dag_run_duration
Analyze DAG run durations and performance metrics to monitor workflow execution times and identify optimization opportunities in Apache Airflow.
Instructions
[Tool Role]: Analyzes DAG run durations and performance metrics.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| dag_id | Yes | ||
| limit | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Implementation Reference
- Core handler function for the `dag_run_duration` tool. Fetches recent DAG runs via Airflow API, calculates execution durations, and computes performance statistics including average, fastest, and slowest runs.
@mcp.tool() async def dag_run_duration(dag_id: str, limit: int = 10) -> Dict[str, Any]: """[Tool Role]: Analyzes DAG run durations and performance metrics.""" if not dag_id: raise ValueError("dag_id must not be empty") resp = await airflow_request("GET", f"/dags/{dag_id}/dagRuns?limit={limit}&order_by=-execution_date") resp.raise_for_status() data = resp.json() runs = data.get("dag_runs", []) durations = [] for run in runs: start_date = run.get("start_date") end_date = run.get("end_date") if start_date and end_date: from datetime import datetime start = datetime.fromisoformat(start_date.replace('Z', '+00:00')) end = datetime.fromisoformat(end_date.replace('Z', '+00:00')) duration_seconds = (end - start).total_seconds() durations.append({ "run_id": run.get("run_id"), "duration_seconds": duration_seconds, "state": run.get("state"), "execution_date": run.get("execution_date") }) avg_duration = sum(d["duration_seconds"] for d in durations) / len(durations) if durations else 0 return { "dag_id": dag_id, "run_durations": durations, "statistics": { "average_duration_seconds": avg_duration, "total_analyzed_runs": len(durations), "fastest_run": min(durations, key=lambda x: x["duration_seconds"]) if durations else None, "slowest_run": max(durations, key=lambda x: x["duration_seconds"]) if durations else None } } - src/mcp_airflow_api/tools/v1_tools.py:13-28 (registration)Registration entry point for v1 API tools. Sets the v1-specific `airflow_request` function and calls `common_tools.register_common_tools(mcp)` which defines and registers the `dag_run_duration` handler.
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:14-104 (registration)Registration entry point for v2 API tools. Sets the v2-specific `airflow_request` function and calls `common_tools.register_common_tools(mcp)` which defines and registers the `dag_run_duration` handler.
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) # Add V2-exclusive tools (2 tools) @mcp.tool() async def list_assets(limit: int = 20, offset: int = 0, uri_pattern: Optional[str] = None) -> Dict[str, Any]: """ [V2 New] List all assets in the system for data-aware scheduling. Assets are a key feature in Airflow 3.0 for data-aware scheduling. They enable workflows to be triggered by data changes rather than time schedules. Args: limit: Maximum number of assets to return (default: 20) offset: Number of assets to skip for pagination (default: 0) uri_pattern: Filter assets by URI pattern (optional) Returns: Dict containing assets list, pagination info, and metadata """ params = {'limit': limit, 'offset': offset} if uri_pattern: params['uri_pattern'] = uri_pattern query_string = "&".join([f"{k}={v}" for k, v in params.items()]) resp = await airflow_request_v2("GET", f"/assets?{query_string}") resp.raise_for_status() data = resp.json() return { "assets": data.get("assets", []), "total_entries": data.get("total_entries", 0), "limit": limit, "offset": offset, "api_version": "v2", "feature": "assets" } @mcp.tool() async def list_asset_events(limit: int = 20, offset: int = 0, asset_uri: Optional[str] = None, source_dag_id: Optional[str] = None) -> Dict[str, Any]: """ [V2 New] List asset events for data lineage tracking. Asset events track when assets are created or updated by DAGs. This enables data lineage tracking and data-aware scheduling in Airflow 3.0. Args: limit: Maximum number of events to return (default: 20) offset: Number of events to skip for pagination (default: 0) asset_uri: Filter events by specific asset URI (optional) source_dag_id: Filter events by source DAG that produced the event (optional) Returns: Dict containing asset events list, pagination info, and metadata """ params = {'limit': limit, 'offset': offset} if asset_uri: params['asset_uri'] = asset_uri if source_dag_id: params['source_dag_id'] = source_dag_id query_string = "&".join([f"{k}={v}" for k, v in params.items()]) resp = await airflow_request_v2("GET", f"/assets/events?{query_string}") resp.raise_for_status() data = resp.json() return { "asset_events": data.get("asset_events", []), "total_entries": data.get("total_entries", 0), "limit": limit, "offset": offset, "api_version": "v2", "feature": "asset_events" } logger.info("Registered all Airflow API v2 tools (43 common + 2 assets + 4 management = 49 tools)")