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Lumenore MCP Server

@Lumenore-Platform

Repository

Basic information

Category

Data & Analytics

Transports

stdio

Author

Lumenore-Platform

Submitted by

Lumenore Platform

About Lumenore MCP Server

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 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"
      }
    }
  }
}

Lumenore MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

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

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

Frequently asked questions

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.

Comments

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