Airflow Operations

Use af commands to query, manage, and troubleshoot Airflow workflows.

Astro CLI

The Astro CLI is the recommended way to run Airflow locally and deploy to production. It provides a containerized Airflow environment that works out of the box:

# Initialize a new project
astro dev init

# Start local Airflow (webserver at http://localhost:8080)
astro dev start

# Parse DAGs to catch errors quickly (no need to start Airflow)
astro dev parse

# Run pytest against your DAGs
astro dev pytest

# Deploy to production
astro deploy            # Full deploy (image + DAGs)
astro deploy --dags     # DAG-only deploy (fast, no image build)

For more details:

• New project? See the setting-up-astro-project skill
• Local environment? See the managing-astro-local-env skill
• Deploying? See the deploying-airflow skill

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Running the CLI

Run all af commands using uvx (no installation required):

uvx --from astro-airflow-mcp af <command>

Throughout this document, af is shorthand for uvx --from astro-airflow-mcp af.

Instance Configuration

Manage multiple Airflow instances with persistent configuration:

# Add a new instance
af instance add prod --url https://airflow.example.com --token "$API_TOKEN"
af instance add staging --url https://staging.example.com --username admin --password admin

# List and switch instances
af instance list      # Shows all instances in a table
af instance use prod  # Switch to prod instance
af instance current   # Show current instance
af instance delete old-instance

# Auto-discover instances (use --dry-run to preview first)
af instance discover --dry-run        # Preview all discoverable instances
af instance discover                  # Discover from all backends (astro, local)
af instance discover astro            # Discover Astro deployments only
af instance discover astro --all-workspaces  # Include all accessible workspaces
af instance discover local            # Scan common local Airflow ports
af instance discover local --scan     # Deep scan all ports 1024-65535

# IMPORTANT: Always run with --dry-run first and ask for user consent before
# running discover without it. The non-dry-run mode creates API tokens in
# Astro Cloud, which is a sensitive action that requires explicit approval.

# Override instance for a single command
af --instance staging dags list

Config file: ~/.af/config.yaml (override with --config or AF_CONFIG env var)

Tokens in config can reference environment variables using ${VAR} syntax:

instances:
- name: prod
  url: https://airflow.example.com
  auth:
    token: ${AIRFLOW_API_TOKEN}

Or use environment variables directly (no config file needed):

export AIRFLOW_API_URL=http://localhost:8080
export AIRFLOW_AUTH_TOKEN=your-token-here
# Or username/password:
export AIRFLOW_USERNAME=admin
export AIRFLOW_PASSWORD=admin

Or CLI flags: af --airflow-url http://localhost:8080 --token "$TOKEN" <command>

Quick Reference

Command	Description
af health	System health check
af dags list	List all DAGs
af dags get <dag_id>	Get DAG details
af dags explore <dag_id>	Full DAG investigation
af dags source <dag_id>	Get DAG source code
af dags pause <dag_id>	Pause DAG scheduling
af dags unpause <dag_id>	Resume DAG scheduling
af dags errors	List import errors
af dags warnings	List DAG warnings
af dags stats	DAG run statistics
af runs list	List DAG runs
af runs get <dag_id> <run_id>	Get run details
af runs trigger <dag_id>	Trigger a DAG run
af runs trigger-wait <dag_id>	Trigger and wait for completion
af runs delete <dag_id> <run_id>	Permanently delete a DAG run
af runs clear <dag_id> <run_id>	Clear a run for re-execution
af runs diagnose <dag_id> <run_id>	Diagnose failed run
af tasks list <dag_id>	List tasks in DAG
af tasks get <dag_id> <task_id>	Get task definition
af tasks instance <dag_id> <run_id> <task_id>	Get task instance
af tasks logs <dag_id> <run_id> <task_id>	Get task logs
af config version	Airflow version
af config show	Full configuration
af config connections	List connections
af config variables	List variables
af config variable <key>	Get specific variable
af config pools	List pools
af config pool <name>	Get pool details
af config plugins	List plugins
af config providers	List providers
af config assets	List assets/datasets
af api <endpoint>	Direct REST API access
af api ls	List available API endpoints
af api ls --filter X	List endpoints matching pattern

User Intent Patterns

Getting Started

• "How do I run Airflow locally?" / "Set up Airflow" -> use the managing-astro-local-env skill (uses Astro CLI)
• "Create a new Airflow project" / "Initialize project" -> use the setting-up-astro-project skill (uses Astro CLI)
• "How do I install Airflow?" / "Get started with Airflow" -> use the setting-up-astro-project skill

DAG Operations

• "What DAGs exist?" / "List all DAGs" -> af dags list
• "Tell me about DAG X" / "What is DAG Y?" -> af dags explore <dag_id>
• "What's the schedule for DAG X?" -> af dags get <dag_id>
• "Show me the code for DAG X" -> af dags source <dag_id>
• "Stop DAG X" / "Pause this workflow" -> af dags pause <dag_id>
• "Resume DAG X" -> af dags unpause <dag_id>
• "Are there any DAG errors?" -> af dags errors
• "Create a new DAG" / "Write a pipeline" -> use the authoring-dags skill

