Render Monitor

by render-osse8f889396634MITListed Oct 8, 2026Updated Oct 8, 2026

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.

Instructions onlyDevOps & Cloud
AI-generated overview

Monitors Render cloud services in real time: health checks, performance metrics, logs, and database status.

What it does
This skill guides an agent through checking the health of Render services using Render MCP tools or the Render CLI. It covers service status and deployment state, error and search logs, CPU, memory, HTTP latency and request-count metrics, plus PostgreSQL and key-value store checks. It produces status assessments against healthy, warning and critical thresholds, and points to a bundled metrics reference guide.
When to use it
Use it when a user wants to check whether a Render service is healthy, review performance metrics, inspect logs, verify a deployment is working, investigate slow performance, or check database health.
Requirements
Requires Render MCP tools (preferred) or the Render CLI as fallback; authentication via Render OAuth or a Render API key, and a selected workspace. Metrics and database queries require the MCP server. Ships no scripts, only instructions and a reference document.

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

Prerequisites

MCP tools (preferred): Test with list_services() - provides structured data

CLI (fallback): render --version - use if MCP tools unavailable

Authentication: If you installed the Render plugin (Cursor, Codex, Claude Code), it provides OAuth for MCP — complete the OAuth prompt. For manual MCP clients, use a Render API key. For CLI, verify with render whoami -o json.

Workspace: get_selected_workspace() or render workspace current -o json

Note: MCP tools require the Render MCP server. If unavailable, use the CLI for status and logs; metrics and database queries require MCP.

MCP Setup

If list_services() fails, set up the Render MCP server. For detailed per-tool walkthroughs, see render-mcp.

Plugin setup: If the Render plugin is installed, complete Render OAuth when prompted, then reload your tool and retry list_services().

Manual MCP setup: Add the Render MCP server to your AI tool's MCP config:

  • URL: https://mcp.render.com/mcp
  • Auth header: Authorization: Bearer <YOUR_API_KEY>
  • API key: https://dashboard.render.com/u/*/settings#api-keys

After configuring, restart your tool and retry list_services(). Then set your workspace with list_workspaces() / get_selected_workspace().

Quick Health Check

Run these 5 checks to assess service health:

# 1. Check service statuslist_services()
# 2. Check latest deploylist_deploys(serviceId: "<service-id>", limit: 1)
# 3. Check for errorslist_logs(resource: ["<service-id>"], level: ["error"], limit: 20)
# 4. Check resource usageget_metrics(resourceId: "<service-id>", metricTypes: ["cpu_usage", "memory_usage"])
# 5. Check latencyget_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 [blocked]

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)

bash
render logs -r <service-id> --tail -o text

Quick Reference

MCP Tools

# Serviceslist_services()get_service(serviceId: "<id>")list_deploys(serviceId: "<id>", limit: 5)
# Logslist_logs(resource: ["<id>"], level: ["error"], limit: 100)list_logs(resource: ["<id>"], text: ["search"], limit: 50)
# Metricsget_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"])
# Databaselist_postgres_instances()get_postgres(postgresId: "<id>")query_render_postgres(postgresId: "<id>", sql: "SELECT ...")get_metrics(resourceId: "<postgres-id>", metricTypes: ["active_connections"])
# Key-Valuelist_key_value()get_key_value(keyValueId: "<id>")

CLI Commands (Fallback)

Use these if MCP tools are unavailable:

bash
# Service statusrender services -o jsonrender services instances <service-id>
# Deploymentsrender deploys list <service-id> -o json
# Logsrender logs -r <service-id> --tail -o text          # Stream logsrender logs -r <service-id> --level error -o json   # Error logsrender logs -r <service-id> --type deploy -o json   # Build logs
# Databaserender psql <database-id>                           # Connect to PostgreSQL
# SSH for live debuggingrender 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

  • Metrics guide: references/metrics-guide.md [blocked]

Related Skills

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

Source and attribution

Source:render-oss/render-plugin-claude-codeinskills/render-monitorat commite8f8893

License: MIT

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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