InsightFlow MCP Server

@ilissrk

代码仓库

基本信息

分类

数据与分析

传输方式

stdio

作者

ilissrk

关于 InsightFlow MCP Server

InsightFlow - a real-time analytics dashboard server with an MCP (Message Control Protocol) architecture that integrates with AI services like Claude or Cursor. This solution enables real-time data analytics with natural language query capabilities.

#insightflow#analytics #ai-integration

InsightFlow 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": {
    "InsightFlow": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

InsightFlow 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 InsightFlow?

InsightFlow is an advanced analytics platform that combines real-time data processing with AI‑powered insights using the Model Context Protocol (MCP). It integrates with Claude AI for intelligent data analysis and decision support, targeting developers and data analysts.

How to use InsightFlow?

Install Python 3.9+, set up a virtual environment, install dependencies, configure the YAML file and environment variables with your Anthropic API key and Redis details, then run python app/main.py. Access the API docs at http://localhost:8000/docs and use the REST or WebSocket endpoints to execute MCP tools.

Key features of InsightFlow

MCP integration for advanced AI capabilities
Real‑time data stream processing
AI‑powered insights via Claude AI
Flexible multi‑source data processing
RESTful and WebSocket APIs

Use cases of InsightFlow

Analyze streaming data with configurable metrics and statistical insights
Perform flexible data queries with filtering, aggregation, and export
Use AI to detect trends, anomalies, and generate data interpretations
Integrate real‑time analytics into existing applications via WebSocket

常见问题

What are the prerequisites for running InsightFlow?

Python 3.9 or higher, an Anthropic API key, and Redis for caching and message queuing are required.

What APIs does InsightFlow provide?

It offers REST endpoints (`GET /tools`, `POST /tool/{tool_name}`) and a WebSocket endpoint (`WS /ws`) for real‑time communication.

How do I configure InsightFlow?

Configuration is done through `config.yaml` or environment variables, where you set server host/port, MCP settings, and AI model parameters like `temperature` and `max_tokens`.

Does InsightFlow support real‑time analytics?

Yes, it processes data streams in real time and provides a WebSocket endpoint for live communication.

What MCP tools are included?

Three tools are built in: Data Analysis (statistical and time‑series metrics), Query Data (filtering, aggregation, export), and Generate Insight (AI‑powered trend identification and anomaly detection).

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