render-monitor

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Original

English

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Translation

Chinese

Monitor Render Services

监控Render服务

Real-time monitoring of Render services including health checks, performance metrics, and logs.

实时监控Render服务，包括健康检查、性能指标和日志。

When to Use This Skill

何时使用此技能

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

当用户有以下需求时激活此技能：

检查服务是否健康
查看性能指标
监控日志
验证部署是否正常运行
排查性能缓慢问题
检查数据库健康状态

Prerequisites

前提条件

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()

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工具（推荐）： 使用

list_services()

测试 - 提供结构化数据

CLI（备选）：

render --version

- 当MCP工具不可用时使用

身份验证： 对于MCP，使用API密钥（在MCP配置中设置，或通过

RENDER_API_KEY

环境变量，具体取决于工具）。对于CLI，使用

render whoami -o json

验证。

工作区：

get_selected_workspace()

或

render workspace current -o json

注意： MCP工具需要Render MCP服务器。如果不可用，使用CLI查看状态和日志；指标和数据库查询需要MCP。

MCP Setup (Per Tool)

按工具配置MCP

list_services()

fails because MCP isn't configured, guide the user to set up the hosted Render MCP server. Ask which AI tool they're using, then provide the matching instructions below. Always use their API key.

如果

list_services()

因未配置MCP而失败，引导用户设置托管的Render MCP服务器。询问用户使用的AI工具，然后提供以下对应说明。请始终使用他们的API密钥。

Cursor

Walk the user through these steps:

Get a Render API key:

https://dashboard.render.com/u/*/settings#api-keys

Add this to
```
~/.cursor/mcp.json
```
(replace
```
<YOUR_API_KEY>
```
):

json

{
  "mcpServers": {
    "render": {
      "url": "https://mcp.render.com/mcp",
      "headers": {
        "Authorization": "Bearer <YOUR_API_KEY>"
      }
    }
  }
}

Restart Cursor, then retry
```
list_services()
```
.

引导用户完成以下步骤：

获取Render API密钥：

https://dashboard.render.com/u/*/settings#api-keys

将以下内容添加到
```
~/.cursor/mcp.json
```
（替换
```
<YOUR_API_KEY>
```
）：

json

{
  "mcpServers": {
    "render": {
      "url": "https://mcp.render.com/mcp",
      "headers": {
        "Authorization": "Bearer <YOUR_API_KEY>"
      }
    }
  }
}

重启Cursor，然后重试
```
list_services()
```
。

Claude Code

Walk the user through these steps:

Get a Render API key:

https://dashboard.render.com/u/*/settings#api-keys

Add the MCP server with Claude Code (replace
```
<YOUR_API_KEY>
```
):

bash

claude mcp add --transport http render https://mcp.render.com/mcp --header "Authorization: Bearer <YOUR_API_KEY>"

Restart Claude Code, then retry
```
list_services()
```
.

引导用户完成以下步骤：

获取Render API密钥：

https://dashboard.render.com/u/*/settings#api-keys

使用Claude Code添加MCP服务器（替换
```
<YOUR_API_KEY>
```
）：

bash

claude mcp add --transport http render https://mcp.render.com/mcp --header "Authorization: Bearer <YOUR_API_KEY>"

重启Claude Code，然后重试
```
list_services()
```
。

Codex

Walk the user through these steps:

Get a Render API key:

https://dashboard.render.com/u/*/settings#api-keys

Set it in their shell:

bash

export RENDER_API_KEY="<YOUR_API_KEY>"

Add the MCP server with the Codex CLI:

bash

codex mcp add render --url https://mcp.render.com/mcp --bearer-token-env-var RENDER_API_KEY

Restart Codex, then retry
```
list_services()
```
.

引导用户完成以下步骤：

获取Render API密钥：

https://dashboard.render.com/u/*/settings#api-keys

在Shell中设置：

bash

export RENDER_API_KEY="<YOUR_API_KEY>"

使用Codex CLI添加MCP服务器：

bash

codex mcp add render --url https://mcp.render.com/mcp --bearer-token-env-var RENDER_API_KEY

重启Codex，然后重试
```
list_services()
```
。

Other Tools

其他工具

If the user is on another AI app, direct them to the Render MCP docs for that tool's setup steps and install method.

如果用户使用其他AI应用，引导他们查看Render MCP文档中对应工具的设置步骤和安装方法。

Workspace Selection

工作区选择

After MCP is configured, have the user set the active Render workspace with a prompt like:

Set my Render workspace to [WORKSPACE_NAME]

配置MCP后，让用户通过如下提示设置活跃的Render工作区：

将我的Render工作区设置为[WORKSPACE_NAME]

Quick Health Check

快速健康检查

Run these 5 checks to assess service health:

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运行以下5项检查来评估服务健康状态：

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1. Check service status

1. 检查服务状态

list_services()

2. Check latest deploy

2. 检查最新部署

list_deploys(serviceId: "<service-id>", limit: 1)

3. Check for errors

3. 检查错误

list_logs(resource: ["<service-id>"], level: ["error"], limit: 20)

4. Check resource usage

4. 检查资源使用情况

get_metrics(resourceId: "<service-id>", metricTypes: ["cpu_usage", "memory_usage"])

5. Check latency

5. 检查延迟

get_metrics(resourceId: "<service-id>", metricTypes: ["http_latency"], httpLatencyQuantile: 0.95)

---

get_metrics(resourceId: "<service-id>", metricTypes: ["http_latency"], httpLatencyQuantile: 0.95)

---

Service Health

服务健康状态

Check Status

检查状态

list_services()

get_service(serviceId: "<id>")

list_services()

get_service(serviceId: "<id>")

