failed_dags
Identify and list recently failed DAG runs in Apache Airflow clusters to monitor workflow execution issues and facilitate troubleshooting.
Instructions
[Tool Role]: Lists all recently failed DAG runs in the Airflow cluster.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Implementation Reference
- The core handler function implementing the 'failed_dags' tool. It performs an API request to fetch recently failed DAG runs (state=failed, limit=1000, ordered by start_date descending), processes the response to extract key run information, and returns a structured dictionary with the list of failed runs, total count, and query metadata.
@mcp.tool() async def failed_dags() -> Dict[str, Any]: """[Tool Role]: Lists all recently failed DAG runs in the Airflow cluster.""" resp = await airflow_request("GET", "/dags/~/dagRuns?state=failed&limit=1000&order_by=-start_date") resp.raise_for_status() data = resp.json() failed_runs = [] for run in data.get("dag_runs", []): run_info = { "dag_id": run.get("dag_id"), "dag_display_name": run.get("dag_display_name"), "run_id": run.get("run_id"), "run_type": run.get("run_type"), "state": run.get("state"), "execution_date": run.get("execution_date"), "start_date": run.get("start_date"), "end_date": run.get("end_date"), "data_interval_start": run.get("data_interval_start"), "data_interval_end": run.get("data_interval_end"), "external_trigger": run.get("external_trigger"), "conf": run.get("conf"), "note": run.get("note") } failed_runs.append(run_info) return { "dag_runs": failed_runs, "total_failed": len(failed_runs), "query_info": { "state_filter": "failed", "limit": 1000, "order_by": "start_date (descending)" } } - src/mcp_airflow_api/tools/v1_tools.py:13-27 (registration)Registration entry point for v1 API tools. Sets the v1-specific airflow_request function and calls register_common_tools(mcp), which defines and registers the failed_dags tool using the @mcp.tool() decorator.
def register_tools(mcp): """Register v1 tools by importing common tools with v1 request function.""" logger.info("Initializing MCP server for Airflow API v1") logger.info("Loading Airflow API v1 tools (Airflow 2.x)") # Set the global request function to v1 common_tools.airflow_request = airflow_request_v1 # Register all 56 common tools (includes management tools) common_tools.register_common_tools(mcp) # V1 has no exclusive tools - all tools are shared with v2 logger.info("Registered all Airflow API v1 tools (56 tools: 43 core + 13 management tools)") - src/mcp_airflow_api/tools/v2_tools.py:14-25 (registration)Registration entry point for v2 API tools. Sets the v2-specific airflow_request function and calls register_common_tools(mcp), which defines and registers the failed_dags tool using the @mcp.tool() decorator. Also registers v2-exclusive asset tools.
def register_tools(mcp): """Register v2 tools: common tools + v2-exclusive asset tools.""" logger.info("Initializing MCP server for Airflow API v2") logger.info("Loading Airflow API v2 tools (Airflow 3.0+)") # Set the global request function to v2 common_tools.airflow_request = airflow_request_v2 # Register all 43 common tools common_tools.register_common_tools(mcp)