DataHub MCP Server

The DataHub MCP Server implements the Model Context Protocol (MCP), giving AI agents direct access to your DataHub metadata. Search for data assets, traverse lineage, inspect schemas, and generate SQL — all through natural language in tools like Cursor, Windsurf, Claude Desktop, and OpenAI.

Want to learn more about the motivation, architecture, and advanced use cases? Check out our deep dive blog post.

Deployment Options

• Managed MCP Server - Available on DataHub Cloud v0.3.12+
• Self-Hosted MCP Server - Available for DataHub Core

Capabilities

Search for Data

Find the right data by asking questions in plain English. Supports wildcard matching (revenue_*), field searches (tag:PII), and boolean logic ((sales OR revenue) AND quarterly).

Dive Deeper

Get usage stats, ownership, documentation, tags, glossary terms, and quality signals for any table, column, dashboard, & more — so agents can separate signal from noise.

Lineage & Impact Analysis

Trace data flow at table and column level, upstream or downstream, across multiple hops. Understand the origins of your data, and plan for upcoming changes.

Query Analysis & Authoring

Surface real SQL queries that reference a dataset — see join patterns, common filters, and aggregation behavior — then generate new queries grounded in actual usage.

Works Where You Work

Seamlessly integrates with Cursor, Windsurf, Claude Desktop, OpenAI, and any other MCP-compatible client.

Tools

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.

Mutation Tools

:::info Mutation tools are available in mcp-server-datahub v0.5.0+. They are enabled via the TOOLS_IS_MUTATION_ENABLED=true environment variable. :::

add_tags / remove_tags

Add or remove tags from entities or schema fields (columns). Supports bulk operations on multiple entities.

add_terms / remove_terms

Add or remove glossary terms from entities or schema fields. Useful for applying business definitions and data classification.

add_owners / remove_owners

Add or remove ownership assignments from entities. Supports different ownership types (technical owner, data owner, etc.).

set_domains / remove_domains

Assign or remove domain membership for entities. Each entity can belong to one domain.

update_description

Update, append to, or remove descriptions for entities or schema fields. Supports markdown formatting.

add_structured_properties / remove_structured_properties

Manage structured properties (typed metadata fields) on entities. Supports string, number, URN, date, and rich text value types.

User Tools

:::info User tools are available in mcp-server-datahub v0.5.0+. They are enabled via the TOOLS_IS_USER_ENABLED=true environment variable. :::

get_me

Retrieve information about the currently authenticated user, including profile details and group memberships.

Document Tools

:::info Document tools are available in mcp-server-datahub v0.5.0+. Document tools are automatically hidden if no documents exist in the catalog. :::

search_documents

Search for documents using keyword search with filters for platforms, domains, tags, glossary terms, and owners.

grep_documents

Search within document content using regex patterns. Useful for finding specific information across multiple documents.

save_document

Save standalone documents (insights, decisions, FAQs, notes) to DataHub's knowledge base. Documents are organized under a configurable parent folder.

Managed MCP Server Usage

For DataHub Cloud v0.3.12+, you can connect directly to the hosted MCP server endpoint — no local installation required.

:::info The managed MCP server endpoint is only available with DataHub Cloud v0.3.12+. For DataHub Core and older versions of DataHub Cloud, self-host the MCP server instead. :::

:::note Streamable HTTP Only DataHub's managed MCP server uses the streamable HTTP transport. Some older MCP clients (e.g. chatgpt.com) may only support the deprecated SSE transport — for those, use mcp-remote to bridge the gap. :::

Prerequisites

• The URL of your DataHub Cloud instance, e.g. https://<tenant>.acryl.io
• A personal access token

Connecting & Authenticating

Your managed MCP server URL is:

https://<tenant>.acryl.io/integrations/ai/mcp/

There are two ways to authenticate:

1. Authorization header — pass your token as a Bearer token in the Authorization header:

   Copy

   Authorization: Bearer <token>
   

2. Token in URL — append your token as a query parameter:

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   https://<tenant>.acryl.io/integrations/ai/mcp/?token=<token>
   

   This is a convenient alternative when your MCP client doesn't support custom headers.

For on-premises DataHub Cloud, replace <tenant>.acryl.io with your DataHub FQDN, e.g. https://datahub.example.com/integrations/ai/mcp/?token=<token>.

