InsightFlow

Name: InsightFlow
Author: ilissrk

@ilissrk

About InsightFlow

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

Basic information

Config

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

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

Tools

No tools detected

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

Overview

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

FAQ from InsightFlow

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).

Comments

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