Run Operations

• "What runs have executed?" -> af runs list
• "Run DAG X" / "Trigger the pipeline" -> af runs trigger <dag_id>
• "Run DAG X and wait" -> af runs trigger-wait <dag_id>
• "Why did this run fail?" -> af runs diagnose <dag_id> <run_id>
• "Delete this run" / "Remove stuck run" -> af runs delete <dag_id> <run_id>
• "Clear this run" / "Retry this run" / "Re-run this" -> af runs clear <dag_id> <run_id>
• "Test this DAG and fix if it fails" -> use the testing-dags skill

Task Operations

• "What tasks are in DAG X?" -> af tasks list <dag_id>
• "Get task logs" / "Why did task fail?" -> af tasks logs <dag_id> <run_id> <task_id>
• "Full root cause analysis" / "Diagnose and fix" -> use the debugging-dags skill

Data Operations

• "Is the data fresh?" / "When was this table last updated?" -> use the checking-freshness skill
• "Where does this data come from?" -> use the tracing-upstream-lineage skill
• "What depends on this table?" / "What breaks if I change this?" -> use the tracing-downstream-lineage skill

Deployment Operations

• "Deploy my DAGs" / "Push to production" -> use the deploying-airflow skill
• "Set up CI/CD" / "Automate deploys" -> use the deploying-airflow skill
• "Deploy to Kubernetes" / "Set up Helm" -> use the deploying-airflow skill
• "astro deploy" / "DAG-only deploy" -> use the deploying-airflow skill

System Operations

• "What version of Airflow?" -> af config version
• "What connections exist?" -> af config connections
• "Are pools full?" -> af config pools
• "Is Airflow healthy?" -> af health

API Exploration

• "What API endpoints are available?" -> af api ls
• "Find variable endpoints" -> af api ls --filter variable
• "Access XCom values" / "Get XCom" -> af api xcom-entries -F dag_id=X -F task_id=Y
• "Get event logs" / "Audit trail" -> af api event-logs -F dag_id=X
• "Create connection via API" -> af api connections -X POST --body '{...}'
• "Create variable via API" -> af api variables -X POST -F key=name -f value=val

Common Workflows

Validate DAGs Before Deploying

If you're using the Astro CLI, you can validate DAGs without a running Airflow instance:

# Parse DAGs to catch import errors and syntax issues
astro dev parse

# Run unit tests
astro dev pytest

Otherwise, validate against a running instance:

af dags errors     # Check for parse/import errors
af dags warnings   # Check for deprecation warnings

Investigate a Failed Run

# 1. List recent runs to find failure
af runs list --dag-id my_dag

# 2. Diagnose the specific run
af runs diagnose my_dag manual__2024-01-15T10:00:00+00:00

# 3. Get logs for failed task (from diagnose output)
af tasks logs my_dag manual__2024-01-15T10:00:00+00:00 extract_data

# 4. After fixing, clear the run to retry all tasks
af runs clear my_dag manual__2024-01-15T10:00:00+00:00

Morning Health Check

# 1. Overall system health
af health

# 2. Check for broken DAGs
af dags errors

# 3. Check pool utilization
af config pools

Understand a DAG

# Get comprehensive overview (metadata + tasks + source)
af dags explore my_dag

Check Why DAG Isn't Running

# Check if paused
af dags get my_dag

# Check for import errors
af dags errors

# Check recent runs
af runs list --dag-id my_dag

Trigger and Monitor

# Option 1: Trigger and wait (blocking)
af runs trigger-wait my_dag --timeout 1800

# Option 2: Trigger and check later
af runs trigger my_dag
af runs get my_dag <run_id>

Output Format

All commands output JSON (except instance commands which use human-readable tables):

af dags list
# {
#   "total_dags": 5,
#   "returned_count": 5,
#   "dags": [...]
# }

Use jq for filtering:

# Find failed runs
af runs list | jq '.dag_runs[] | select(.state == "failed")'

# Get DAG IDs only
af dags list | jq '.dags[].dag_id'

# Find paused DAGs
af dags list | jq '[.dags[] | select(.is_paused == true)]'

Task Logs Options

# Get logs for specific retry attempt
af tasks logs my_dag run_id task_id --try 2

# Get logs for mapped task index
af tasks logs my_dag run_id task_id --map-index 5

Direct API Access with af api

Use af api for endpoints not covered by high-level commands (XCom, event-logs, backfills, etc).

# Discover available endpoints
af api ls
af api ls --filter variable

# Basic usage
af api dags
af api dags -F limit=10 -F only_active=true
af api variables -X POST -F key=my_var -f value="my value"
af api variables/old_var -X DELETE

Field syntax: -F key=value auto-converts types, -f key=value keeps as string.

Full reference: See api-reference.md for all options, common endpoints (XCom, event-logs, backfills), and examples.

Related Skills

Skill	Use when...
authoring-dags	Creating or editing DAG files with best practices
testing-dags	Iterative test -> debug -> fix -> retest cycles
debugging-dags	Deep root cause analysis and failure diagnosis
checking-freshness	Checking if data is up to date or stale
tracing-upstream-lineage	Finding where data comes from