DataHub Extension

Add DataHub MCP Server as a goose Extension

This tutorial covers how to add the DataHub MCP Server as a goose extension to enable AI-powered data discovery, lineage exploration, and metadata querying across your data ecosystem.

Quick Install

goose Desktop

Launch the installer

goose CLI

Command

uvx mcp-server-datahub@latest

Environment Variables

DATAHUB_GMS_URL: <your-datahub-url>
DATAHUB_GMS_TOKEN: <your-datahub-token>

What is DataHub?

DataHub is an open-source metadata platform that provides a unified view of your data ecosystem, cataloging datasets, dashboards, pipelines, and more with rich metadata including ownership, lineage, usage statistics, and data quality information.

The DataHub MCP Server enables AI agents to:

Find trustworthy data using natural language search with trust signals like popularity, quality, and lineage
Explore data lineage to understand upstream and downstream dependencies at table and column level
Understand business context through glossaries, domains, data products, and organizational metadata
Generate SQL queries with help from documentation, lineage, and popular query patterns

Learn more: DataHub MCP Server Guide | GitHub Repository

Prerequisites

Before using the DataHub MCP Server, ensure you have:

Python 3.10+ and uv package manager installed
A DataHub instance: DataHub Cloud or self-hosted DataHub
A Personal Access Token from your DataHub instance

Configuration

Note that you'll need uv installed on your system to run this command, as it uses uvx.

goose Desktop

goose CLI

Example Usage

Finding Trustworthy Data

Find datasets related to your project by describing what you need in natural language.

goose Prompt

Find all datasets related to customer transactions that are owned by the analytics team

goose Output

Desktop

The DataHub extension will search across your data catalog and return relevant datasets with their metadata, including:

Dataset names and descriptions
Column names, types, descriptions, and labels
Owners
Tags, properties, and glossary terms
Usage statistics
Data quality status

Exploring Data Lineage

I want to remove the "timestamp_seconds" column from the customer_orders table. What will break?

goose Prompt

Show me the upstream lineage for the customer_orders table

goose Output

Desktop

The extension will traverse the lineage graph and show any:

Source tables and datasets
Transformation pipelines
ETL jobs and workflows
Downstream columns

That would be impacted by removing the column.

Generating SQL Queries

How do I calculate the number of orders made in the USA last year?

goose Prompt

What are the most common queries run against the customer_orders dataset?

goose Output

Desktop

The extension will retrieve SQL query history showing:

Frequently executed queries
Common join patterns
Filter conditions
Aggregation patterns

In addition to column names, types, descriptions, and any labels. This will enable the agent to generate high quality SQL to answer the question.

Understanding Data Quality & Freshness

Determine whether a dataset is trustworthy before using it.

goose Prompt

Is the customer_orders table fresh and free of data quality issues?

goose Output

Desktop

The extension will fetch:

Latest data quality assertions and test results
Freshness / staleness metrics
Schema change history
SLA or SLO metadata
Owner-provided health status

Allowing the agent to warn the user or confirm data trustworthiness.

Capabilities

The DataHub MCP Server provides the following tools:

search

Search DataHub using structured keyword search (/q syntax) with boolean logic, filters, pagination, and optional sorting by usage metrics.

get_lineage

Retrieve upstream or downstream lineage for any entity (datasets, columns, dashboards, etc.) with filtering, query-within-lineage, pagination, and hop control.

get_dataset_queries

Fetch real SQL queries referencing a dataset or column—manual or system-generated—to understand usage patterns, joins, filters, and aggregation behavior.

get_entities

Fetch detailed metadata for one or more entities by URN; supports batch retrieval for efficient inspection of search results.

list_schema_fields

List schema fields for a dataset with keyword filtering and pagination, useful when search results truncate fields or when exploring large schemas.

get_lineage_paths_between

Retrieve the exact lineage paths between two assets or columns, including intermediate transformations and SQL query information.

Resources

Troubleshooting

Connection Issues

If you're having trouble connecting to DataHub:

Verify your DATAHUB_GMS_URL is correct:
- For DataHub Cloud: https://your-tenant.acryl.io
- For local instances: http://localhost:8080
- For on-premises: https://datahub.your-company.com
Confirm your Personal Access Token is valid and has appropriate permissions
Check network connectivity and firewall rules

Installation Issues

If uvx is not found:

Ensure uv is installed: curl -LsSf https://astral.sh/uv/install.sh | sh
Restart your terminal or source your shell configuration
Verify installation: which uvx