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

• Access to a locally hosted [Datagroom gateway] (https://github.com/h-tendy/datagroom-gateway) instance

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