render-debug

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>):

{
  "servers": {
    "render": {
      "type": "http",
      "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:

export RENDER_API_KEY="<YOUR_API_KEY>"

3. Add the MCP server with the Codex CLI:

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

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:

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

---

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

• Error patterns: references/error-patterns.md
• Metrics debugging: references/metrics-debugging.md
• Database debugging: references/database-debugging.md
• Quick workflows: references/quick-workflows.md
• Log analysis: references/log-analysis.md
• Troubleshooting: references/troubleshooting.md

Related Skills

• deploy: Deploy new applications to Render
• monitor: Ongoing service health monitoring