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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.
npx claudepluginhub robinebers/converted-plugins --plugin renderHow this skill is triggered — by the user, by Claude, or both
Slash command
/render:render-debugThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Analyze deployment failures using logs, metrics, and database queries. Identify root causes and apply fixes.
Mandates invoking relevant skills via tools before any response in coding sessions. Covers access, priorities, and adaptations for Claude Code, Copilot CLI, Gemini CLI.
Share bugs, ideas, or general feedback.
Analyze deployment failures using logs, metrics, and database queries. Identify root causes and apply fixes.
Activate this skill when:
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.
If list_services() fails because MCP isn't configured, ask whether they want to set up MCP (preferred) or continue with the CLI fallback. If they choose MCP, ask which AI tool they're using, then provide the matching instructions below. Always use their API key.
Walk the user through these steps:
https://dashboard.render.com/u/*/settings#api-keys
~/.cursor/mcp.json (replace <YOUR_API_KEY>):{
"mcpServers": {
"render": {
"url": "https://mcp.render.com/mcp",
"headers": {
"Authorization": "Bearer <YOUR_API_KEY>"
}
}
}
}
list_services().Walk the user through these steps:
https://dashboard.render.com/u/*/settings#api-keys
export RENDER_API_KEY="<YOUR_API_KEY>"
codex mcp add render --url https://mcp.render.com/mcp --bearer-token-env-var RENDER_API_KEY
list_services().If the user is on another AI app, direct them to the Render MCP docs for that tool's setup steps and install method.
After MCP is configured, have the user set the active Render workspace with a prompt like:
Set my Render workspace to [WORKSPACE_NAME]
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>")
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)
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.
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
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
For environment variables:
update_environment_variables(
serviceId: "<service-id>",
envVars: [{"key": "MISSING_VAR", "value": "value"}]
)
For code changes:
# 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"])
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
# 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}])
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