Subconscious AI MCP

★ 4

from subconscious-ai

Run conjoint experiments and causal research through AI powered behavioral simulations

🔥🔥🔥✓ VerifiedPaid serviceAdvanced setup

⌁ labsco summary — our analysis, not the vendor's

What it is — an MCP server for running AI-powered conjoint experiments and causal research using synthetic populations, aimed at understanding why people make decisions.

What you get —

check_causality to validate that a research question is genuinely causal
generate_attributes_levels to build experiment attributes and levels with AI
Synthetic populations based on US Census microdata (IPUMS) for representative sampling
Conjoint/AMCE analysis to measure attribute importance
REST API and real-time SSE progress updates for integrations (n8n, Zapier)

Requirements — needs a Subconscious AI account and an access token (AUTH0_JWT_TOKEN) plus API_BASE_URL; get the token at app.subconscious.ai. Python 3.11+ for local runs.

Cost snapshot — the server carries a Proprietary license, and pricing is paid_service — it fronts the paid Subconscious AI platform, so usage is billed by them (no dollar figures in the README).

Setup effort — easiest via the hosted SSE URL with your token; or clone and run locally with the env vars set.

Our take — compelling for researchers wanting fast causal/conjoint studies via synthetic respondents; the honest caveats are that it's a proprietary paid service requiring an account, and results come from AI-simulated populations rather than live human respondents.

Source: the project README — summarized 2026-07-08.

⌁ readme from github — mirrored (latest on GitHub ↗)

Subconscious AI MCP Server

Run AI-powered conjoint experiments from Claude, Cursor, or any MCP-compatible client. Understand why people make decisions using causal inference and synthetic populations.

✨ Features

🧠 Causal Research - Validate research questions and generate statistically valid experiments
👥 Synthetic Populations - AI personas based on US Census microdata (IPUMS) for representative sampling
📊 Conjoint Analysis - AMCE (Average Marginal Component Effects) for measuring attribute importance
🤖 MCP Protocol - Works with Claude Desktop, Cursor, and any MCP-compatible AI assistant
🌐 REST API - Direct HTTP access for integrations (n8n, Zapier, custom apps)
⚡ Real-time Updates - Server-Sent Events (SSE) for live experiment progress

📋 Available Tools

Tool	Description
`check_causality`	Validate that a research question is causal
`generate_attributes_levels`	Generate experiment attributes and levels using AI
`validate_population`	Validate target population demographics
`get_population_stats`	Get population statistics for a country
`create_experiment`	Create and run a conjoint experiment
`get_experiment_status`	Check experiment progress
`list_experiments`	List all your experiments
`get_experiment_results`	Get detailed experiment results
`get_run_details`	Get detailed run information
`get_run_artifacts`	Get run artifacts and files
`update_run_config`	Update run configuration
`generate_personas`	Generate AI personas for an experiment
`get_experiment_personas`	Get personas for an experiment
`get_amce_data`	Get AMCE analytics data
`get_causal_insights`	Get AI-generated causal insights

🔬 Example Workflow

You: "Check if this is a causal question: What factors influence people's decision to buy electric vehicles?"

AI: ✅ This is a causal question. Let me generate attributes for this study.

You: "Generate attributes for an EV preference study"

AI: Generated 5 attributes with 4 levels each:
    - Price: $25,000 / $35,000 / $45,000 / $55,000
    - Range: 200 miles / 300 miles / 400 miles / 500 miles
    ...

You: "Create an experiment about EV purchasing decisions"

AI: 🚀 Experiment created! Run ID: abc-123-xyz
    Status: Processing (surveying 500 synthetic respondents)

You: "Check the status of experiment abc-123-xyz"

AI: ✅ Experiment completed!
    - 500 respondents surveyed
    - Ready for analysis

You: "Get causal insights from this experiment"

AI: 📊 Key Findings:
    - Price has the strongest effect (-0.32 AMCE)
    - 400+ mile range increases preference by 28%
    - Brand reputation matters more than charging speed

🌐 REST API

Call tools directly via HTTP for integrations:

# List experiments
curl -X POST https://ghostshell-runi.vercel.app/api/call/list_experiments \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"limit": 5}'

# Check causality
curl -X POST https://ghostshell-runi.vercel.app/api/call/check_causality \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"why_prompt": "What factors influence EV purchases?"}'

# Create experiment
curl -X POST https://ghostshell-runi.vercel.app/api/call/create_experiment \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"why_prompt": "What factors influence EV purchases?", "confidence_level": "Reasonable"}'

# Get experiment results
curl -X POST https://ghostshell-runi.vercel.app/api/call/get_experiment_results \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"run_id": "your-run-id"}'

📡 API Endpoints

Endpoint	Method	Auth	Description
`/`	GET	No	Server info and available tools
`/api/health`	GET	No	Health check
`/api/tools`	GET	No	List all tools with schemas
`/api/sse`	GET	Yes	MCP SSE connection (Authorization header preferred; query token fallback)
`/api/call/{tool}`	POST	Yes	Call a tool directly

🏗️ Self-Hosting on Vercel

Deploy your own instance for your organization:

# Install Vercel CLI
npm i -g vercel

# Clone and deploy
git clone https://github.com/Subconscious-ai/ghostshell.git
cd ghostshell
vercel --prod

Configure environment variables in Vercel dashboard:

API_BASE_URL: https://api.subconscious.ai (or your backend URL)

⚠️ Users must provide their own tokens - the server proxies requests to the Subconscious AI backend.

💡 Feature Requests & Support

Have a feature request or need help? Email us at nihar@subconscious.ai

📚 Resources

Subconscious AI Platform - Create experiments via UI
API Documentation — Full API reference
MCP Protocol - Model Context Protocol specification
Conjoint Analysis - Learn about the methodology

📄 License

This software requires an active Subconscious AI subscription. See the LICENSE file for details.

Made with ❤️ by Subconscious AI

Copy & paste — that's it

{
  "mcpServers": {
    "subconscious-ai": {
      "url": "https://ghostshell-runi.vercel.app/api/sse?token=YOUR_TOKEN"
    }
  }
}

Before it works, you'll need: AUTH0_JWT_TOKENAPI_BASE_URL

🚀 Quick Start

Option 1: Use Hosted Server (Recommended)

No setup required! Add to your MCP client configuration:

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "subconscious-ai": {
      "url": "https://ghostshell-runi.vercel.app/api/sse?token=YOUR_TOKEN"
    }
  }
}

Cursor (~/.cursor/mcp.json):

{
  "mcpServers": {
    "subconscious-ai": {
      "url": "https://ghostshell-runi.vercel.app/api/sse?token=YOUR_TOKEN"
    }
  }
}

🔑 Get your token at app.subconscious.ai → Settings → Access Token

Option 2: Run Locally

Prerequisites:

Python 3.11+
A Subconscious AI account and Access token

# Clone the repository
git clone https://github.com/Subconscious-ai/ghostshell.git
cd ghostshell

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e ".[dev]"

# Set environment variables
export AUTH0_JWT_TOKEN="your_token_here"
export API_BASE_URL="https://api.subconscious.ai"

Add to your MCP config:

{
  "mcpServers": {
    "subconscious-ai": {
      "command": "/absolute/path/to/venv/bin/python3",
      "args": ["/absolute/path/to/server/main.py"],
      "env": {
        "AUTH0_JWT_TOKEN": "your_token",
        "API_BASE_URL": "https://api.subconscious.ai"
      }
    }
  }
}