mcp-tools.public.json

{
  "authentication": {
    "delivery": "environment",
    "environment_variable": "AUTH0_JWT_TOKEN",
    "type": "bearer"
  },
  "generated_at": "2026-08-31T23:21:36-04:00",
  "manifest_version": 1,
  "source": {
    "repository": "Subconscious-ai/ghostshell",
    "revision": "17cb7d9e2ee860d3fabe0f3740a1a6b2d41a925e"
  },
  "tool_count": 15,
  "tools": [
    {
      "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": {
        "properties": {
          "llm_model": {
            "default": "sonnet",
            "description": "LLM model to use for analysis",
            "enum": [
              "gpt4",
              "sonnet",
              "haiku"
            ],
            "type": "string"
          },
          "why_prompt": {
            "description": "The research question to check for causality",
            "type": "string"
          }
        },
        "required": [
          "why_prompt"
        ],
        "type": "object"
      },
      "name": "check_causality"
    },
    {
      "description": "Create and run a new conjoint experiment. The experiment will be queued for execution and run asynchronously. Returns a run ID to track progress. IMPORTANT: Run check_causality and generate_attributes_levels first!",
      "inputSchema": {
        "properties": {
          "attribute_count": {
            "default": 5,
            "description": "Number of attributes (2-10)",
            "maximum": 10,
            "minimum": 2,
            "type": "integer"
          },
          "confidence_level": {
            "default": "Low",
            "description": "Confidence level for experiment sizing",
            "enum": [
              "Low",
              "Reasonable",
              "High"
            ],
            "type": "string"
          },
          "country": {
            "default": "United States",
            "description": "Target country",
            "type": "string"
          },
          "expr_llm_model": {
            "default": "sonnet",
            "description": "LLM model for experiment",
            "enum": [
              "gpt4",
              "sonnet",
              "haiku"
            ],
            "type": "string"
          },
          "is_private": {
            "default": false,
            "description": "Make experiment private",
            "type": "boolean"
          },
          "level_count": {
            "default": 4,
            "description": "Number of levels per attribute (2-10)",
            "maximum": 10,
            "minimum": 2,
            "type": "integer"
          },
          "pre_cooked_attributes_and_levels_lookup": {
            "description": "Pre-defined attributes and levels from generate_attributes_levels",
            "items": {
              "type": "array"
            },
            "type": "array"
          },
          "state": {
            "description": "Target US state (optional)",
            "type": "string"
          },
          "why_prompt": {
            "description": "The causal research question (e.g., 'What factors influence consumer preference for electric vehicles?')",
            "type": "string"
          }
        },
        "required": [
          "why_prompt"
        ],
        "type": "object"
      },
      "name": "create_experiment"
    },
    {
      "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": {
        "properties": {
          "attribute_count": {
            "default": 5,
            "description": "Number of attributes to generate (2-10)",
            "maximum": 10,
            "minimum": 2,
            "type": "integer"
          },
          "country": {
            "default": "United States",
            "description": "Target country for the experiment",
            "type": "string"
          },
          "level_count": {
            "default": 4,
            "description": "Number of levels per attribute (2-10)",
            "maximum": 10,
            "minimum": 2,
            "type": "integer"
          },
          "llm_model": {
            "default": "sonnet",
            "description": "LLM model to use",
            "enum": [
              "gpt4",
              "sonnet",
              "haiku"
            ],
            "type": "string"
          },
          "why_prompt": {
            "description": "The research question (must be causal)",
            "type": "string"
          },
          "year": {
            "default": "2024",
            "description": "Year context for the experiment",
            "type": "string"
          }
        },
        "required": [
          "why_prompt"
        ],
        "type": "object"
      },
      "name": "generate_attributes_levels"
    },
    {
      "description": "Generate synthetic personas based on experiment configuration. Returns persona definitions that can be used in experiments.",
      "inputSchema": {
        "properties": {
          "count": {
            "default": 5,
            "description": "Number of personas to generate",
            "maximum": 20,
            "minimum": 1,
            "type": "integer"
          },
          "run_id": {
            "description": "The experiment run ID",
            "type": "string"
          }
        },
        "required": [
          "run_id"
        ],
        "type": "object"
      },
      "name": "generate_personas"
    },
    {
      "description": "Get Average Marginal Component Effect (AMCE) data from experiment results. AMCE shows the average effect of each attribute level on choice probability.",
      "inputSchema": {
        "properties": {
          "format": {
            "default": "json",
            "description": "Output format",
            "enum": [
              "json",
