Subconscious Ai Mcp

Name: Subconscious Ai Mcp
Author: subconscious-ai

@subconscious-ai

Repository

About Subconscious Ai Mcp

Run conjoint experiments and causal research through AI-powered behavioral simulations

#subconscious-ai #behavioral-simulation #causal-research

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Subconscious Ai Mcp?

Subconscious Ai Mcp is an MCP (Model Context Protocol) server that lets you run AI-powered conjoint experiments from Claude Desktop, Cursor, or any MCP-compatible client. It uses causal inference and synthetic populations based on US Census microdata to help understand why people make decisions.

How to use Subconscious Ai Mcp?

You can use the hosted server by adding a URL with a token to your MCP client configuration (Claude Desktop or Cursor), or run it locally by cloning the repo, installing dependencies with Python 3.11+, and setting environment variables AUTH0_JWT_TOKEN and API_BASE_URL. Tokens are obtained from app.subconscious.ai.

Key features of Subconscious Ai Mcp

Causal research: validate research questions and generate statistically valid experiments
Synthetic populations: AI personas based on US Census microdata (IPUMS)
Conjoint analysis: AMCE for measuring attribute importance
Works with Claude Desktop, Cursor, and any MCP-compatible client
REST API with direct HTTP access for integrations (n8n, Zapier)
Real-time updates via Server-Sent Events for live experiment progress

Use cases of Subconscious Ai Mcp

Validate whether a research question is causal before running an experiment
Generate experiment attributes and levels (e.g., for an EV preference study)
Run a conjoint experiment with synthetic respondents and get AMCE analytics
Get AI-generated causal insights and key findings from completed experiments
Integrate experiment creation into no-code automation tools like n8n

FAQ from Subconscious Ai Mcp

What runtime and dependencies are required?

Python 3.11+ is required for local execution. Dependencies are listed in requirements.txt and installed via pip.

How does authentication work?

You need an active Subconscious AI subscription. For the hosted server, include your token as a query parameter in the SSE URL. For local setup, set the AUTH0_JWT_TOKEN environment variable.

Where does experiment data live?

The server proxies requests to the Subconscious AI backend (https://api.subconscious.ai). Self-hosted deployments also route through this backend; user data is not stored on the MCP server itself.

What transports and protocols are supported?

The server supports MCP via SSE (Server-Sent Events) and also provides a REST API at /api/call/{tool} endpoints. Both require authentication.

Is there a limit to the number of experiments or respondents?

The README mentions experiments can be created with a specified confidence level (e.g., "Reasonable"), but does not state explicit quantitative limits. The platform surveys synthetic respondents (e.g., 500 per experiment).