mcp-subconscios

"""MCP tools for ideation workflow - causality check and attribute/level generation.""" from typing import Any, Dict from mcp.types import Tool as MCPTool from ._core.base import EnvironmentTokenProvider from ._core.handlers import ( check_causality as _check_causality, ) from ._core.handlers import ( generate_attributes_levels as _generate_attributes_levels, ) # ============================================================================= # STEP 1: Check Causality # ============================================================================= def check_causality_tool() -> MCPTool: """Check if a research question is causal.""" return MCPTool( name="check_causality", description=( "Check if a research question (why_prompt) is causal. " "A causal question asks about factors that influence an outcome. " "Example: 'What factors influence consumer preference for electric vehicles?' " "Returns whether the question is causal and suggestions for improvement if not." ), inputSchema={ "type": "object", "properties": { "why_prompt": { "type": "string", "description": "The research question to check for causality", }, "llm_model": { "type": "string", "description": "LLM model to use for analysis", "enum": ["gpt4", "sonnet", "haiku"], "default": "sonnet", }, }, "required": ["why_prompt"], }, ) async def handle_check_causality(arguments: Dict[str, Any]) -> Dict[str, Any]: """Handle check_causality tool execution.""" result = await _check_causality(arguments, EnvironmentTokenProvider()) return result.to_dict() # ============================================================================= # STEP 2: Generate Attributes and Levels # ============================================================================= def generate_attributes_levels_tool() -> MCPTool: """Generate attributes and levels for a conjoint experiment.""" return MCPTool( name="generate_attributes_levels", description=( "Generate attributes and levels for a conjoint experiment based on the research question. " "Attributes are the factors being tested (e.g., price, brand, features). " "Levels are the specific values for each attribute (e.g., $10, $20, $30 for price). " "This is required before creating an experiment." ), inputSchema={ "type": "object", "properties": { "why_prompt": { "type": "string", "description": "The research question (must be causal)", }, "country": { "type": "string", "description": "Target country for the experiment", "default": "United States", }, "year": { "type": "string", "description": "Year context for the experiment", "default": "2024", }, "attribute_count": { "type": "integer", "description": "Number of attributes to generate (2-10)", "default": 5, "minimum": 2, "maximum": 10, }, "level_count": { "type": "integer", "description": "Number of levels per attribute (2-10)", "default": 4, "minimum": 2, "maximum": 10, }, "llm_model": { "type": "string", "description": "LLM model to use", "enum": ["gpt4", "sonnet", "haiku"], "default": "sonnet", }, }, "required": ["why_prompt"], }, ) async def handle_generate_attributes_levels(arguments: Dict[str, Any]) -> Dict[str, Any]: """Handle generate_attributes_levels tool execution.""" result = await _generate_attributes_levels(arguments, EnvironmentTokenProvider()) return result.to_dict()