Check Deployments

检查部署

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_deploys(serviceId: "<service-id>", limit: 5)

状态	含义
`live`	部署成功
`build_in_progress`	构建中
`build_failed`	构建失败
`deactivated`	已被新版本部署替代

Check Errors

检查错误

list_logs(resource: ["<service-id>"], level: ["error"], limit: 50)

list_logs(resource: ["<service-id>"], statusCode: ["500", "502", "503"], limit: 50)

list_logs(resource: ["<service-id>"], level: ["error"], limit: 50)

list_logs(resource: ["<service-id>"], statusCode: ["500", "502", "503"], limit: 50)

Performance Metrics

性能指标

CPU & Memory

CPU与内存

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: ["cpu_usage", "memory_usage", "cpu_limit", "memory_limit"]
)

指标	健康状态	警告	严重
CPU	<70%	70-85%	>85%
内存	<80%	80-90%	>90%

HTTP Latency

HTTP延迟

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_latency"],
  httpLatencyQuantile: 0.95
)

p95延迟	状态
<200ms	优秀
200-500ms	良好
500ms-1s	需关注
>1s	存在问题

Request Count

请求数量

get_metrics(
  resourceId: "<service-id>",
  metricTypes: ["http_request_count"]
)

get_metrics(
  resourceId: "<service-id>",
  metricTypes: ["http_request_count"]
)

Filter by Endpoint

按端点过滤

get_metrics(
  resourceId: "<service-id>",
  metricTypes: ["http_latency"],
  httpPath: "/api/users"
)

Detailed metrics guide: references/metrics-guide.md

get_metrics(
  resourceId: "<service-id>",
  metricTypes: ["http_latency"],
  httpPath: "/api/users"
)

详细指标指南：references/metrics-guide.md

Database Monitoring

数据库监控

PostgreSQL Status

PostgreSQL状态

list_postgres_instances()
get_postgres(postgresId: "<postgres-id>")

list_postgres_instances()
get_postgres(postgresId: "<postgres-id>")

Connection Count

连接数

get_metrics(resourceId: "<postgres-id>", metricTypes: ["active_connections"])

get_metrics(resourceId: "<postgres-id>", metricTypes: ["active_connections"])

Query Database

查询数据库

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 state, count(*) FROM pg_stat_activity GROUP BY state"
)

Find Slow Queries

查找慢查询

query_render_postgres(
  postgresId: "<postgres-id>",
  sql: "SELECT query, mean_exec_time FROM pg_stat_statements ORDER BY mean_exec_time DESC LIMIT 10"
)

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

键值存储

list_key_value()
get_key_value(keyValueId: "<kv-id>")

list_key_value()
get_key_value(keyValueId: "<kv-id>")

Log Monitoring

日志监控

Recent Logs

近期日志

list_logs(resource: ["<service-id>"], limit: 100)

list_logs(resource: ["<service-id>"], limit: 100)

Error Logs

错误日志

list_logs(resource: ["<service-id>"], level: ["error"], limit: 50)

list_logs(resource: ["<service-id>"], level: ["error"], limit: 50)

Search Logs

搜索日志

list_logs(resource: ["<service-id>"], text: ["timeout", "error"], limit: 50)

list_logs(resource: ["<service-id>"], text: ["timeout", "error"], limit: 50)

Filter by Time

按时间过滤

list_logs(
  resource: ["<service-id>"],
  startTime: "2024-01-15T10:00:00Z",
  endTime: "2024-01-15T11:00:00Z"
)

list_logs(
  resource: ["<service-id>"],
  startTime: "2024-01-15T10:00:00Z",
  endTime: "2024-01-15T11:00:00Z"
)

Stream Logs (CLI)

流式日志（CLI）

bash

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

bash

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

Quick Reference

快速参考

MCP Tools

MCP工具

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undefined

Services

服务

list_services() get_service(serviceId: "<id>") list_deploys(serviceId: "<id>", limit: 5)

Logs

日志

list_logs(resource: ["<id>"], level: ["error"], limit: 100) list_logs(resource: ["<id>"], text: ["search"], limit: 50)

Metrics

指标

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

数据库

list_postgres_instances() get_postgres(postgresId: "<id>") query_render_postgres(postgresId: "<id>", sql: "SELECT ...") get_metrics(resourceId: "<postgres-id>", metricTypes: ["active_connections"])

Key-Value

键值存储

list_key_value() get_key_value(keyValueId: "<id>")

undefined

list_key_value() get_key_value(keyValueId: "<id>")

undefined

CLI Commands (Fallback)

CLI命令（备选）

Use these if MCP tools are unavailable:

bash

undefined

当MCP工具不可用时使用以下命令：

bash

undefined

Service status

服务状态

render services -o json render services instances <service-id>

Deployments

部署

render deploys list <service-id> -o json

Logs

日志

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

render logs -r <service-id> --tail -o text # 流式日志 render logs -r <service-id> --level error -o json # 错误日志 render logs -r <service-id> --type deploy -o json # 构建日志

Database

数据库

render psql <database-id> # Connect to PostgreSQL

render psql <database-id> # 连接到PostgreSQL

SSH for live debugging

SSH实时调试

render ssh <service-id>

undefined

render ssh <service-id>

undefined

Healthy Service Indicators

健康服务指标

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%

指标	健康状态	警告	严重
部署状态	`live`	`update_in_progress`	`build_failed`
错误率	<0.1%	0.1-1%	>1%
p95延迟	<500ms	500ms-2s	>2s
CPU使用率	<70%	70-90%	>90%
内存使用率	<80%	80-95%	>95%