render-debug

द्वारा openai

लॉग, मेट्रिक्स और डेटाबेस स्थिति का विश्लेषण करके विफल Render डिप्लॉयमेंट को डीबग करता है। त्रुटियों (गुम एनवी वेरिएबल, पोर्ट बाइंडिंग, OOM, आदि) की पहचान करता है और सुझाव देता है…

npx skills add https://github.com/openai/plugins --skill render-debug

ZIP डाउनलोड करें GitHub

Debug Render Deployments

Analyze deployment failures using logs, metrics, and database queries. Identify root causes and apply fixes.

When to Use This Skill

Activate this skill when:

Deployment fails on Render
Service won't start or keeps crashing
User mentions errors, logs, or debugging
Health checks are timing out
Application errors in production
Performance issues (slow responses)
Database connection problems

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 logs and deploy status; metrics and structured 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().

Debugging Workflow

Step 1: Identify Failed Service

list_services()

If MCP isn't configured, ask whether to set it up (preferred) or continue with CLI. Then proceed.

Look for services with failed status. Get details:

get_service(serviceId: "<id>")

Step 2: Retrieve Logs

Build/Deploy Logs (most failures):

list_logs(resource: ["<service-id>"], type: ["build"], limit: 200)

Runtime Error Logs:

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

Search for Specific Errors:

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

HTTP Error Logs:

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

Step 3: Analyze Error Patterns

Match log errors against known patterns:

Error	Log Pattern	Common Fix
MISSING_ENV_VAR	`KeyError`, `not defined`	Add to render.yaml or `update_environment_variables`
PORT_BINDING	`EADDRINUSE`	Use `0.0.0.0:$PORT`
MISSING_DEPENDENCY	`Cannot find module`	Add to package.json/requirements.txt
DATABASE_CONNECTION	`ECONNREFUSED :5432`	Check DATABASE_URL, DB status
HEALTH_CHECK	`Health check timeout`	Add /health endpoint, check port binding
OUT_OF_MEMORY	`heap out of memory`, exit 137	Optimize memory or upgrade plan
BUILD_FAILURE	`Command failed`	Fix build command or dependencies

Full error catalog: references/error-patterns.md

If errors repeat across deploys: Switch from incremental fixes to a broader sweep. Scan the codebase/config for all likely causes in that error class (related env vars, build config, dependencies, or type errors) and address them together before the next redeploy.

Step 4: Check Metrics (Performance Issues)

For crashes, slow responses, or resource issues:

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

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

Detailed metrics guide: references/metrics-debugging.md

Step 5: Debug Database Issues

For database-related errors:

# Check database status
list_postgres_instances()

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

# Query directly
query_render_postgres(
  postgresId: "<postgres-id>",
  sql: "SELECT state, count(*) FROM pg_stat_activity GROUP BY state"
)

Detailed database guide: references/database-debugging.md

Step 6: Apply Fix

For environment variables:

update_environment_variables(
  serviceId: "<service-id>",
  envVars: [{"key": "MISSING_VAR", "value": "value"}]
)

For code changes:

Edit the source file
Commit and push
Deploy triggers automatically (if auto-deploy enabled)

Step 7: Verify Fix

# Check deploy status
list_deploys(serviceId: "<service-id>", limit: 1)

# Check for new errors
list_logs(resource: ["<service-id>"], level: ["error"], limit: 20)

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

Quick Workflows

Pre-built debugging sequences for common scenarios:

Scenario	Workflow
Deploy failed	`list_deploys` → `list_logs(type: build)` → fix → redeploy
App crashing	`list_logs(level: error)` → `get_metrics(memory)` → fix
App slow	`get_metrics(http_latency)` → `get_metrics(cpu)` → `query_postgres`
DB connection	`list_postgres` → `get_metrics(connections)` → `query_postgres`
Post-deploy check	`list_deploys` → `list_logs(error)` → `get_metrics`

Detailed workflows: references/quick-workflows.md

Quick Reference

MCP Tools

# Service Discovery
list_services()
get_service(serviceId: "<id>")
list_postgres_instances()

# Logs
list_logs(resource: ["<id>"], level: ["error"], limit: 100)
list_logs(resource: ["<id>"], type: ["build"], limit: 200)
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)

# Database
query_render_postgres(postgresId: "<id>", sql: "SELECT ...")

# Deployments
list_deploys(serviceId: "<id>", limit: 5)

# Environment Variables
update_environment_variables(serviceId: "<id>", envVars: [{key, value}])

CLI Commands (Fallback)

render services -o json
render logs -r <service-id> --level error -o json
render logs -r <service-id> --tail -o text
render deploys create <service-id> --wait