Apache Airflow MCP connector

Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring data workflows through directed acyclic graphs (DAGs). The Apache Airflow MCP connector allows AI agents to manage DAGs and tasks, configure connections to external data sources, and retrieve workflow execution details. It also supports accessing configuration settings, reviewing dag warnings, and inspecting source code for workflow definitions.

Authentication type

• Basic Auth - Requires a username and password to be configured for the agent to access the service.

Uses

Use the Apache Airflow MCP connector to perform the following actions:

• Trigger data pipeline runs on schedule or on-demand across your organization
• Monitor task execution status and troubleshoot workflow failures in real time
• Automate complex multi-step data workflows without writing code
• Manage connections and credentials for all downstream systems in one place
• Track data lineage and task dependencies across hundreds of workflows
• Orchestrate repeating business processes like payroll, reconciliation, and reporting
• Share workflow logs and execution history with stakeholders for compliance audits

Example prompts

Use the following example prompts to invoke Apache Airflow MCP connector tools from your AI assistant or Boomi Connect workflow:

• Show me all Apache Airflow workflows that failed yesterday.
• Trigger the customer data sync workflow in Apache Airflow right now.
• List all active connections configured in Apache Airflow.
• Get the execution logs for the last run of the monthly reconciliation workflow in Apache Airflow.
• Create a new database connection in Apache Airflow for the data warehouse.
• Check which Apache Airflow tasks are currently running across all workflows.
• Update the status of a failed task in Apache Airflow so the workflow can retry.
• Show me all Apache Airflow workflows scheduled to run tomorrow.
• Delete the test workflow from Apache Airflow production.
• Add a note to the last workflow execution in Apache Airflow explaining why it failed.

Apache Airflow MCP connector tools

The Apache Airflow MCP connector provides the following tools. Each tool maps to a specific action you can invoke from your AI agent or automation.

Tool	Description
get_config	Retrieves the current Airflow configuration settings and values.
get_connections	Lists all available connections configured in Airflow.
post_connection	Creates a new connection with the specified credentials and details.
test_connection	Validates that a connection can successfully establish communication.
delete_connection	Removes a connection from Airflow configuration.
get_connection	Retrieves detailed information about a specific connection.
patch_connection	Modifies the settings and credentials of an existing connection.
get_dag_source	Retrieves the source code for a specific DAG.
get_dag_warnings	Lists all warnings and validation issues for a DAG.
get_dags	Lists all directed acyclic graphs available in Airflow.
patch_dags	Updates settings for multiple DAGs at once.
delete_dag	Removes a DAG and its associated data from Airflow.
get_dag	Retrieves basic information about a specific DAG.
patch_dag	Modifies the configuration or properties of a DAG.
post_clear_task_instances	Clears the execution state of specified task instances.
get_dag_runs	Lists all execution runs for a specific DAG.
post_dag_run	Triggers a new execution of a DAG.
delete_dag_run	Removes a specific DAG run and its data.
get_dag_run	Retrieves detailed information about a specific DAG run.
update_dag_run_state	Changes the execution state of a DAG run.
clear_dag_run	Clears all task instances within a DAG run.
set_dag_run_note	Adds or updates a note associated with a DAG run.
get_task_instances	Lists task instances with optional filtering and sorting.
get_task_instance	Retrieves detailed information about a specific task instance.
patch_task_instance	Changes the execution state of a task instance.
get_extra_links	Lists additional links related to a task or DAG run.
get_mapped_task_instances	Lists task instances created from dynamic task mapping.
get_log	Retrieves execution logs for a task instance.
set_task_instance_note	Adds or updates a note associated with a task instance.
get_xcom_entries	Lists shared data exchanges between tasks in a DAG run.
get_xcom_entry	Get an XCom entry.
get_mapped_task_instance	Get a mapped task instance.
patch_mapped_task_instance	Updates the state of a mapped task instance.
set_mapped_task_instance_note	Update the TaskInstance note.
get_upstream_dataset_events	Get dataset events for a DAG run.
get_dag_details	Get a simplified representation of DAG.
get_tasks	Get tasks for DAG.
get_task	Get simplified representation of a task.
post_set_task_instances_state	Set a state of task instances.
get_dag_runs_batch	List DAG runs (batch).
get_task_instances_batch	List task instances (batch).
get_datasets	List datasets.
get_dataset_events	Get dataset events.
get_dataset	Get a dataset.
get_event_logs	List log entries.
get_event_log	Get a log entry.
get_health	Get instance status.
get_import_errors	List import errors.
get_import_error	Get an import error.
get_permissions	List permissions.
get_plugins	Get a list of loaded plugins.
get_pools	List pools.
post_pool	Create a pool.
delete_pool	Delete a pool.
get_pool	Get a pool.
patch_pool	Update a pool.
get_providers	List providers.
get_roles	List roles.
post_role	Create a role.
delete_role	Delete a role.
get_role	Get a role.
patch_role	Update a role.
get_users	List users.
post_user	Create a user.
delete_user	Delete a user.
get_user	Get a user.
patch_user	Update a user.
get_variables	List variables.
post_variables	Create a variable.
delete_variable	Delete a variable.
get_variable	Get a variable.
patch_variable	Update a variable.
get_version	Get version information.