Lumenore MCP Server

@Lumenore-Platform

代码仓库

基本信息

分类

数据与分析

传输方式

stdio

作者

Lumenore-Platform

提交者

Lumenore Platform

关于 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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "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 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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.

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.

数据与分析分类下的更多 MCP 服务器

Minds: Synthetic Market Research Panels

Minds

Minds lets AI agents create synthetic Audiences and run Studies for concept testing, message testing, and segment comparison. Connect the hosted MCP server at https://getminds.ai/mcp using OAuth or a Minds API key. Star

YouTube MCP Server

HasData

Hosted YouTube MCP server for search, video metadata, channel data and timestamped transcripts. Connect Claude, Cursor or other MCP clients with HasData; no YouTube API key required.

PermitIntel

PermitIntel.ai

Building permit data and Florida contractor license checks over MCP. 81.8M permit records from the cities and counties that issue them, across 51 states. Four tools: verify_license, search_permits, lookup_contractor, roo

Zillow MCP Server