Configure

1. Open your claude_desktop_config.json file. You can find it by navigating to Claude Desktop -> Settings -> Developer -> Edit Config.
2. Update the file to include the following content. Be sure to replace <tenant> and <token> with your own values.

{
  "mcpServers": {
    "datahub-cloud": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://<tenant>.acryl.io/integrations/ai/mcp/?token=<token>"
      ]
    }
  }
}

Claude Code natively supports streamable HTTP, so no proxy or additional dependencies are needed.

Run the following command, replacing <tenant> and <token> with your own values:

claude mcp add --transport http datahub-cloud "https://<tenant>.acryl.io/integrations/ai/mcp/?token=<token>"

1. Make sure you're using Cursor v1.1 or newer.
2. Navigate to Cursor -> Settings -> Cursor Settings -> MCP -> add a new MCP server.
3. Enter the following into the file:

{
  "mcpServers": {
    "datahub-cloud": {
      "url": "https://<tenant>.acryl.io/integrations/ai/mcp/?token=<token>"
    }
  }
}

4. Once you've saved the file, confirm that the MCP settings page shows a green dot and the DataHub tools listed.

Most AI tools support remote MCP servers. Provide the hosted MCP server URL:

https://<tenant>.acryl.io/integrations/ai/mcp/?token=<token>

Make sure authentication mode is not set to "OAuth" (if applicable).

For clients that don't yet support remote MCP servers, use mcp-remote:

• Command: npx
• Args: -y mcp-remote https://<tenant>.acryl.io/integrations/ai/mcp/?token=<token>

Self-Hosted MCP Server Usage

Run the open-source MCP server locally. This works with any DataHub instance — both DataHub Core and DataHub Cloud.

Prerequisites

1. Install uv:

   bashCopy

   # macOS and Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. The URL of your DataHub instance's GMS endpoint, e.g. http://localhost:8080 or https://<tenant>.acryl.io

3. A personal access token

Connecting & Authenticating

The self-hosted server authenticates via environment variables:

• DATAHUB_GMS_URL — your DataHub GMS endpoint
• DATAHUB_GMS_TOKEN — your personal access token

These are passed to the mcp-server-datahub process at startup (see configuration examples below).

Configure

1. Run which uvx to find the full path to the uvx command.

2. Open your claude_desktop_config.json file. You can find it by navigating to Claude Desktop -> Settings -> Developer -> Edit Config.

3. Update the file to include the following content. Be sure to replace the placeholder values.

{
  "mcpServers": {
    "datahub": {
      "command": "<full-path-to-uvx>",  // e.g. /Users/hsheth/.local/bin/uvx
      "args": ["mcp-server-datahub@latest"],
      "env": {
        "DATAHUB_GMS_URL": "<your-datahub-url>",
        "DATAHUB_GMS_TOKEN": "<your-datahub-token>"
      }
    }
  }
}

Run the following command, replacing the placeholder values:

claude mcp add datahub \
  -e DATAHUB_GMS_URL="<your-datahub-url>" \
  -e DATAHUB_GMS_TOKEN="<your-datahub-token>" \
  -- uvx mcp-server-datahub@latest

1. Navigate to Cursor -> Settings -> Cursor Settings -> MCP -> add a new MCP server.
2. Enter the following into the file:

{
  "mcpServers": {
    "datahub": {
      "command": "uvx",
      "args": ["mcp-server-datahub@latest"],
      "env": {
        "DATAHUB_GMS_URL": "<your-datahub-url>",
        "DATAHUB_GMS_TOKEN": "<your-datahub-token>"
      }
    }
  }
}

3. Once you've saved the file, confirm that the MCP settings page shows a green dot and the DataHub tools listed.

For other AI tools, provide the following configuration:

• Command: uvx
• Args: mcp-server-datahub@latest
• Env:
  • DATAHUB_GMS_URL: <your-datahub-url>
  • DATAHUB_GMS_TOKEN: <your-datahub-token>

Troubleshooting

spawn uvx ENOENT

The full stack trace might look like this:

2025-04-08T19:58:16.593Z [datahub] [error] spawn uvx ENOENT {"stack":"Error: spawn uvx ENOENT\n    at ChildProcess._handle.onexit (node:internal/child_process:285:19)\n    at onErrorNT (node:internal/child_process:483:16)\n    at process.processTicksAndRejections (node:internal/process/task_queues:82:21)"}

Solution: Replace the uvx bit of the command with the output of which uvx.