render-debug
$
npx mdskill add openai/plugins/render-debugDiagnose Render deployment failures using logs, metrics, and database state.
- Resolves missing env vars, port binding, OOM, and startup crashes.
- Depends on Render MCP tools or CLI for data access.
- Analyzes logs and metrics to pinpoint root causes automatically.
- Delivers specific fixes and actionable recommendations to users.
SKILL.md
.github/skills/render-debugView on GitHub ↗
---
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 `<YOUR_API_KEY>`):
```json
{
"mcpServers": {
"render": {
"url": "https://mcp.render.com/mcp",
"headers": {
"Authorization": "Bearer <YOUR_API_KEY>"
}
}
}
}
```
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="<YOUR_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: "<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](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](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](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:
| 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: "<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)
```bash
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
- **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
More from openai/plugins
- accessibility-and-inclusive-visualizationMake data visualizations accessible and inclusive. Use when the user needs chart or diagram accessibility guidance, text alternatives for complex visuals, color and contrast review, keyboard support, reduced-motion behavior for animation or parallax, or an accessibility QA workflow for exported figures, UML-like diagrams, and dashboards.
- agent-browserBrowser automation CLI for AI agents. Use when the user needs to interact with websites, verify dev server output, test web apps, navigate pages, fill forms, click buttons, take screenshots, extract data, or automate any browser task. Also triggers when a dev server starts so you can verify it visually.
- agent-browser-verifyAutomated browser verification for dev servers. Triggers when a dev server starts to run a visual gut-check with agent-browser — verifies the page loads, checks for console errors, validates key UI elements, and reports pass/fail before continuing.
- agents-sdkBuild AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, or chat applications. Covers Agent class, state management, callable RPC, Workflows integration, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
- ai-elementsAI Elements component library guidance — pre-built React components for AI interfaces built on shadcn/ui. Use when building chat UIs, message displays, tool call rendering, streaming responses, reasoning panels, or any AI-native interface with the AI SDK.
- ai-gatewayVercel AI Gateway expert guidance. Use when configuring model routing, provider failover, cost tracking, or managing multiple AI providers through a unified API.
- ai-generation-persistenceAI generation persistence patterns — unique IDs, addressable URLs, database storage, and cost tracking for every LLM generation
- ai-sdkVercel AI SDK expert guidance. Use when building AI-powered features — chat interfaces, text generation, structured output, tool calling, agents, MCP integration, streaming, embeddings, reranking, image generation, or working with any LLM provider.
- aiq-deploy|
- aiq-research|