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.
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: If you installed the Render plugin (Cursor, Codex, Claude Code), it provides OAuth for MCP — complete the OAuth prompt. For manual MCP clients, use a Render API key. 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, set up the Render MCP server. For detailed per-tool walkthroughs, see render-mcp.
Plugin setup: If the Render plugin is installed, complete Render OAuth when prompted, then reload your tool and retry list_services().
Manual MCP setup: Add the Render MCP server to your AI tool's MCP config:
https://mcp.render.com/mcpAuthorization: Bearer <YOUR_API_KEY>https://dashboard.render.com/u/*/settings#api-keysAfter configuring, restart your tool and retry list_services(). Then set your workspace with list_workspaces() / get_selected_workspace().
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
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Category:developer