Skip to main content
Glama

MCP Server for Apache Airflow

by yangkyeongmo
monitoring.py1.22 kB
from typing import Callable, List, Union import mcp.types as types from airflow_client.client.api.monitoring_api import MonitoringApi from src.airflow.airflow_client import api_client monitoring_api = MonitoringApi(api_client) def get_all_functions() -> list[tuple[Callable, str, str, bool]]: """Return list of (function, name, description, is_read_only) tuples for registration.""" return [ (get_health, "get_health", "Get instance status", True), (get_version, "get_version", "Get version information", True), ] async def get_health() -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]: """ Get the status of Airflow's metadatabase, triggerer and scheduler. It includes info about metadatabase and last heartbeat of scheduler and triggerer. """ response = monitoring_api.get_health() return [types.TextContent(type="text", text=str(response.to_dict()))] async def get_version() -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]: """ Get version information about Airflow. """ response = monitoring_api.get_version() return [types.TextContent(type="text", text=str(response.to_dict()))]

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/yangkyeongmo/mcp-server-apache-airflow'

If you have feedback or need assistance with the MCP directory API, please join our Discord server