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Causal Panopticon MCP Server

@apifyforge

リポジトリホームページドキュメント

基本情報

カテゴリ

その他

最終更新

ライセンス

MIT

認証方式

oauth

作者

apifyforge

Causal Panopticon MCP Server について

Causal Panopticon is a cross-domain causal discovery and inference engine for AI agents, exposed via the Model Context Protocol.

Causal Panopticon 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": {
    "causal-panopticon-mcp": {
      "url": "https://ryanclinton--causal-panopticon-mcp.apify.actor/mcp"
    }
  }
}

Causal Panopticon 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 Causal Panopticon MCP Server?

Causal Panopticon MCP Server is a cross-domain causal discovery and inference engine for AI agents, exposed via the Model Context Protocol. It integrates 18 data sources across economics, health, environment, security, policy, finance, academia, and labor, applying eight peer-reviewed causal algorithms to uncover causal relationships beyond correlation.

How to use Causal Panopticon MCP Server?

Add the server endpoint to any MCP client (e.g., Claude Desktop, Cursor) by including "url": "https://causal-panopticon-mcp.apify.actor/mcp" in your client's MCP configuration JSON. An optional Authorization header with a Bearer token (Apify API key) can be provided. All tool parameters are passed directly when calling each MCP tool; there are no actor-level inputs.

Key features of Causal Panopticon MCP Server

  • Meta-algorithm for automated DAG selection
  • PC, GES, and NOTEARS structural learning algorithms
  • Do-calculus effect identification with three criteria
  • Counterfactual estimation via Pearl’s SCM
  • Bareinboim-Pearl transportability for cross-population inference
  • Colimit graph merging for cross-domain causal edges
  • Confounder detection with strength and adjustability assessment
  • Causal Bayesian optimization for experiment design

Use cases of Causal Panopticon MCP Server

  • Policy impact research: evaluate legislative effect on economic outcomes
  • Cross-domain causal hypothesis generation across siloed data sources
  • Clinical and public health attribution via counterfactual estimates
  • Generalizing findings across populations with transportability analysis
  • AI agent experiment planning with expected information gain

よくある質問

1

How is this different from correlation-based analysis?

Correlation cannot distinguish causation from confounding. This server applies do-calculus and DAG-based structural discovery to estimate average treatment effects, counterfactuals, and identify which variables actually cause changes.

2

What runtime or dependencies are required?

No local runtime beyond an MCP-compatible client (Claude Desktop, Cursor, etc.). Data collection across 18 sources runs in parallel on Apify’s platform with built-in proxy rotation and monitoring.

3

Where does the data come from and does it stay local?

Data is fetched in real time from 18 public APIs (FRED, BLS, WHO, NOAA, SEC EDGAR, etc.) on each tool call. No data is stored by the server; results are returned directly to the calling agent.

4

Are there any usage limits or costs?

Each tool call costs $0.04. The server uses Apify’s infrastructure, and optional API key monitoring can alert on failures or unexpected results.

5

How is authentication handled?

Authentication is optional; the server supports an `Authorization: Bearer <token>` header in the MCP connection. The endpoint uses Streamable HTTP transport over the standard MCP `tools/list` and `tools/call` operations.

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