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Intugle MCP Server

@Intugle

Repository

Basic information

Category

Data & Analytics

Last updated

Transports

stdio

Author

Intugle

Submitted by

raphael-intugle

About Intugle MCP Server

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

#semantic-model#data-product#semantic-layer#data-analysis

Intugle MCP Server config

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

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

Intugle 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 Intugle Mcp?

Intugle Mcp is a GenAI-powered open-source Python library that builds a semantic data model over existing data systems. It profiles, classifies, and discovers links and relationships across data assets, enriches them with business glossaries, and enables semantic search and auto-generated queries for unified data products.

How to use Intugle Mcp?

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.

Key features of Intugle Mcp

  • Semantic data model that transforms fragmented datasets into a connected graph.
  • Auto-generated business glossary and semantic search across technical and business users.
  • Data product generation with auto-joined SQL queries and reusable outputs.

Use cases of Intugle Mcp

  • Data engineers automate profiling, classification, and linking of fragmented data assets.
  • Data analysts and scientists accelerate data readiness with contextual intelligence and auto-generated SQL.
  • Business analysts query data via natural language semantic search without technical dependencies.
  • Organizations unify data from heterogeneous sources (CSV, Snowflake, Databricks) into consistent data products.

Frequently asked questions

1

How do I configure an LLM for Intugle Mcp?

Set the `LLM_PROVIDER` environment variable (e.g., `openai:gpt-3.5-turbo`) and the corresponding API key (e.g., `OPENAI_API_KEY`).

2

What are the runtime requirements?

Python with a virtual environment. On macOS, you may need to install `libomp` via Homebrew and run SSL certificates command if using official Python installer.

3

Does Intugle Mcp work with Databricks or Snowflake?

Yes. Quickstart notebooks are provided for Databricks Unity Catalog, Snowflake Horizon Catalog, and native Snowflake with Cortex Analyst, as well as native Databricks with AI/BI Genie.

4

What license is Intugle Mcp released under?

Apache 2.0.

5

How do I enable semantic search?

You need a running Qdrant instance (Docker command provided) and set `QDRANT_URL` and optionally `QDRANT_API_KEY`. Only OpenAI embeddings are currently supported for semantic search.

Comments

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