dg-mcp-server

Model context Protocol server for Datagroom

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

✨ Features <a id="features"></a>

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

🚀 Getting Started <a id="getting-started"></a>

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 <a id="tools"></a>

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 <a id="development"></a>

Running the server with mcp-inspector

Run uv run mcp dev main.py