Actionable coding & configuration rules for building observable, self-healing monitoring and logging pipelines on MCP servers using Prometheus, Grafana, ELK/Loki, Datadog and Terraform-as-Code.
You're building MCP servers that need to handle real traffic, respond to incidents before users notice, and scale without constant firefighting. Traditional monitoring approaches leave you debugging in the dark when things go wrong.
MCP servers present unique observability challenges that generic monitoring solutions can't address:
You need monitoring that understands MCP architectures and delivers actionable insights, not just data collection.
These Cursor Rules create a comprehensive monitoring and logging system specifically designed for MCP server environments. The ruleset combines battle-tested open source tools (Prometheus, Grafana, Loki) with enterprise platforms (Datadog, New Relic) through Infrastructure-as-Code patterns that scale.
Proactive Detection: ML-powered anomaly detection that learns your traffic patterns and alerts on genuine issues before they impact users. No more false positives from CPU spikes during routine deployments.
End-to-End Visibility: Correlated metrics, logs, and traces in unified dashboards. When latency spikes, you immediately see the related error logs and trace spans – no context switching between tools.
Self-Healing Infrastructure: Automated runbooks that resolve common issues without human intervention. Database connection pools exhausted? The system detects and restarts the connection manager automatically.
# Example: Auto-scaling trigger based on composite signals
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
name: mcp-api-autoscale-trigger
spec:
groups:
- name: mcp.api.scaling
rules:
- alert: APIHighLoadComposite
expr: |
(
rate(mcp_api_requests_total[5m]) > 1000 and
mcp_api_p95_latency_seconds > 0.5 and
mcp_api_error_rate > 0.05
)
for: 2m
annotations:
runbook: "scripts/api-scale-up.py --instances=3 --region={{ $labels.region }}"
Pre-built correlation dashboards surface root causes immediately. Instead of spending 45 minutes tracing through logs during incidents, you identify the failing component in under 10 minutes.
Machine learning baselines replace static CPU/memory thresholds. Get alerted when request latency deviates from learned patterns, not when someone runs a batch job.
Common failure scenarios trigger automated remediation. Service mesh configuration drift? Auto-rollback. Database deadlocks? Connection pool restart. Memory leaks? Graceful pod cycling.
Prometheus sharding and Thanos federation handle 10,000+ targets without performance degradation. Your monitoring scales with your MCP environment.
# Add monitoring to any new service with one command
./scripts/bootstrap-monitoring.py --service=payment-api --team=backend --tier=critical
This automatically creates:
Time saved: 4 hours of manual dashboard creation per service
Before: SSH into servers, grep through logs, check multiple monitoring dashboards
# Traditional approach - scattered information
ssh prod-server-01
grep ERROR /var/log/app.log | tail -100
# Switch to Grafana, check CPU dashboard
# Switch to Datadog, check request rates
# Try to correlate timestamps manually
After: Single pane of glass with correlated signals
# Grafana dashboard shows unified view
- Request rate spike at 14:23:15
- Error rate increases from 0.1% to 5.2%
- Related log entries automatically filtered
- Distributed traces show database timeout
- Runbook suggests connection pool adjustment
Time saved: 30+ minutes per incident investigation
Exemplar integration connects high-latency requests directly to distributed traces:
# Prometheus query with trace correlation
histogram_quantile(0.95, mcp_api_request_duration_seconds_bucket{service="auth"})
# Click on outlier point → Jump directly to Jaeger trace
# See exact database query causing slowdown
# Optimize based on trace spans, not guesswork
Result: 60% reduction in performance debugging time
git clone <your-mcp-repo>
cd monitoring-infrastructure
# Directory structure matches the ruleset
mkdir -p {monitoring/{prometheus-rules,alertmanager,grafana-dashboards},logging/{loki,fluent-bit,parsers},terraform/{modules,envs}}
# terraform/modules/monitoring/main.tf
module "prometheus" {
source = "./prometheus"
retention_days = 30
shard_count = var.target_count > 10000 ? 3 : 1
# Automatic service discovery
kubernetes_discovery = true
cloud_discovery = {
aws = var.enable_aws_discovery
azure = var.enable_azure_discovery
gcp = var.enable_gcp_discovery
}
}
module "grafana" {
source = "./grafana"
# Unified data sources
prometheus_url = module.prometheus.endpoint
loki_url = module.loki.endpoint
jaeger_url = module.jaeger.endpoint
# Auto-provision dashboards from Git
dashboard_sync_repo = var.dashboard_repo_url
}
# Initialize Terraform with environment-specific variables
cd terraform/envs/production
terraform init
terraform plan -var-file="production.tfvars"
terraform apply
# Deploy Prometheus rules
kubectl apply -f ../../monitoring/prometheus-rules/
# Sync Grafana dashboards
./scripts/sync-dashboards.py --env=production
# monitoring/alertmanager/routes.yml
route:
group_by: ['severity', 'team']
routes:
- match:
severity: critical
receiver: 'pagerduty-critical'
group_wait: 30s
- match:
severity: warning
receiver: 'slack-alerts'
group_wait: 5m
receivers:
- name: 'pagerduty-critical'
pagerduty_configs:
- integration_key: '{{ .ExternalURL }}'
description: '{{ range .Alerts }}{{ .Annotations.summary }}{{ end }}'
# Run comprehensive validation
./scripts/validate-monitoring.py --full-stack
# Test alert firing
./scripts/chaos-test.py --kill-random-pod --duration=60s
# Verify runbook automation
./scripts/test-runbooks.py --service=api --scenario=high-latency
# Before vs After Metrics
incident_mttr:
before: 45_minutes
after: 12_minutes
improvement: 73%
alert_precision:
before: 23% # true positives / total alerts
after: 89%
improvement: 387%
deployment_confidence:
before: 67% # deployments without rollback
after: 94%
improvement: 40%
The ruleset transforms MCP server monitoring from reactive debugging to proactive system management. You'll spend less time fighting fires and more time building features that matter.
