {
          \"x\": 0,
          \"y\": 0,
          \"w\": 24,
          \"h\": 8,
          \"i\": \"1\"
        },
        \"version\": \"7.10.0\",
        \"type\": \"visualization\",
        \"id\": \"llm-mcp-logs-over-time\"
      },
      {
        \"panelIndex\": \"2\",
        \"gridData\": {
          \"x\": 0,
          \"y\": 8,
          \"w\": 12,
          \"h\": 8,
          \"i\": \"2\"
        },
        \"version\": \"7.10.0\",
        \"type\": \"visualization\",
        \"id\": \"llm-mcp-error-distribution\"
      },
      {
        \"panelIndex\": \"3\",
        \"gridData\": {
          \"x\": 12,
          \"y\": 8,
          \"w\": 12,
          \"h\": 8,
          \"i\": \"3\"
        },
        \"version\": \"7.10.0\",
        \"type\": \"visualization\",
        \"id\": \"llm-mcp-response-times\"
      },
      {
        \"panelIndex\": \"4\",
        \"gridData\": {
          \"x\": 0,
          \"y\": 16,
          \"w\": 24,
          \"h\": 12,
          \"i\": \"4\"
        },
        \"version\": \"7.10.0\",
        \"type\": \"search\",
        \"id\": \"llm-mcp-error-logs\"
      }
    ]",
    "timeRestore": false,
    "kibanaSavedObjectMeta": {
      "searchSourceJSON": "{\"query\":{\"language\":\"kuery\",\"query\":\"\"},\"filter\":[]}"
    }
  }
}

Integration with External Systems​

Prometheus Integration​

Configure Prometheus to scrape LLM-MCP metrics:

# prometheus.yml
scrape_configs:
  - job_name: 'llm-mcp'
    scrape_interval: 15s
    metrics_path: '/metrics'
    static_configs:
      - targets: ['llm-mcp:9090']
    relabel_configs:
      - source_labels: [__address__]
        target_label: instance

Grafana Integration​

Configure Grafana with Prometheus data source:

# grafana-datasource.yaml
apiVersion: 1

datasources:
  - name: Prometheus
    type: prometheus
    access: proxy
    url: http://prometheus:9090
    isDefault: true
    version: 1
    editable: false

ELK Stack Integration​

Configure Filebeat to ship logs to Elasticsearch:

# filebeat.yml
filebeat.inputs:
- type: log
  enabled: true
  paths:
    - /var/log/llm-mcp/*.log
  json.keys_under_root: true
  json.add_error_key: true

output.elasticsearch:
  hosts: ["elasticsearch:9200"]
  username: "elastic"
  password: "[ELASTICSEARCH_PASSWORD]"
  index: "llm-mcp-logs-%[+yyyy.MM.dd]"

Monitoring in Kubernetes​

Kubernetes Monitoring Setup​

Example Kubernetes manifests for monitoring LLM-MCP:

# prometheus-service-monitor.yaml
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: llm-mcp
  namespace: monitoring
spec:
  selector:
    matchLabels:
      app: llm-mcp
  endpoints:
  - port: metrics
    interval: 15s
    path: /metrics
# grafana-dashboard-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: llm-mcp-grafana-dashboards
  namespace: monitoring
  labels:
    grafana_dashboard: "1"
data:
  llm-mcp-dashboard.json: |
    {
      "dashboard": {
        "id": null,
        "title": "LLM-MCP Overview",
        "tags": ["llm-mcp", "generated"],
        "timezone": "browser",
        "panels": [
          // Dashboard panels...
        ],
        "time": {
          "from": "now-6h",
          "to": "now"
        },
        "timepicker": {
          "refresh_intervals": ["5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d"]
        }
      }
    }

Kubernetes Resource Monitoring​

Configure resource monitoring for LLM-MCP pods:

