DAG Testing Skill 🚀 FIRST ACTION: Just Trigger the DAG
When the user asks to test a DAG, your FIRST AND ONLY action should be:
trigger_dag_and_wait(dag_id="
DO NOT:
❌ Call list_dags first ❌ Call get_dag_details first ❌ Call list_import_errors first ❌ Use grep or ls or any bash command ❌ Do any "pre-flight checks"
Just trigger the DAG. If it fails, THEN debug.
⚠️ CRITICAL WARNING: Use MCP Tools, NOT CLI Commands ⚠️
STOP! Before running ANY Airflow-related command, read this.
You MUST use MCP tools for ALL Airflow interactions. CLI commands like astro dev run, airflow dags test, or shell commands to read logs are FORBIDDEN.
Why? MCP tools provide structured, reliable output. CLI commands are fragile, produce unstructured text, and often fail silently.
CLI vs MCP Quick Reference ❌ DO NOT USE ✅ USE INSTEAD astro dev run dags test trigger_dag_and_wait MCP tool airflow dags test trigger_dag_and_wait MCP tool airflow tasks test trigger_dag_and_wait MCP tool cat / grep / tail on logs get_task_logs MCP tool astro dev run dags list list_dags MCP tool Any astro dev run ... Equivalent MCP tool Any airflow ... CLI Equivalent MCP tool ls on /usr/local/airflow/dags/ list_dags or explore_dag MCP tool cat ... | jq to filter MCP results Read the JSON directly from MCP response grep on MCP tool result files Read the JSON directly from MCP response ls on local dags directory Not needed — just trigger the DAG pwd to check directory Not needed — just trigger the DAG
Remember:
✅ Airflow is ALREADY running — the MCP server handles the connection ✅ Just call trigger_dag_and_wait — don't check anything first ❌ Do NOT call list_dags before testing — just trigger ❌ Do NOT use shell commands (ls, grep, cat, pwd) ❌ Do NOT use bash to parse or filter MCP tool results ❌ Do NOT do "pre-flight checks" — try first, debug on failure Testing Workflow Overview ┌─────────────────────────────────────┐ │ 1. TRIGGER AND WAIT │ │ Run DAG, wait for completion │ └─────────────────────────────────────┘ ↓ ┌───────┴───────┐ ↓ ↓ ┌─────────┐ ┌──────────┐ │ SUCCESS │ │ FAILED │ │ Done! │ │ Debug... │ └─────────┘ └──────────┘ ↓ ┌─────────────────────────────────────┐ │ 2. DEBUG (only if failed) │ │ Get logs, identify root cause │ └─────────────────────────────────────┘ ↓ ┌─────────────────────────────────────┐ │ 3. FIX AND RETEST │ │ Apply fix, restart from step 1 │ └─────────────────────────────────────┘
Philosophy: Try first, debug on failure. Don't waste time on pre-flight checks — just run the DAG and diagnose if something goes wrong.
Phase 1: Trigger and Wait
Use trigger_dag_and_wait to test the DAG:
Primary Method: Trigger and Wait
MCP tool: trigger_dag_and_wait(dag_id="your_dag_id", timeout=300)
trigger_dag_and_wait( dag_id="my_dag", conf={}, # Optional: pass config to the DAG timeout=300 # Wait up to 5 minutes (adjust as needed) )
Why this is the preferred method:
Single tool call handles trigger + monitoring Returns immediately when DAG completes (success or failure) Includes failed task details if run fails No manual polling required Response Interpretation
Success:
{ "dag_run": { "dag_id": "my_dag", "dag_run_id": "manual__2025-01-14T...", "state": "success", "start_date": "...", "end_date": "..." }, "timed_out": false, "elapsed_seconds": 45.2 }
Failure:
{ "dag_run": { "state": "failed" }, "timed_out": false, "elapsed_seconds": 30.1, "failed_tasks": [ { "task_id": "extract_data", "state": "failed", "try_number": 2 } ] }
Timeout:
{ "dag_id": "my_dag", "dag_run_id": "manual__...", "state": "running", "timed_out": true, "elapsed_seconds": 300.0, "message": "Timed out after 300 seconds. DAG run is still running." }
Alternative: Trigger and Monitor Separately
Use this only when you need more control:
Step 1: Trigger
trigger_dag(dag_id="my_dag", conf={})
Returns:
Step 2: Check status
get_dag_run(dag_id="my_dag", dag_run_id="manual__...")
