get_config_section
Retrieve all configuration options from a specific section in Apache Airflow, enabling users to inspect and manage cluster settings without API complexity.
Instructions
[Tool Role]: Gets all options within a specific configuration section.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| section_name | Yes |
Implementation Reference
- The core handler function for the 'get_config_section' tool. It retrieves a specific section from Airflow's configuration by making an API request to /config and extracting the requested section.@mcp.tool() async def get_config_section(section_name: str) -> Dict[str, Any]: """[Tool Role]: Gets all options within a specific configuration section.""" try: resp = await airflow_request("GET", "/config") resp.raise_for_status() config_data = resp.json() section_data = config_data.get("sections", {}).get(section_name) if not section_data: return {"error": f"Section '{section_name}' not found"} return { "section_name": section_name, "options": section_data.get("options", {}), "options_count": len(section_data.get("options", {})) } except Exception as e: return { "error": f"Configuration access denied: {str(e)}", "note": "This requires 'expose_config = True' in airflow.cfg [webserver] section" }
- 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 registers the get_config_section tool among others.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 register_common_tools(mcp), which registers the get_config_section tool among others.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)")