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dg-mcp-server

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dg-mcp-server

Model context Protocol server for Datagroom

✨ Features | 🚀 Getting Started | 🛠️ Tools | 🧑‍💻 Development

✨ Features

A powerful and flexible Datagroom MCP server implementation to read the data and let the LLM handle the reasoning of the data.

Related MCP server: Databricks MCP Server

🚀 Getting Started

Requirements

Prerequisites

  • Install the python-sdk dependencies like uv, mcp etc.

  • Clone the project

  • Run uv sync to install the dependencies

Letta ADE (Local installation - Letta Desktop)

  • Set BASIC_AUTH_USER and BASIC_AUTH_PASS on the os using export or set command.

  • Run the mcp server using uv run main.py

  • Create a new agent in Letta ADE

  • Go to Tool manager, add a new custom server.

  • Provide the server name as DgGatewayMcpServer.

  • Provide the url as http://127.0.0.1:8001/mcp

  • Connect with the server and add it and attach required tools to the agent.

🛠️ Tools

server_info

Get the mcp server information

get_datasets

Get all the datasets that are hosted on the Datagroom

get_columns_and_filters_metadata

Get all the columns or headers for a given dataset name, dataset view and for a given user. If the dataset view is not provided it is assumed to be default view. It also provides the data like filters for a dataset, the metadata attributes for each column/header and the access control list for the given dataset.

Parameters:

  • dsName (string, required)

    • The datasetname for which the metadata is to be retrieved

  • dsView (string, optional, default = default)

    • The view for which the the metadata to be retrieved

🧑‍💻 Development

Running the server with mcp-inspector

Run uv run mcp dev main.py

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive
ResponsivenessNo issues

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