Returns current state
Handling Results If Success
The DAG ran successfully. Summarize for the user:
Total elapsed time Number of tasks completed Any notable outputs (if visible in logs)
You're done!
If Timed Out
The DAG is still running. Options:
Check current status with get_dag_run(dag_id, dag_run_id) Ask user if they want to continue waiting Increase timeout and try again If Failed
Move to Phase 2 (Debug) to identify the root cause.
Phase 2: Debug Failures (Only If Needed)
When a DAG run fails, use these tools to diagnose:
Get Comprehensive Diagnosis
MCP tool: diagnose_dag_run(dag_id, dag_run_id)
Returns in one call:
Run metadata (state, timing) All task instances with states Summary of failed tasks State counts (success, failed, skipped, etc.) Get Task Logs
MCP tool: get_task_logs(dag_id, dag_run_id, task_id)
get_task_logs( dag_id="my_dag", dag_run_id="manual__2025-01-14T...", task_id="extract_data", try_number=1 # Which attempt (1 = first try) )
Look for:
Exception messages and stack traces Connection errors (database, API, S3) Permission errors Timeout errors Missing dependencies Check Upstream Tasks
If a task shows upstream_failed, the root cause is in an upstream task. Use diagnose_dag_run to find which task actually failed.
Check Import Errors (If DAG Didn't Run)
If the trigger failed because the DAG doesn't exist:
MCP tool: list_import_errors
This reveals syntax errors or missing dependencies that prevented the DAG from loading.
Phase 3: Fix and Retest
Once you identify the issue:
Common Fixes Issue Fix Missing import Add to DAG file Missing package Add to requirements.txt Connection error Check list_connections, verify credentials Variable missing Check list_variables, create if needed Timeout Increase task timeout or optimize query Permission error Check credentials in connection After Fixing Save the file Retest: trigger_dag_and_wait(dag_id)
Repeat the test → debug → fix loop until the DAG succeeds.
MCP Tools Quick Reference Phase Tool Purpose Test trigger_dag_and_wait Primary test method — start here Test trigger_dag Start run (alternative) Test get_dag_run Check run status Debug diagnose_dag_run Comprehensive failure diagnosis Debug get_task_logs Get task output/errors Debug list_import_errors Check for parse errors (if DAG won't load) Debug get_dag_details Verify DAG config Debug explore_dag Full DAG inspection Testing Scenarios Scenario 1: Test a DAG (Happy Path) 1. trigger_dag_and_wait(dag_id) → Run and wait 2. Success! Done.
Scenario 2: Test a DAG (With Failure) 1. trigger_dag_and_wait(dag_id) → Run and wait 2. Failed → diagnose_dag_run → Find failed tasks 3. get_task_logs → Get error details 4. [Fix the issue] 5. trigger_dag_and_wait(dag_id) → Retest
Scenario 3: DAG Doesn't Exist / Won't Load 1. trigger_dag_and_wait(dag_id) → Error: DAG not found 2. list_import_errors → Find parse error 3. [Fix the issue] 4. trigger_dag_and_wait(dag_id) → Retest
Scenario 4: Debug a Failed Scheduled Run 1. diagnose_dag_run(dag_id, dag_run_id) → Get failure summary 2. get_task_logs(dag_id, dag_run_id, failed_task_id) → Get error 3. [Fix the issue] 4. trigger_dag_and_wait(dag_id) → Retest
Scenario 5: Test with Custom Configuration 1. trigger_dag_and_wait( dag_id="my_dag", conf={"env": "staging", "batch_size": 100}, timeout=600 ) 2. [Analyze results]
Scenario 6: Long-Running DAG 1. trigger_dag_and_wait(dag_id, timeout=3600) → Wait up to 1 hour 2. [If timed out] get_dag_run(dag_id, dag_run_id) → Check current state
Debugging Tips Common Error Patterns
Connection Refused / Timeout:
Check list_connections for correct host/port Verify network connectivity to external system Check if connection credentials are correct
ModuleNotFoundError:
Package missing from requirements.txt After adding, may need environment restart
PermissionError:
Check IAM roles, database grants, API keys Verify connection has correct credentials
Task Timeout:
Query or operation taking too long Consider adding timeout parameter to task Optimize underlying query/operation Reading Task Logs
Task logs typically show:
Task start timestamp Any print/log statements from task code Return value (for @task decorated functions) Exception + full stack trace (if failed) Task end timestamp and duration
Focus on the exception at the bottom of failed task logs.
Related Skills authoring-dags: For creating new DAGs (includes validation before testing) debugging-dags: For general Airflow troubleshooting