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:

•Get a Render API key:

code

https://dashboard.render.com/u/*/settings#api-keys

•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>"
      }
    }
  }
}

•Restart Cursor, then retry list_services().

Claude Code

Walk the user through these steps:

•Get a Render API key:

code

https://dashboard.render.com/u/*/settings#api-keys

•Add the MCP server with Claude Code (replace <YOUR_API_KEY>):

bash

claude mcp add --transport http render https://mcp.render.com/mcp --header "Authorization: Bearer <YOUR_API_KEY>"

•Restart Claude Code, then retry list_services().

Codex

Walk the user through these steps:

•Get a Render API key:

code

https://dashboard.render.com/u/*/settings#api-keys

•Set it in their shell:

bash

export RENDER_API_KEY="<YOUR_API_KEY>"

•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

•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:

code

Set my Render workspace to [WORKSPACE_NAME]

Debugging Workflow

Step 1: Identify Failed Service

code

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:

code

get_service(serviceId: "<id>")

Step 2: Retrieve Logs

Build/Deploy Logs (most failures):

code

list_logs(resource: ["<service-id>"], type: ["build"], limit: 200)

Runtime Error Logs:

code

list_logs(resource: ["<service-id>"], level: ["error"], limit: 100)

Search for Specific Errors:

code

list_logs(resource: ["<service-id>"], text: ["KeyError", "ECONNREFUSED"], limit: 50)

HTTP Error Logs:

code

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:

code

get_metrics(
  resourceId: "<service-id>",
  metricTypes: ["cpu_usage", "memory_usage", "memory_limit"]
)

code

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:

code

# 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:

code

update_environment_variables(
  serviceId: "<service-id>",
  envVars: [{"key": "MISSING_VAR", "value": "value"}]
)

For code changes:

•Edit the source file
•Commit and push
•Deploy triggers automatically (if auto-deploy enabled)

Step 7: Verify Fix

code

# 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

code

# 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
•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