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Intugle MCP Server

@Intugle

リポジトリ

基本情報

カテゴリ

データと分析

トランスポート

stdio

作者

Intugle

投稿者

raphael-intugle

Intugle MCP Server について

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 の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

Intugle MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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.

よくある質問

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.

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