Xplainable MCP Server

""" Datasets service MCP tools. Auto-generated and maintained by the xplainable-client sync workflow. """ import logging from typing import Optional, List, Dict, Any from ..mcp_instance import mcp logger = logging.getLogger(__name__) # Import shared utilities from ..server import get_client # Datasets Tools # ============================================ @mcp.tool() def datasets_load_dataset(name: str): """ Load a public dataset by name. Args: name: Name of the dataset to load Returns: DataFrame containing the dataset Raises: ValueError: If dataset doesn't exist XplainableAPIError: If loading fails Category: read """ try: client = get_client() result = client.datasets.load_dataset(name) logger.info(f"Executed datasets.load_dataset") # Handle different return types if hasattr(result, 'model_dump'): return result.model_dump() elif isinstance(result, list) and result and hasattr(result[0], 'model_dump'): return [item.model_dump() for item in result] else: return result except Exception as e: logger.error(f"Error in datasets_load_dataset: {e}") raise @mcp.tool() def datasets_list_datasets(): """ List all available public datasets. Returns: List of dataset names Raises: XplainableAPIError: If listing fails Category: read """ try: client = get_client() result = client.datasets.list_datasets() logger.info(f"Executed datasets.list_datasets") # Handle different return types if hasattr(result, 'model_dump'): return result.model_dump() elif isinstance(result, list) and result and hasattr(result[0], 'model_dump'): return [item.model_dump() for item in result] else: return result except Exception as e: logger.error(f"Error in datasets_list_datasets: {e}") raise @mcp.tool() def datasets_list_team_datasets(team_id: Optional[str] = None): """ List all datasets for a team. Args: team_id: Optional team ID (uses session team_id if not provided) Returns: List of dataset information Raises: XplainableAPIError: If listing fails Category: read """ try: client = get_client() result = client.datasets.list_team_datasets(team_id) logger.info(f"Executed datasets.list_team_datasets") # Handle different return types if hasattr(result, 'model_dump'): return result.model_dump() elif isinstance(result, list) and result and hasattr(result[0], 'model_dump'): return [item.model_dump() for item in result] else: return result except Exception as e: logger.error(f"Error in datasets_list_team_datasets: {e}") raise