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InsightFlow MCP Server

@ilissrk

Repository

Basic information

Category

Data & Analytics

Transports

stdio

Author

ilissrk

About 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 config

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

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

InsightFlow 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 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

Frequently asked questions

1

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.

2

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.

3

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`.

4

Does InsightFlow support real‑time analytics?

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

5

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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