Agent Skills
› fanfan-de/anybox
› render-monitor
render-monitor
GitHub用于实时监控 Render 服务，涵盖健康检查、性能指标（CPU/内存/延迟）、日志及部署状态验证。支持通过 MCP 工具或 CLI 获取结构化数据与详细报告，辅助排查故障与评估系统健康状况。
Trigger Scenarios
检查服务是否健康
查看性能指标
监控日志
验证部署是否成功
调查性能缓慢问题
检查数据库健康
Install
npx skills add fanfan-de/anybox --skill render-monitor -g -y
SKILL.md
Frontmatter
{
"name": "render-monitor",
"license": "MIT",
"metadata": {
"author": "Render",
"version": "1.0.0",
"category": "monitoring"
},
"description": "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.",
"compatibility": "Requires Render MCP tools or CLI"
}
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: 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.
MCP Setup
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:
- 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
Version History
- 08dc189 Current 2026-07-05 19:05
