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Servers/Workplace & Productivity/Intugle MCP
Workplace & ProductivityOfficial
I

Intugle MCP

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

151 starsUpdated Aug 17, 2026
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Overview

Transform Fragmented Data into Connected Semantic Data Model 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.

Installation

pip install intugle[streamlit]

Configuration

Configuration example is not available yet.

Capabilities

  • Profile and classify your data sources

    Analyze CSV, Parquet, Excel, or database tables to understand structure, data types, and characteristics via `SemanticModel.build()`.

  • Discover links and relationships across tables

    Automatically identify primary/foreign keys and composite relationships between fragmented datasets.

  • Generate a business glossary

    Create business-friendly terms for your data columns and tables, enabling natural language queries.

  • Search your data semantically

    Find relevant columns using natural language (e.g., "reason for hospital visit") with `SemanticModel.search()`.

  • Build unified data products

    Define fields across tables and let `DataProduct.build()` auto-generate SQL with required joins and aggregations.

  • Expose your semantic layer to AI assistants

    Start the built-in MCP server with `intugle-mcp` so agents can explore your data structure via `get_tables` and `get_schema`.

Tags

  • productivity

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