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Lumenore MCP Server

@Lumenore-Platform

リポジトリ

基本情報

カテゴリ

データと分析

トランスポート

stdio

作者

Lumenore-Platform

投稿者

Lumenore Platform

Lumenore MCP Server について

A Model Context Protocol (MCP) server that provides AI assistants with access to Lumenore's analytics and natural language query capabilities. Built with FastMCP and Python 3.13 for scalable data analytics integration.

#lumenore-mcp#lumenore-mcp-server#lumenore-com

Lumenore 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": {
    "lumenore-analytics": {
      "command": "python3",
      "args": [
        "/absolute/path/to/lumenore_mcp_server.py"
      ],
      "env": {
        "LUMENORE_CLIENT_ID": "your_lumenore_client_id",
        "LUMENORE_CLIENT_SECRET": "your_lumenore_client_secret"
      }
    }
  }
}

Lumenore 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。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Lumenore Mcp?

Lumenore Mcp is a Model Context Protocol (MCP) server that gives AI assistants access to Lumenore’s analytics and natural language query (NLQ) capabilities. Built with FastMCP and Python 3.13, it is designed for scalable, real‑time data analytics integration.

How to use Lumenore Mcp?

Clone the repository, install dependencies, copy .env.example to .env, and set your LUMENORE_CLIENT_ID, LUMENORE_SECRET, and optionally SERVER_URL. Run python main.py to start the server on http://0.0.0.0:8080. Connect any MCP‑compatible AI assistant (e.g., Claude for Desktop) using the URL http://localhost:8080/mcp with Streamable HTTP transport. Use the eight available tools to query datasets, run natural language queries, and perform advanced analytics.

Key features of Lumenore Mcp

  • Natural language queries converted into structured data insights
  • Advanced analytics: trends, predictions, correlations, outliers, changes, Pareto
  • Real‑time streaming of responses for large datasets
  • Zero persistent data storage – all processing in memory
  • Eight purpose‑built MCP tools for dataset management and analysis
  • Standardized error handling with validation and service error types

Use cases of Lumenore Mcp

  • Sales performance analysis: regional breakdowns, product contribution, monthly trends
  • Customer behavior analysis: segmentation, anomaly detection, value drivers
  • Inventory optimization: demand trend analysis, forecasting, change detection

よくある質問

1

How do I obtain Lumenore client credentials?

Contact Lumenore support to request a `LUMENORE_CLIENT_ID` and `LUMENORE_SECRET`. Store them securely in your `.env` file.

2

What are the runtime requirements?

Python 3.13 or higher is required, along with access to a Lumenore server instance and valid client credentials.

3

Where does data get stored?

The MCP server processes data in memory and does not persist any user queries or results. When requests are forwarded to Lumenore’s backend, Lumenore’s privacy policies apply.

4

Which transport and authentication does the server use?

The server uses Streamable HTTP (SSE) transport on port 8080. No MCP‑level authentication is required; credentials are used only for Lumenore backend authentication.

5

What are the performance characteristics of the tools?

Typical response time is 1–7 seconds with a 60‑second timeout. The server supports multiple simultaneous requests.

コメント

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