---name: render-monitordescription: 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.license: MITcompatibility: Requires Render MCP tools or CLImetadata: author: Renderversion: 1.0.0category: monitoring---

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 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)

Status	Meaning
live	Deployment successful
build_in_progress	Building
build_failed	Build failed
deactivated	Replaced 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"]
)

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)

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

Indicator	Healthy	Warning	Critical
Deploy Status	live	update_in_progress	build_failed
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

• 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 tool catalog