mcp-subconscios

"""MCP tools for analytics and insights.""" from typing import Any, Dict from mcp.types import Tool as MCPTool from ._core.base import EnvironmentTokenProvider from ._core.handlers import ( get_amce_data as _get_amce_data, ) from ._core.handlers import ( get_causal_insights as _get_causal_insights, ) def get_amce_data_tool() -> MCPTool: """Get Average Marginal Component Effect (AMCE) data.""" return MCPTool( name="get_amce_data", description=( "Get Average Marginal Component Effect (AMCE) data from experiment results. " "AMCE shows the average effect of each attribute level on choice probability." ), inputSchema={ "type": "object", "properties": { "run_id": { "type": "string", "description": "The experiment run ID", }, "format": { "type": "string", "description": "Output format", "enum": ["json", "csv"], "default": "json", }, }, "required": ["run_id"], }, ) async def handle_get_amce_data(arguments: Dict[str, Any]) -> Dict[str, Any]: """Handle get_amce_data tool execution.""" result = await _get_amce_data(arguments, EnvironmentTokenProvider()) return result.to_dict() def get_causal_insights_tool() -> MCPTool: """Get causal insights from experiment results.""" return MCPTool( name="get_causal_insights", description=( "Get causal insights and feature importance from experiment results. " "Includes causal statements and feature importance rankings." ), inputSchema={ "type": "object", "properties": { "run_id": { "type": "string", "description": "The experiment run ID", }, "include_visualizations": { "type": "boolean", "description": "Whether to include visualization URLs", "default": False, }, }, "required": ["run_id"], }, ) async def handle_get_causal_insights(arguments: Dict[str, Any]) -> Dict[str, Any]: """Handle get_causal_insights tool execution.""" result = await _get_causal_insights(arguments, EnvironmentTokenProvider()) return result.to_dict()