v2_tools.py•3.91 kB
"""
Airflow API v2 (3.0+) MCP tools.
Imports common tools and adds v2-specific asset management tools.
Total: 45 tools (43 from common_tools + 2 v2-exclusive)
"""
from typing import Any, Dict, Optional
from ..functions import airflow_request as airflow_request_v2
from . import common_tools
import logging
logger = logging.getLogger(__name__)
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)")