plugins/render/skills/render-monitor/SKILL.md
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.
npx skillsauth add openai/plugins render-monitorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Real-time monitoring of Render services including health checks, performance metrics, and logs.
Activate this skill when users want to:
MCP tools (preferred): Test with list_services() - provides structured data
CLI (fallback): render --version - use if MCP tools unavailable
Authentication: For MCP, use an API key (set in the MCP config or via the RENDER_API_KEY env var, depending on tool). 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.
If list_services() fails, set up the Render MCP server. For detailed per-tool walkthroughs, see render-mcp.
Quick setup: Add the Render MCP server to your AI tool's MCP config:
https://mcp.render.com/mcpAuthorization: Bearer <YOUR_API_KEY>https://dashboard.render.com/u/*/settings#api-keysAfter configuring, restart your tool and retry list_services(). Then set your workspace with list_workspaces() / get_selected_workspace().
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)
list_services()
get_service(serviceId: "<id>")
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 |
list_logs(resource: ["<service-id>"], level: ["error"], limit: 50)
list_logs(resource: ["<service-id>"], statusCode: ["500", "502", "503"], limit: 50)
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% |
get_metrics(
resourceId: "<service-id>",
metricTypes: ["http_latency"],
httpLatencyQuantile: 0.95
)
| p95 Latency | Status | |-------------|--------| | <200ms | Excellent | | 200-500ms | Good | | 500ms-1s | Concerning | | >1s | Problem |
get_metrics(
resourceId: "<service-id>",
metricTypes: ["http_request_count"]
)
get_metrics(
resourceId: "<service-id>",
metricTypes: ["http_latency"],
httpPath: "/api/users"
)
Detailed metrics guide: references/metrics-guide.md
list_postgres_instances()
get_postgres(postgresId: "<postgres-id>")
get_metrics(resourceId: "<postgres-id>", metricTypes: ["active_connections"])
query_render_postgres(
postgresId: "<postgres-id>",
sql: "SELECT state, count(*) FROM pg_stat_activity GROUP BY state"
)
query_render_postgres(
postgresId: "<postgres-id>",
sql: "SELECT query, mean_exec_time FROM pg_stat_statements ORDER BY mean_exec_time DESC LIMIT 10"
)
list_key_value()
get_key_value(keyValueId: "<kv-id>")
list_logs(resource: ["<service-id>"], limit: 100)
list_logs(resource: ["<service-id>"], level: ["error"], limit: 50)
list_logs(resource: ["<service-id>"], text: ["timeout", "error"], limit: 50)
list_logs(
resource: ["<service-id>"],
startTime: "2024-01-15T10:00:00Z",
endTime: "2024-01-15T11:00:00Z"
)
render logs -r <service-id> --tail -o text
# 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>")
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>
| 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% |
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