              "csv"
            ],
            "type": "string"
          },
          "run_id": {
            "description": "The experiment run ID",
            "type": "string"
          }
        },
        "required": [
          "run_id"
        ],
        "type": "object"
      },
      "name": "get_amce_data"
    },
    {
      "description": "Get causal insights and feature importance from experiment results. Includes causal statements and feature importance rankings.",
      "inputSchema": {
        "properties": {
          "include_visualizations": {
            "default": false,
            "description": "Whether to include visualization URLs",
            "type": "boolean"
          },
          "run_id": {
            "description": "The experiment run ID",
            "type": "string"
          }
        },
        "required": [
          "run_id"
        ],
        "type": "object"
      },
      "name": "get_causal_insights"
    },
    {
      "description": "Retrieve synthetic personas generated for a specific experiment. Returns persona descriptions and demographics.",
      "inputSchema": {
        "properties": {
          "run_id": {
            "description": "The experiment run ID",
            "type": "string"
          }
        },
        "required": [
          "run_id"
        ],
        "type": "object"
      },
      "name": "get_experiment_personas"
    },
    {
      "description": "Get comprehensive results from a completed experiment. Includes AMCE (Average Marginal Component Effects), insights, and visualizations.",
      "inputSchema": {
        "properties": {
          "run_id": {
            "description": "The experiment run ID",
            "type": "string"
          }
        },
        "required": [
          "run_id"
        ],
        "type": "object"
      },
      "name": "get_experiment_results"
    },
    {
      "description": "Get the current status of an experiment run. Returns status (running, completed, failed) and progress information.",
      "inputSchema": {
        "properties": {
          "run_id": {
            "description": "The experiment run ID (wandb_run_id)",
            "type": "string"
          }
        },
        "required": [
          "run_id"
        ],
        "type": "object"
      },
      "name": "get_experiment_status"
    },
    {
      "description": "Get statistical information about a population configuration. Includes demographic breakdowns and census data summaries.",
      "inputSchema": {
        "properties": {
          "country": {
            "default": "United States of America (USA)",
            "description": "Target country",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "get_population_stats"
    },
    {
      "description": "Get artifacts from a completed experiment run. Returns download URLs for CSV files, visualizations, and other artifacts.",
      "inputSchema": {
        "properties": {
          "artifact_type": {
            "default": "all",
            "description": "Filter by artifact type",
            "enum": [
              "csv",
              "image",
              "json",
              "all"
            ],
            "type": "string"
          },
          "run_id": {
            "description": "The experiment run ID",
            "type": "string"
          }
        },
        "required": [
          "run_id"
        ],
        "type": "object"
      },
      "name": "get_run_artifacts"
    },
    {
      "description": "Get detailed information about a specific experiment run. Includes configuration, status, metrics, and metadata.",
      "inputSchema": {
        "properties": {
          "run_id": {
            "description": "The experiment run ID",
            "type": "string"
          }
        },
        "required": [
          "run_id"
        ],
        "type": "object"
      },
      "name": "get_run_details"
    },
    {
      "description": "List all experiments for the authenticated user. Returns experiment names, IDs, status, and creation dates.",
      "inputSchema": {
        "properties": {
          "limit": {
            "default": 20,
            "description": "Maximum number to return",
            "maximum": 100,
            "minimum": 1,
            "type": "integer"
          }
        },
        "type": "object"
      },
      "name": "list_experiments"
    },
    {
      "description": "Update configuration for an experiment run. Can update metadata, tags, and other configuration values.",
      "inputSchema": {
        "properties": {
          "config": {
            "description": "Configuration updates to apply",
            "type": "object"
          },
          "run_id": {
            "description": "The experiment run ID",
            "type": "string"
          }
        },
        "required": [
          "run_id"
        ],
        "type": "object"
      },
      "name": "update_run_config"
    },
    {
      "description": "Validate a population configuration before running an experiment. Checks census data availability and population parameters.",
      "inputSchema": {
        "properties": {
          "country": {
            "default": "United States of America (USA)",
            "description": "Target country (e.g., 'United States')",
            "type": "string"
          },
          "target_population": {
            "description": "Target population demographics configuration",
            "type": "object"
          }
        },
        "type": "object"
      },
      "name": "validate_population"
    }
  ],
  "tools_sha256": "8afb51da88f5a59090b7c39ec1f4bd49b54272bb1b6758a2c98e9b985ed0c51b",
  "transport": {
    "status": "supported",
    "type": "stdio"
  }
}