Subconscious Ai MCP Server

@subconscious-ai

リポジトリ

基本情報

カテゴリ

その他

トランスポート

stdio

認証方式

oauth

作者

subconscious-ai

投稿者

Nihar Shah

Subconscious Ai MCP Server について

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

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

Subconscious Ai MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

Subconscious Ai MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

このサーバーは認証が必要です。ツールの取得にはログインや提供元の認証情報が必要な場合があります。

上のボタンでサーバーのエンドポイントに接続し、ツール一覧を取得できます。

概要

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

よくある質問

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).

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