dg-mcp-server

hrishi2710/dg-mcp-server

3.2

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The Model Context Protocol (MCP) server for Datagroom is designed to facilitate seamless communication and data management between various machine learning models and applications.

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.

🚀 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