Render Monitor

作者 render-osse8f889396634MIT收錄於 2026年10月8日更新於 2026年10月8日

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.

僅含說明DevOps & Cloud
AI 產生的概覽

即時監控 Render 雲端服務：健康檢查、效能指標、日誌與資料庫狀態。

功能
此技能引導代理使用 Render MCP 工具或 Render CLI 檢查 Render 服務的健康狀態。涵蓋服務狀態與部署狀態、錯誤日誌與搜尋日誌、CPU、記憶體、HTTP 延遲與請求量指標，以及 PostgreSQL 與鍵值儲存檢查。它會依健康、警告、嚴重三級門檻給出狀態評估，並指向隨附的指標參考文件。
適用情境
當使用者想檢查 Render 服務是否健康、檢視效能指標、查看日誌、確認部署是否正常、調查效能變慢，或檢查資料庫健康狀態時使用。
執行需求
需要 Render MCP 工具（優先）或作為備援的 Render CLI；透過 Render OAuth 或 Render API 金鑰進行驗證，並需選定工作區。指標與資料庫查詢必須使用 MCP 伺服器。不含指令碼，僅有指示文件與一份參考文件。

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

來源與署名

來源：render-oss/render-plugin-claude-code位於skills/render-monitor提交e8f8893

授權條款： MIT

內容歸原作者所有。SourceWeft 從公開儲存庫中收錄這些內容。

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