InsightFlow

by ilissrk

4 stars

390 downloads

Not rated

GitHub

About

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.

Details

Author: ilissrk
GitHub stars: 4
Downloads: 390
Categories: Search

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name InsightFlow

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

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

JSON

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

JSON

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

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

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

JSON

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

JSON

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

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

From the repository

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.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

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

McpServers

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

InsightFlow

InsightFlow is an advanced analytics platform that combines real-time data processing with AI-powered insights using the Model Context Protocol (MCP). It provides seamless integration with Claude AI for intelligent data analysis and decision support.

🚀 Features

- MCP Integration: Full support for Model Context Protocol, enabling advanced AI capabilities
- Real-time Analytics: Process and analyze data streams in real-time
- AI-Powered Insights: Leverage Claude AI for intelligent data interpretation
- Flexible Data Processing: Support for multiple data sources and formats
- RESTful & WebSocket APIs: Comprehensive API support for various integration needs

🛠️ Technology Stack

- Backend: Python 3.9+, FastAPI
- AI Integration: Anthropic Claude API
- Data Processing: Pandas, NumPy
- Database: SQLAlchemy (supports multiple databases)
- API: REST + WebSocket
- Protocol: Model Context Protocol (MCP)

📋 Prerequisites

- Python 3.9 or higher
- Anthropic API key
- Redis (for caching and message queuing)

🔧 Installation

1. Clone the repository:

git clone https://github.com/yourusername/insightflow.git

cd insightflow

2. Create and activate virtual environment:

python -m venv venv

source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Configure environment:
```bash
cp config/config.example.yaml config/config.yaml

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Details

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Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

InsightFlow

🚀 Features

🛠️ Technology Stack

📋 Prerequisites

🔧 Installation

Install

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