goose/documentation/docs/mcp/datahub-mcp.mdx

---
title: DataHub Extension
description: Add DataHub MCP Server as a goose Extension
---

import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import YouTubeShortEmbed from '@site/src/components/YouTubeShortEmbed';
import CLIExtensionInstructions from '@site/src/components/CLIExtensionInstructions';
import GooseDesktopInstaller from '@site/src/components/GooseDesktopInstaller';

<YouTubeShortEmbed videoUrl="https://www.youtube.com/embed/VXRvHIZ3Eww?start=1878" />

This tutorial covers how to add the [DataHub MCP Server](https://github.com/acryldata/mcp-server-datahub) as a goose extension to enable AI-powered data discovery, lineage exploration, and metadata querying across your data ecosystem.

:::tip Quick Install
<Tabs groupId="interface">
  <TabItem value="ui" label="goose Desktop" default>
  [Launch the installer](goose://extension?cmd=uvx&arg=mcp-server-datahub%40latest&id=datahub-mcp&name=DataHub&description=Data%20discovery%20and%20metadata%20platform%20integration&env=DATAHUB_GMS_URL%3DDataHub%20GMS%20URL%20(e.g.%2C%20https%3A%2F%2Fyour-instance.acryl.io%20or%20http%3A%2F%2Flocalhost%3A8080)&env=DATAHUB_GMS_TOKEN%3DDataHub%20Personal%20Access%20Token)
  </TabItem>
  <TabItem value="cli" label="goose CLI">
  **Command**
  ```sh
  uvx mcp-server-datahub@latest
  ```
  </TabItem>
</Tabs>
  **Environment Variables**
  ```
  DATAHUB_GMS_URL: <your-datahub-url>
  DATAHUB_GMS_TOKEN: <your-datahub-token>
  ```
:::

## What is DataHub?

[DataHub](https://datahub.com/) 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](https://docs.datahub.com/docs/features/feature-guides/mcp) | [GitHub Repository](https://github.com/acryldata/mcp-server-datahub)

## Prerequisites

Before using the DataHub MCP Server, ensure you have:

- **Python 3.10+** and **[uv](https://docs.astral.sh/uv/#installation)** package manager installed
- A **DataHub instance**: [DataHub Cloud](https://www.datahub.com) or [self-hosted DataHub](https://docs.datahub.com/docs/quickstart)
- A **[Personal Access Token](https://docs.datahub.com/docs/authentication/personal-access-tokens)** from your DataHub instance

## Configuration

:::info
Note that you'll need [uv](https://docs.astral.sh/uv/#installation) installed on your system to run this command, as it uses `uvx`.
:::

<Tabs groupId="interface">
  <TabItem value="ui" label="goose Desktop" default>

    <GooseDesktopInstaller
      extensionId="datahub-mcp"
      extensionName="DataHub"
      description="Data discovery and metadata platform integration"
      type="stdio"
      command="uvx"
      args={["mcp-server-datahub@latest"]}
      timeout={300}
      envVars={[
        { name: "DATAHUB_GMS_URL", label: "DataHub GMS URL (e.g., https://your-instance.acryl.io or http://localhost:8080)" },
        { name: "DATAHUB_GMS_TOKEN", label: "DataHub Personal Access Token" }
      ]}
      apiKeyLink="https://docs.datahub.com/docs/authentication/personal-access-tokens"
      apiKeyLinkText="DataHub Personal Access Token"
    />

  </TabItem>
  <TabItem value="cli" label="goose CLI">

    <CLIExtensionInstructions
      name="DataHub"
      description="Data discovery and metadata platform integration"
      type="stdio"
      command="uvx mcp-server-datahub@latest"
      timeout={300}
      envVars={[
        { key: "DATAHUB_GMS_URL", value: "https://your-instance.acryl.io" },
        { key: "DATAHUB_GMS_TOKEN", value: "▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪" }
      ]}
      infoNote={
        <>
          Get your Personal Access Token from{" "}
          <a href="https://docs.datahub.com/docs/authentication/personal-access-tokens" target="_blank" rel="noopener noreferrer">
            DataHub documentation
          </a>. Use your DataHub GMS URL (e.g., https://your-instance.acryl.io for DataHub Cloud or http://localhost:8080 for local instances).
        </>
      }
    />

  </TabItem>
</Tabs>

## 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

:::note 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

:::note 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

:::note 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

:::note 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

- [DataHub MCP Server GitHub](https://github.com/acryldata/mcp-server-datahub)
- [DataHub Documentation](https://docs.datahub.com/docs/)
- [DataHub MCP Server Guide](https://docs.datahub.com/docs/features/feature-guides/mcp)
- [Demo Video](https://youtu.be/VXRvHIZ3Eww?t=1878)

## Troubleshooting

### Connection Issues

If you're having trouble connecting to DataHub:

1. 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`

2. Confirm your Personal Access Token is valid and has appropriate permissions

3. Check network connectivity and firewall rules

### Installation Issues

If `uvx` is not found:

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