Googleapis

Initiates the creation of a new Managed Airflow environment with default settings. This tool creates a small private IP environment with a default configuration, including default networking settings. Creating public IP environments, or customizing most settings (such as network, subnetwork, network attachment, environment size, CMEK or custom environment bucket) is not supported. If the user requests settings that aren't supported by the tool parameters, inform them that this tool can't create an environment in the requested configuration, and that they should use gcloud CLI or Cloud Console instead. Some environment configuration parameters are mandatory. Always ask about them: - Always ask the user to provide a service account that they want to use for their environment. Users are strongly recommended to use a dedicated user-managed service account and are discouraged from using the default Cloud Compute service account for security reasons. - Always ask the user to specify the IP ranges that they want to use to access the Airflow UI and web server. If the user doesn't specify IP ranges, clarify that the default is not allowing access from any IPs. We also recommend to not allow access from all IP addresses to avoid potential security risks. This tool creates an environment without pre-installed PyPI packages. Use the `manage_pypi_packages` tool to install PyPI packages after the environment is created. This tool triggers the environment creation process, which is a long-running operation. The tool returns an operation object. Use the `get_operation` tool with the operation name returned by this tool to poll for creation status. Environment creation typically takes 25-30 minutes. A completed operation will include the details of the created environment.

Retrieves details about a specific Managed Airflow environment, including its configuration and status. Use this tool to check the current state of an environment, its Airflow version, Airflow UI URL, environment's bucket, or other configuration details.

Lists all Managed Airflow environments in a given project and location. Use this tool to when you need to know which environments exist in a specific project and location. Results are paginated; use `page_size` to control the number of results per page and `page_token` from a previous response to get the next page. If `page_token` is not present in the response, there are no more results.

Initiates the deletion of a Managed Airflow environment. Warning: This is a destructive action permanently deletes an environment and can't be undone. Always ask the user to confirm the deletion before proceeding. This tool triggers the environment deletion process, which is a long-running operation that typically takes 10-20 minutes. The tool returns an operation object. Use the `get_operation` tool with the operation name returned by this tool to poll for deletion status.

Manages PyPI packages in a Managed Airflow environment. Use this tool to install and uninstall PyPI packages in the Managed Airflow environment, or change PyPI package versions or extras. To install packages or change versions or extras, use the `update_packages` field. To remove packages, use the `remove_packages` field. At least one package must be specified in one of these fields. The same package can't be specified in both fields. This tool triggers an environment update, which is a long-running operation that can take several minutes. The tool returns an operation object. Use the `get_operation` tool to poll its status. A completed operation will include the details of the updated environment.

Gets the latest status of a long-running operation. Several tools like `create_environment`, `delete_environments`, and `manage_pypi_packages` return an operation object, because they initiate processes that take time to complete. Use this tool to check if such an operation has finished and whether it was successful. Provide the `name` of the operation as returned by the tool that initiated it. A completed operation may include the details specific to the operation, described in the description of the tool that initiates the operation.

Retrieves metadata and status of a specific DAG in a Managed Airflow environment. Use this tool when you need to check if a DAG exists, inspect its metadata (such as schedule or current state), or verify whether a newly uploaded/updated DAG file has been imported by Airflow (see `list_import_errors` tool description for more details). In particular, if a DAG was recently uploaded but `get_dag` returns NOT_FOUND, use `list_import_errors` to check if any errors occurred during the import.

Lists all DAGs in a Managed Airflow environment. Use this tool to get a list of all DAGs in the environment, for example to discover the DAGs present in the environment or get the state or properties of each DAG. Results are paginated; use `page_size` to control the number of results per page and `page_token` from a previous response to get the next page. If `page_token` is not present in the response, there are no more results.

Retrieves the DAG source code from a Managed Airflow environment. Use this tool when you want to understand DAG implementation details, for example to understand why a DAG run has failed, or to debug DAG's behavior. The tool returns the latest version of the source code.

Retrieves details of a specific DAG run from a Managed Airflow environment. Use this tool when you need to retrieve details of a specific DAG run, for example its state, start time or end time. This tool does not return DAG run logs directly. Instead, use the `cloud_logging_filter` field from the response to query Cloud Logging for the logs for this DAG run.

Lists DAG runs in a Managed Airflow environment. Use this tool to get a list of DAG runs in the environment, or runs for a specific DAG, for example to determine the overall healthiness of the environment or DAG. The tool returns DAG runs in the order of creation date, most recent first. Results are paginated; use `page_size` to control the number of results per page and `page_token` from a previous response to get the next page. If `page_token` is not present in the response, there are no more results.

Retrieves details of a specific task instance from a Managed Airflow environment. Use this tool to retrieve details of a specific task instance, such as its state, start time, end time, or parameters, to understand its status or to debug a failure. This tool does not return task instance logs directly. Instead, use the `cloud_logging_filter` field from the response to query Cloud Logging for the logs for this task instance.

Finds failed DAG runs in a Managed Airflow environment. Use this tool to find recent DAG run failures, for example to begin troubleshooting. If the user wants to troubleshoot a specific DAG, provide the DAG ID to get the details of its last failed run. If the user doesn't know which DAG is failing, or wants to see recent failures across all DAGs, leave the DAG ID empty; in this case, the tool will return the last failed DAG run for every DAG in the environment that has at least one failure. If the response returns more than one DAG run and the user asked for troubleshooting or explaining the failure, ask the user which DAG run to troubleshoot. Results are paginated; use `page_size` to control the number of results per page and `page_token` from a previous response to get the next page. If `page_token` is not present in the response, there are no more results.

Lists failed task instances of a specific DAG run in a Managed Airflow environment. If a DAG run has failed, use this tool to get the list of task instances of this DAG run that are in the FAILED state and may have caused the DAG run failure. This tool does not return task instance logs directly. Instead, for each task instance, use the `cloud_logging_filter` field from the response to query Cloud Logging for the logs for that task instance.

Triggers a DAG run for a specified DAG in a Managed Airflow environment. Use this tool when you need to manually start a new run of a DAG. The tool returns a DAG run object containing the details and state of the newly triggered run. You can use the `get_dag_run` tool to poll for state changes.

Lists Python import errors and parsing failures in a Managed Airflow environment. Use this tool to check if all DAGs are being parsed correctly in the environment. After uploading a DAG, use this tool alongside `get_dag` to check its parsing status: - **Success**: `get_dag` returns DAG details. - **Import failed**: `get_dag` returns `NOT_FOUND` and `list_import_errors` returns error details and stack traces referencing the DAG file created after the upload. - **Pending parsing**: `get_dag` returns `NOT_FOUND` and `list_import_errors` has no errors referencing the DAG file, created after upload. Always check the import error creation time to make sure the error is relevant to the latest upload. Results are paginated; use `page_size` to control the number of results per page and `page_token` from a previous response to get the next page. If `page_token` is not present in the response, there are no more results.