Data Tools MCP Server

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

Local serverstdioPython

What is the Data Tools MCP Server?

The GenAI-powered toolkit for automated data intelligence.

What the server does

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.

  • 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
  • Conceptual Search - — Generate data product plans from natural language queries, bridging the gap between business questions and executable data product definitions. Learn more in the documentation

Available tools

The toolset is worth reading before you wire it up, because it tells you what the integration is really for:

  • Installation — For Windows and Linux, you can follow these steps. For macOS, please see the additional steps in the macOS section below
  • macOS — For macOS users, you may need to install the libomp library:
  • Configuration — Before running the project, you need to configure a LLM. This is used for tasks like generating business glossaries and predicting links between
  • Usage — Once you have built the semantic model, you can use the search method to perform a semantic search. The search function returns a pandas DataFrame

Credentials and setup notes

Configuration is passed through the environment: LLM_PROVIDER, OPENAI_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.

Installation

The server ships on PyPI as uvx, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.

Where it fits

Worth knowing first

  • It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
  • Keep per-call confirmation enabled while you learn its behaviour; it is the cheapest safeguard you have.

More MCP options

Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.

How to install the Data Tools MCP Server

{
  "mcpServers": {
    "data-tools": {
      "command": "uvx",
      "args": ["uvx"],
      "env": {
        "LLM_PROVIDER": "your-value",
        "OPENAI_API_KEY": "your-value"
      }
    }
  }
}

Add to claude_desktop_config.json, then restart Claude Desktop.

Configuration

VariableDescriptionRequired
LLM_PROVIDERConfiguration value read at startup.Optional
OPENAI_API_KEYCredential the server authenticates with.Yes

Example prompts to try

  • “Use Data Tools to Installation.”
  • “Use Data Tools to macOS.”
  • “Use Data Tools to Configuration.”

Frequently asked questions

<h3 align="center"><i>The GenAI-powered toolkit for automated data intelligence.</i></h3> Confirm the tools offered by your installed version in the connected client. Tool availability and permissions depend on the server and account configuration.