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)

• 🔌 Local stdio transport - Protocol-tested MCP connection for desktop clients

Related MCP server: mcp-usercall

🚀 Quick Start

Supported: Run locally over stdio

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"
      }
    }
  }
}

Use an absolute path for both the Python executable and server/main.py. Keep the configuration file private because it contains a bearer credential. The checked examples under examples/ use the same supported stdio contract.

You can verify the protocol connection without calling a paid tool:

python scripts/smoke_stdio_mcp.py

The smoke test performs a real MCP initialize and tools/list exchange and expects all 15 registered tools.

Experimental: Hosted SSE

The hosted SSE endpoint is not a supported client setup yet. It accepts bearer credentials only through the Authorization header; URL query credentials are rejected. Because the hosted protocol/auth flow does not yet have a credentialed smoke test, do not rely on it for production client configuration.

📋 Available Tools

The checked mcp-tools.public.json file is the machine-readable discovery contract. It is generated directly from the same 15-tool registry used by the hosted API and includes deterministic source, transport, authentication, and schema metadata without credential values.

The manifest names the exact commit that owns every tool implementation. A squash merge creates a new owning commit even when the exported tools do not change, so regenerate and commit the manifest after the merge. A green pull-request artifact proves its named branch revision, not the default branch.

🔬 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

🏗️ 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.