Lumenore Mcp

Name: Lumenore Mcp
Author: Lumenore-Platform

@Lumenore-Platform

About Lumenore Mcp

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

Basic information

Config

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

{
  "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"
      }
    }
  }
}

Tools

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Overview

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

FAQ from Lumenore Mcp

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.

What are the runtime requirements?

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

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.

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.