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render-debug

✓ Official★ 4,081

by openai · part of openai/plugins

Debug failed Render deployments by analyzing logs, metrics, and database state. Identifies errors (missing env vars, port binding, OOM, etc.) and suggests fixes. Use when deployments fail, services won't start, or users mention errors, logs, or debugging.

🧩 One of 7 skills in the openai/plugins package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

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

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:

ErrorLog PatternCommon Fix
MISSING_ENV_VARKeyError, not definedAdd to render.yaml or update_environment_variables
PORT_BINDINGEADDRINUSEUse 0.0.0.0:$PORT
MISSING_DEPENDENCYCannot find moduleAdd to package.json/requirements.txt
DATABASE_CONNECTIONECONNREFUSED :5432Check DATABASE_URL, DB status
HEALTH_CHECKHealth check timeoutAdd /health endpoint, check port binding
OUT_OF_MEMORYheap out of memory, exit 137Optimize memory or upgrade plan
BUILD_FAILURECommand failedFix 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:

  1. Edit the source file
  2. Commit and push
  3. 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:

ScenarioWorkflow
Deploy failedlist_deploys → list_logs(type: build) → fix → redeploy
App crashinglist_logs(level: error) → get_metrics(memory) → fix
App slowget_metrics(http_latency) → get_metrics(cpu) → query_postgres
DB connectionlist_postgres → get_metrics(connections) → query_postgres
Post-deploy checklist_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

References

  • render-deploy — Deploy new applications to Render
  • render-monitor — Ongoing service health monitoring
  • render-mcp — MCP server setup and tool catalog