Your MCP infrastructure becomes self-aware, self-healing, and genuinely observable – exactly what production systems demand.
You are an expert in multi-cloud platform (MCP) monitoring, logging and observability. Primary IaC / config language: YAML (Prometheus rules, Kubernetes manifests, Terraform variables). Secondary scripting: Python (automation, runbooks).
Key Principles
- Treat monitoring & logging as first-class code; commit all dashboards, alerts, and pipelines to VCS with CI/CD.
- Prioritise end-user experience: start from service-level objectives (SLOs) and derive metrics, logs, traces.
- Combine the three pillars of observability (metrics, logs, traces) in one UI (Grafana/Splunk/Datadog).
- Automate everything: provisioning, threshold tuning, incident response via runbooks / Lambda / Functions.
- Prefer proactive, ML-assisted anomaly detection over static thresholds; keep fall-back static rules for critical paths.
- Fail safely: never let monitoring or logging outages impact production workloads (out-of-band collectors, back-pressure).
- Security by design: encrypt in-flight & at-rest, apply RBAC, scrub PII at log source.
YAML / IaC Rules
- Use lower-kebab-case for all Kubernetes object names, Prometheus rule files, Terraform modules.
Example: `mcp-core-latency-alert.yml`.
- Structure repository:
`monitoring/` (prometheus-rules/, alertmanager/, grafana-dashboards/)
`logging/` (loki/, fluent-bit/, parsers/)
`terraform/` (modules/, envs/, variables.tf).
- Keep one logical rule per PrometheusRule object; group by functional domain (auth, payments, infra).
- Use JSONSchema to validate any custom YAML (e.g., log parser configs) in CI.
- Prefer explicit version pins (`image: grafana/grafana:10.2.1`) to avoid breaking changes.
- Always tag dashboards with `team`, `service`, `tier` labels for programmatic ownership routing.
Python Automation Rules
- Only use Python 3.11+.
- Follow PEP-8; 88-char lines; snake_case for functions, UPPER_SNAKE for constants.
- Modularise runbooks: one script per remediation task, callable via CLI flags and AWS Lambda.
- Always wrap external API calls (Grafana, Datadog) with retry + exponential back-off (@tenacity).
- Return rich ExitCodes (0 OK, 1 soft failure retriable, 2 hard failure page SRE).
- Emit structured JSON logs (`logger.info(json.dumps({...}))`) to ease ingestion by Loki/Splunk.
Error Handling & Validation
- Validate all threshold maths at PR time using unit tests (pytest) plus sanity script `verify_rules.py` that loads every PrometheusRule.
- Implement canary alerts: low-impact test rules that fire every 5 min; alert if not received (detect pipeline break).
- In Alertmanager:
• Route by `severity`, `team`, `env`.
• Use inhibition rules to suppress noise (e.g., mute instance-down if cluster-down already firing).
- Store last 24h alert fire rate; auto-escalate if frequency grows >50% week-over-week.
Framework-Specific Rules
Prometheus
- Record rules first, alert rules second; reuse recorded metrics (reduces engine load).
- Always add `for:` clause > 5m to avoid flapping.
- Naming: `mcp_<layer>_<metric>`, e.g., `mcp_api_p99_latency_seconds`.
Grafana
- Folder hierarchy mirrors repository.
- Use variables (`$env`, `$instance`) to reuse dashboards.
- Set panel thresholds colour-blind friendly (green/amber/red #E24D42 #3F6833).
Loki / ELK
- JSON logs only: key set `timestamp`, `level`, `msg`, `service`, `trace_id`.
- Apply log retention tiers:
hot 72h SSD, warm 30d HDD, cold >30d object storage (S3/GLACIER).
Datadog
- Tag every metric with `env`, `service`, `team`. Mandatory for auto-dashboards.
- Use Composite Monitors for multi-signal (metric + log + trace) correlations.
Testing & Validation
- Chaos tests weekly: kill random collector, throttle network; alerts must fire within 60s.
- Synthetic probes every 1 min from 3 regions hitting critical HTTP endpoints; fail if p95 > SLO.
- CI pipeline stages: lint → unit test → schema validate → dry-run apply (terraform plan) → deploy canary → promote.
Performance & Scaling
- Shard Prometheus by tenancy when targets >10k; use Thanos/Cortex for global view.
- Enable exemplar support for trace-metrics correlation (Jaeger/OpenTelemetry) to debug latency.
Security
- Apply mTLS between exporters and Prometheus.
- Use IAM roles for Service Accounts (IRSA) to grant Grafana read-only CloudWatch.
- Scrub secrets in logs via Fluent Bit filter (`lua_filter` masking regex).
Naming Conventions
- Alerts: `<service>_<issue>_<severity>`, e.g., `payments_db_latency_high_critical`.
- Dashboards: `[TEAM] <Service> – <Focus>`, e.g., `[CORE] Auth – Latency`.
- Runbook filenames: `<service>-<alert>-runbook.md`.
Incident Automation
- Store runbooks next to alert definitions; include: summary, diagnosis steps, remediation script path.
- Auto-execute remediation when alert severity == warning and success rate >80% historically.
Continuous Improvement
- Quarterly rule audit: retire silent alerts, adjust SLOs vs business OKRs.
- Enable auto-baselining: retrain ML models each release using previous 30d data.