InsightFlow

by ilissrk

4 390 downloads Not rated yet

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.

Explore

- 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

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

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

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:

cp config/config.example.yaml config/config.yaml

5. Set up environment variables:

cp .env.example .env

1. Start the server:

bash
python app/main.py



2. Access the API documentation:

http://localhost:8000/docs

The system can be configured through config.yaml or environment variables:

yaml
server:
host: "0.0.0.0"
port: 8000
debug: false

mcp:
enabled: true
websocket_path: "/ws"
max_connections: 100

ai:
model_name: "claude-2"
temperature: 0.7
max_tokens: 2000

bash
pytest tests/
```

1. Data Analysis
- Analyze datasets with configurable metrics
- Generate statistical insights
- Support for time-series analysis

2. Query Data
- Flexible data querying capabilities
- Filter and aggregate data
- Export results in multiple formats

3. Generate Insight
- AI-powered data interpretation
- Trend identification
- Anomaly detection

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 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

🛠️ 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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About

Explore

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

🚀 Features

🛠️ Technology Stack

📋 Prerequisites

🔧 Installation

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