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render-monitor

✓ Official★ 4,081

by openai · part of openai/plugins

Monitor Render services in real-time. Check health, performance metrics, logs, and resource usage. Use when users want to check service status, view metrics, monitor performance, or verify deployments are healthy.

🧩 One of 7 skills in the openai/plugins package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

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

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)
StatusMeaning
liveDeployment successful
build_in_progressBuilding
build_failedBuild failed
deactivatedReplaced by newer deploy

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"]
)
MetricHealthyWarningCritical
CPU<70%70-85%>85%
Memory<80%80-90%>90%

HTTP Latency

get_metrics(
  resourceId: "<service-id>",
  metricTypes: ["http_latency"],
  httpLatencyQuantile: 0.95
)
p95 LatencyStatus
<200msExcellent
200-500msGood
500ms-1sConcerning
>1sProblem

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)

render 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:

# 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

IndicatorHealthyWarningCritical
Deploy Statusliveupdate_in_progressbuild_failed
Error Rate<0.1%0.1-1%>1%
p95 Latency<500ms500ms-2s>2s
CPU Usage<70%70-90%>90%
Memory Usage<80%80-95%>95%

References

  • render-deploy — Deploy new applications to Render
  • render-debug — Diagnose and fix deployment failures
  • render-mcp — MCP server setup and tool catalog