Intugle Mcp

by Intugle

366 downloads

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About

Generate automated semantic models using data engineering agents and built data products on demand

Details

Author: Intugle
Downloads: 366
Categories: Productivity, 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 Intugle Mcp

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": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "intugle mcp": {
            "data-tools": {
                "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": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "intugle mcp": {
            "data-tools": {
                "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": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "intugle mcp": {
            "data-tools": {
                "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": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "intugle mcp": {
            "data-tools": {
                "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": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

From the repository

Install the package with pip install intugle in a virtual environment. Configure an LLM provider via environment variables (LLM_PROVIDER, API_KEY) and optionally a Qdrant vector database for semantic search. Use the SemanticModel class to profile, link, and glossarize datasets, then use DataProduct to generate unified data products.

Claude Desktop / Cursor

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

{
    "mcpServers": {
        "intugle mcp": {
            "data-tools": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

McpServers

{
    "data-tools": {
        "command": "python",
        "args": [
            "-m",
            "venv",
            ".venv"
        ]
    }
}

<p align="center">
Intugle Logo
<h3 align="center"><i>The GenAI-powered toolkit for automated data intelligence.</i></h3>
</p>

<p align="center">
<a href="https://pepy.tech/projects/intugle">

</a>
Release
<a href="https://www.python.org/">

</a>
<a href="https://github.com/Intugle/data-tools/blob/main/CONTRIBUTING.md">

</a>
<a href="https://opensource.org/licenses/Apache-2.0">

</a>
<a href="https://github.com/Intugle/data-tools/issues">
Open Issues
</a>

</p>

Transform Fragmented Data into Connected Semantic Data Model

Overview

Intugle’s GenAI-powered open-source Python library builds a semantic data model over your existing data systems. At its core, it discovers meaningful links and relationships across data assets — enriching them with profiles, classifications, and business glossaries. With this connected knowledge layer, you can enable semantic search and auto-generate queries to create unified data products, making data integration and exploration faster, more accurate, and far less manual.

Who is this for?

Data Engineers & Architects often spend weeks manually profiling, classifying, and stitching together fragmented data assets. With Intugle, they can automate this process end-to-end, uncovering meaningful links and relationships to instantly generate a connected semantic layer.
Data Analysts & Scientists spend endless hours on data readiness and preparation before they can even start the real analysis. Intugle accelerates this by providing contextual intelligence, automatically generating SQL and reusable data products enriched with relationships and business meaning.
Business Analysts & Decision Makers are slowed down by constant dependence on technical teams for answers. Intugle removes this bottleneck by enabling natural language queries and semantic search, giving them trusted insights on demand.

Features

Semantic Data Model - Transform raw, fragmented datasets into an intelligent semantic graph that captures entities, relationships, and context — the foundation for connected intelligence.
Business Glossary & Semantic Search: Auto-generate a business glossary and enable search that understands meaning, not just keywords — making data more accessible across technical and business users.
Data Products - Instantly generate SQL and reusable data products enriched with context, eliminating manual pipelines and accelerating data-to-insight.

Getting Started

Installation

For Windows and Linux, you can follow these steps. For macOS, please see the additional steps in the macOS section below.

Before installing, it is recommended to create a virtual environment:

``

bash

python -m venv .venv

source .venv/bin/activate





Then, install the package:

bash

pip install intugle





macOS






For macOS users, you may need to install the

libomp

 library:

bash

brew install libomp





If you installed Python using the official installer from python.org, you may also need to install SSL certificates by running the following command in your terminal. Please replace

3.XX

 with your specific Python version. This step is not necessary if you installed Python using Homebrew.

bash

/Applications/Python\ 3.XX/Install\ Certificates.command





Configuration






Before running the project, you need to configure a LLM. This is used for tasks like generating business glossaries and predicting links between tables.



You can configure the LLM by setting the following environment variables:

LLM_PROVIDER: The LLM provider and model to use (e.g., openai:gpt-3.5-turbo) following LangChain's conventions API_KEY

: Your API key for the LLM provider. The exact name of the variable may vary from provider to provider.



Here's an example of how to set these variables in your environment:

bash

export LLM_PROVIDER="openai:gpt-3.5-turbo"

export OPENAI_API_KEY="your-openai-api-key"





Quickstart






For a detailed, hands-on introduction to the project, please see our quickstart notebooks:



| Domain                  | Notebook                                                                                                             | Open in Colab                                                                                                                                                           |

| ----------------------- | -------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

| Healthcare          |

quickstart_healthcare.ipynb

 |  |

| Tech Manufacturing  |

quickstart_tech_manufacturing.ipynb

 |  |

| FMCG                |

quickstart_fmcg.ipynb

             |              |

| Sports Media        |

quickstart_sports_media.ipynb

 |  |

| Databricks Unity Catalog [Health Care] |

quickstart_healthcare_databricks.ipynb

 | Databricks Notebook Only |

| Snowflake Horizon Catalog [ FMCG ] |

quickstart_fmcg_snowflake.ipynb

 | Snowflake Notebook Only |

| Native Snowflake with Cortex Analyst [ Tech Manufacturing ] |

quickstart_native_snowflake.ipynb

 |  |

| Native Databricks with AI/BI Genie [ Tech Manufacturing ] |

quickstart_native_databricks.ipynb` | [

](https://colab.research.google.com/github/Intugle/data-tools/blob/main/noteboo

…

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Details

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Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Overview

Who is this for?

Features

Getting Started

Installation

macOS

Configuration

Quickstart

Install

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