Monitor Render Services
Real-time monitoring of Render services including health checks, performance metrics, and logs.
When to Use This Skill
Activate this skill when users want to:
- Check if services are healthy
- View performance metrics
- Monitor logs
- Verify a deployment is working
- Investigate slow performance
- Check database health
Render Access
Before reading service state, logs, metrics, or database health, follow references/render-access.md to establish access and verify the intended workspace. Prefer MCP when available; the CLI can provide service status and logs, while metrics and database queries require MCP.
Before interpreting deploy or health-check state, read references/deployments.md.
Quick Health Check
Run these 5 checks to assess service health:
# 1. Check service status list_services() # 2. Check latest deploy list_deploys(serviceId: "<service-id>", limit: 1) # 3. Check for errors list_logs(resource: ["<service-id>"], level: ["error"], limit: 20) # 4. Check resource usage get_metrics(resourceId: "<service-id>", metricTypes: ["cpu_usage", "memory_usage"]) # 5. Check latency get_metrics(resourceId: "<service-id>", metricTypes: ["http_latency"], httpLatencyQuantile: 0.95)
Service Health
Check Status
list_services()
get_service(serviceId: "<id>")
Check Deployments
list_deploys(serviceId: "<service-id>", limit: 5)
Follow references/deployments.md to interpret asynchronous and terminal states, identify the specific deploy, and verify the workload after it becomes live.
Check Errors
list_logs(resource: ["<service-id>"], level: ["error"], limit: 50)
list_logs(resource: ["<service-id>"], statusCode: ["500", "502", "503"], limit: 50)
Performance Metrics
CPU & Memory
get_metrics( resourceId: "<service-id>", metricTypes: ["cpu_usage", "memory_usage", "cpu_limit", "memory_limit"] )
| Metric | Healthy | Warning | Critical |
|---|---|---|---|
| CPU | <70% | 70-85% | >85% |
| Memory | <80% | 80-90% | >90% |
HTTP Latency
get_metrics( resourceId: "<service-id>", metricTypes: ["http_latency"], httpLatencyQuantile: 0.95 )
| p95 Latency | Status |
|---|---|
| <200ms | Excellent |
| 200-500ms | Good |
| 500ms-1s | Concerning |
| >1s | Problem |
Request Count
get_metrics( resourceId: "<service-id>", metricTypes: ["http_request_count"] )
Filter by Endpoint
get_metrics( resourceId: "<service-id>", metricTypes: ["http_latency"], httpPath: "/api/users" )
Detailed metrics guide: references/metrics-guide.md
Database Monitoring
PostgreSQL Status
list_postgres_instances() get_postgres(postgresId: "<postgres-id>")
Connection Count
get_metrics(resourceId: "<postgres-id>", metricTypes: ["active_connections"])
Query Database
query_render_postgres( postgresId: "<postgres-id>", sql: "SELECT state, count(*) FROM pg_stat_activity GROUP BY state" )
Find Slow Queries
query_render_postgres( postgresId: "<postgres-id>", sql: "SELECT query, mean_exec_time FROM pg_stat_statements ORDER BY mean_exec_time DESC LIMIT 10" )
Key-Value Store
list_key_value() get_key_value(keyValueId: "<kv-id>")
Log Monitoring
Recent Logs
list_logs(resource: ["<service-id>"], limit: 100)
Error Logs
list_logs(resource: ["<service-id>"], level: ["error"], limit: 50)
Search Logs
list_logs(resource: ["<service-id>"], text: ["timeout", "error"], limit: 50)
Filter by Time
list_logs( resource: ["<service-id>"], startTime: "2024-01-15T10:00:00Z", endTime: "2024-01-15T11:00:00Z" )
Stream Logs (CLI)
bashrender logs -r <service-id> --tail -o text
Quick Reference
MCP Tools
# Services list_services() get_service(serviceId: "<id>") list_deploys(serviceId: "<id>", limit: 5) # Logs list_logs(resource: ["<id>"], level: ["error"], limit: 100) list_logs(resource: ["<id>"], text: ["search"], limit: 50) # Metrics get_metrics(resourceId: "<id>", metricTypes: ["cpu_usage", "memory_usage"]) get_metrics(resourceId: "<id>", metricTypes: ["http_latency"], httpLatencyQuantile: 0.95) get_metrics(resourceId: "<id>", metricTypes: ["http_request_count"]) # Database list_postgres_instances() get_postgres(postgresId: "<id>") query_render_postgres(postgresId: "<id>", sql: "SELECT ...") get_metrics(resourceId: "<postgres-id>", metricTypes: ["active_connections"]) # Key-Value list_key_value() get_key_value(keyValueId: "<id>")
CLI Commands (Fallback)
Use these if MCP tools are unavailable:
bash# Service status render services -o json render services instances <service-id> # Deployments render deploys list <service-id> -o json # Logs render logs -r <service-id> --tail -o text # Stream logs render logs -r <service-id> --level error -o json # Error logs render logs -r <service-id> --type deploy -o json # Build logs # Database render psql <database-id> # Connect to PostgreSQL # SSH for live debugging render ssh <service-id>
Healthy Service Indicators
| Indicator | Healthy | Warning | Critical |
|---|---|---|---|
| Error Rate | <0.1% | 0.1-1% | >1% |
| p95 Latency | <500ms | 500ms-2s | >2s |
| CPU Usage | <70% | 70-90% | >90% |
| Memory Usage | <80% | 80-95% | >95% |
References
- Render access: references/render-access.md
- Compute plans: references/compute-plans.md
- Deployments: references/deployments.md
- Metrics guide: references/metrics-guide.md
Related Skills
- render-deploy — Deploy new applications to Render
- render-debug — Diagnose and fix deployment failures
- render-mcp — MCP server setup and capability discovery
Current documentation retrieval
Whenever this skill directs you to consult current Render documentation:
- Retrieve the linked Markdown document directly with an available URL-fetching tool or HTTP client, such as
curl. Do not substitute web-search summaries for the document. - Confirm that retrieval succeeded and returned the expected document, then read its contents. Saving a file or printing its path is not sufficient.
- If the request fails or your tool cannot read the Markdown response, open and read the linked HTML version instead.
- If neither version can be retrieved, disclose that the current reference is unavailable and follow any topic-specific fallback in the skill. Use bundled guidance only for stable constraints, and do not guess at changeable platform details.
When a task requires multiple references, apply this workflow to each one and distinguish the documents you verified from those that remain unavailable.
