--- name: render-debug description: 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. license: MIT compatibility: Requires Render MCP tools or CLI metadata: author: Render version: "1.1.0" category: debugging --- # 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 (Per Tool) 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. ### Cursor Walk the user through these steps: 1) Get a Render API key: ``` https://dashboard.render.com/u/*/settings#api-keys ``` 2) Add this to `~/.cursor/mcp.json` (replace ``): ```json { "mcpServers": { "render": { "url": "https://mcp.render.com/mcp", "headers": { "Authorization": "Bearer " } } } } ``` 3) Restart Cursor, then retry `list_services()`. ### Codex Walk the user through these steps: 1) Get a Render API key: ``` https://dashboard.render.com/u/*/settings#api-keys ``` 2) Set it in their shell: ```bash export RENDER_API_KEY="" ``` 3) 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 ``` 4) Restart Codex, then retry `list_services()`. ### Other Tools If the user is on another AI app, direct them to the Render MCP docs for that tool's setup steps and install method. ### Workspace Selection After MCP is configured, have the user set the active Render workspace with a prompt like: ``` Set my Render workspace to [WORKSPACE_NAME] ``` --- ## 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: "") ``` ### Step 2: Retrieve Logs **Build/Deploy Logs (most failures):** ``` list_logs(resource: [""], type: ["build"], limit: 200) ``` **Runtime Error Logs:** ``` list_logs(resource: [""], level: ["error"], limit: 100) ``` **Search for Specific Errors:** ``` list_logs(resource: [""], text: ["KeyError", "ECONNREFUSED"], limit: 50) ``` **HTTP Error Logs:** ``` list_logs(resource: [""], 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](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: "", metricTypes: ["cpu_usage", "memory_usage", "memory_limit"] ) ``` ``` get_metrics( resourceId: "", metricTypes: ["http_latency"], httpLatencyQuantile: 0.95 ) ``` Detailed metrics guide: [references/metrics-debugging.md](references/metrics-debugging.md) ### Step 5: Debug Database Issues For database-related errors: ``` # Check database status list_postgres_instances() # Check connections get_metrics(resourceId: "", metricTypes: ["active_connections"]) # Query directly query_render_postgres( postgresId: "", sql: "SELECT state, count(*) FROM pg_stat_activity GROUP BY state" ) ``` Detailed database guide: [references/database-debugging.md](references/database-debugging.md) ### Step 6: Apply Fix **For environment variables:** ``` update_environment_variables( serviceId: "", 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: "", limit: 1) # Check for new errors list_logs(resource: [""], level: ["error"], limit: 20) # Check metrics get_metrics(resourceId: "", 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](references/quick-workflows.md) --- ## Quick Reference ### MCP Tools ``` # Service Discovery list_services() get_service(serviceId: "") list_postgres_instances() # Logs list_logs(resource: [""], level: ["error"], limit: 100) list_logs(resource: [""], type: ["build"], limit: 200) list_logs(resource: [""], text: ["search"], limit: 50) # Metrics get_metrics(resourceId: "", metricTypes: ["cpu_usage", "memory_usage"]) get_metrics(resourceId: "", metricTypes: ["http_latency"], httpLatencyQuantile: 0.95) # Database query_render_postgres(postgresId: "", sql: "SELECT ...") # Deployments list_deploys(serviceId: "", limit: 5) # Environment Variables update_environment_variables(serviceId: "", envVars: [{key, value}]) ``` ### CLI Commands (Fallback) ```bash render services -o json render logs -r --level error -o json render logs -r --tail -o text render deploys create --wait ``` --- ## References - **Error patterns:** [references/error-patterns.md](references/error-patterns.md) - **Metrics debugging:** [references/metrics-debugging.md](references/metrics-debugging.md) - **Database debugging:** [references/database-debugging.md](references/database-debugging.md) - **Quick workflows:** [references/quick-workflows.md](references/quick-workflows.md) - **Log analysis:** [references/log-analysis.md](references/log-analysis.md) - **Troubleshooting:** [references/troubleshooting.md](references/troubleshooting.md) ## Related Skills - **deploy:** Deploy new applications to Render - **monitor:** Ongoing service health monitoring --- Source attribution: openai Original project: https://github.com/openai/plugins/blob/main/plugins/render/skills/render-debug/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: openai Original project: https://github.com/openai/plugins/blob/main/plugins/render/skills/render-debug/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Customer Support Roles: Backend Developer, DevOps / SRE, Technical Support Specialties: Backend, DevOps Tools: Render Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 72268428eec2917201c224f3c1045369ea3575b41b94ff0c896a066f8b643c43