# llm-mcp-deployment.yaml (excerpt)
spec:
  template:
    spec:
      containers:
      - name: llm-mcp
        image: llm-mcp:latest
        resources:
          limits:
            cpu: "2"
            memory: "2Gi"
          requests:
            cpu: "500m"
            memory: "1Gi"
        livenessProbe:
          httpGet:
            path: /health/live
            port: 8081
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /health/ready
            port: 8081
          initialDelaySeconds: 10
          periodSeconds: 5

Best Practices​

Monitoring Best Practices​

  1. Define clear SLOs: Set realistic Service Level Objectives
  2. Use proper log levels: Use appropriate log levels for different situations
  3. Include context in logs: Add relevant context to logs (user IDs, request IDs, etc.)
  4. Implement structured logging: Use structured logging for easier analysis
  5. Use correlation IDs: Track requests across services with correlation IDs
  6. Monitor business metrics: Track metrics relevant to business processes
  7. Set up proactive alerts: Alert on symptoms, not causes
  8. Use dashboards effectively: Create focused, purpose-driven dashboards
  9. Implement health checks: Add comprehensive health checks
  10. Regular review: Regularly review monitoring and adjust as needed

Logging Best Practices​

  1. Log at the right level: Don't over-log or under-log
  2. Protect sensitive data: Mask sensitive information in logs
  3. Use structured logging: Structure logs for easier parsing
  4. Include metadata: Add useful metadata to logs (service name, version, etc.)
  5. Standardize log formats: Use consistent log formats across services
  6. Implement log rotation: Rotate logs to manage disk space
  7. Centralize logs: Aggregate logs in a central location
  8. Implement log retention: Define clear log retention policies
  9. Use contextual logging: Include relevant context in logs
  10. Log actionable information: Log information that helps troubleshooting

Troubleshooting with Monitoring​

Common Issues and Monitoring Indicators​

IssueMonitoring IndicatorsPotential Causes
High LatencyIncreased llm-mcp_http_request_duration_secondsDatabase slowdown, CPU saturation, memory issues
Error SpikesIncreased llm-mcp_http_requests_total[status_code="5xx"]Code bugs, dependency failures, resource exhaustion
Memory LeaksGrowing process_resident_memory_bytesCode bugs, improper caching, large response handling
Connection IssuesDecreased llm-mcp_active_connectionsNetwork problems, misconfiguration, dependency failures
Database ProblemsIncreased llm-mcp_database_query_duration_secondsSlow queries, index issues, connection pool saturation
Tool Execution FailuresIncreased llm-mcp_tool_execution_errors_totalTool bugs, dependency failures, timeout issues

Debugging with Logs and Metrics​

Steps to debug issues using logs and metrics:

  1. Identify the issue: Use dashboards to identify symptoms
  2. Correlate with logs: Find relevant logs using correlation IDs
  3. Check dependencies: Examine metrics for dependent services
  4. Analyze patterns: Look for patterns in errors and latency
  5. Isolate components: Determine which component is causing the issue
  6. Check recent changes: Correlate issues with recent deployments
  7. Review resource usage: Check for resource constraints
  8. Examine traces: Use distributed tracing to follow request flow
  9. Test hypotheses: Make targeted changes to verify root cause
  10. Implement fix: Deploy fix and monitor results

Example Troubleshooting Workflow​

graph TD;
  A[Alert: High Error Rate] --> B[Check Error Logs];
  B --> C[Identify Error Pattern];
  C --> D[Error Type?];
  D -->|Database Errors| E[Check Database Metrics];
  D -->|Tool Execution Errors| F[Check Tool Metrics];
  D -->|External Service Errors| G[Check External Service Status];
  E --> H[Check Database Connections];
  F --> I[Check Tool Response Times];
  G --> J[Check Network Connectivity];
  H --> K[Implement Fix];
  I --> K;
  J --> K;
  K --> L[Verify Fix with Metrics];
  L --> M[Update Monitoring if Needed];

This comprehensive guide provides everything you need to effectively monitor and log your LLM-MCP deployment. By implementing these practices, you'll ensure optimal performance, reliability, and observability for your AI model orchestration platform.