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mcp-server-datahub

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The official Model Context Protocol (MCP) server for DataHub (https://datahub.com)

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# DataHub MCP Server

A [Model Context Protocol](https://modelcontextprotocol.io/) server implementation for [DataHub](https://datahub.com/).

## What is DataHub?

[DataHub](https://datahub.com/) is an open-source context platform that gives organizations a single pane of glass across their entire data supply chain. DataHub unifies data discovery, governance, and observability under one roof for every table, column, dashboard pipeline, document, and ML Model.

With powerful features for data profiling, data quality monitoring, data lineage, data ownership, and data classification, DataHub brings together both technical and organizational context, allowing teams to find, create, use, and maintain trustworthy data.

## Use Cases

The DataHub MCP Server enables AI agents to:

- **Find trustworthy data**: Search across the entire data landscape using natural language to find the tables, columns, dashboards, & metrics that can answer your most mission-critical questions. Leverage trust signals like data popularity, quality, lineage, and query history to get it right, every time.

- **Explore data lineage & plan for data changes**: Understand the impact of important data changes _before_ they impact your downstream users through rich data lineage at the asset & column level.

- **Understand your business**: Navigate important organizational context like business glossaries, data domains, data products products, _and_ data assets. Understand how key metrics, business processes, and data relate to one another.

- **Explain & generate SQL queries**: Generate accurate SQL queries to answer your most important questions with the help of critical context like data documentation, data lineage, and popular queries across the organization.

## Why DataHub MCP Server?

With DataHub MCP Server, you can instantly give AI agents visibility into of your entire data ecosystem. Find and understand data stored in your databases, data lake, data warehouse, and BI visualization tools. Explore data lineage, understand usage & use cases, identify the data experts, and generate SQL - all through natural language.

###  **Structured Search with Context Filtering**

Go beyond keyword matching with powerful query & filtering syntax:

- Wildcard matching: `/q revenue_*` finds `revenue_kpis`, `revenue_daily`, `revenue_forecast`
- Field searches: `/q tag:PII` finds all PII-tagged data
- Boolean logic: `/q (sales OR revenue) AND quarterly` for complex queries

###  **SQL Intelligence & Query Generation**

Access popular SQL queries, and generate new ones with accuracy:

- See how analysts query tables (perfect for SQL generation)
- Understand join patterns and common filters
- Learn from production query patterns

###  **Table & Column-Level Lineage**

Trace data flow at both the table and column level:

- Track how `user_id` becomes `customer_key` downstream
- Understand transformation logic
- Upstream and downstream exploration (1-3+ hops)
- Handle enterprise-scale lineage graphs

###  **Understands Your Data Ecosystem**

Understand how your data is organized before searching:

- Discover relevant data domains, owners, tags and glossary terms
- Browse across data platforms and environments
- Navigate the complexities of your data landscape without guessing

## Usage

See instructions in the [DataHub MCP server docs](https://docs.datahub.com/docs/features/feature-guides/mcp).

### Local stdio deployment

The default transport remains stdio for local MCP clients:

```bash
uvx mcp-server-datahub
```

The stdio server loads `DATAHUB_GMS_URL` and `DATAHUB_GMS_TOKEN` from the
environment, falling back to `~/.datahubenv` created by `datahub init`. This is
the local deployment mode and does not require HTTP authentication.

The local CLI also supports SSE for trusted, local-network use. SSE does not
provide the per-request bearer authentication used by the dedicated HTTP
entry point and should not be exposed as a shared network service.

### Docker HTTP deployment

The container runs the stateless HTTP transport on port 8000. It deliberately
does not use a server-wide `DATAHUB_GMS_TOKEN`; each MCP client supplies its own
DataHub token so DataHub permissions and audit identity are preserved per user.

> **Breaking change for existing HTTP deployments:** HTTP now has a separate
> `mcp-server-datahub-http` entry point. It refuses to start when
> `DATAHUB_GMS_TOKEN` is configured, and every client must send its own DataHub
> bearer token. Local stdio and SSE behavior are unchanged.

```bash
docker run --rm -p 8000:8000 \
  -e DATAHUB_GMS_URL=https://your-datahub.example \
  acryldata/mcp-server-datahub:latest
```

The same isolated HTTP entry point is available outside Docker:

```bash
DATAHUB_GMS_URL=https://your-datahub.example mcp-server-datahub-http
```

Connect to `http://localhost:8000/mcp` and include the token on every request:

```text
Authorization: Bearer <datahub-personal-access-token>
```

Tokens are accepted only in the `Authorization` header; query-string tokens
such as `?access_token=...` are rejected.

DataHub must have `METADATA_SERVICE_AUTH_ENABLED=true` to establish caller
identity. When upstream auth is disabled, GMS may return a synthetic,
non-existent actor for arbitrary bearer values; the MCP server cannot correct
that upstream configuration.

For a local build, create a `.env` file containing `DATAHUB_GMS_URL`, then run:

```bash
docker compose up --build
```

The image and server expose an unauthenticated `GET /health` endpoint for
container, load-balancer, and Kubernetes probes. The endpoint reports process
health only and does not contact DataHub.

```yaml
livenessProbe:
  httpGet:
    path: /health
    port: 8000
readinessProbe:
  httpGet:
    path: /health
    port: 8000
```

Bearer tokens must be protected in transit. Terminate TLS and apply
rate-limiting at an ingress or reverse proxy before exposing the HTTP deployment
outside a trusted network. Rate limiting is especially important because each
new invalid token can trigger a validation request to GMS.

Successfully verified tokens and their DataHub clients are cached for five
minutes. If a cached request receives a 401 or 403 from DataHub, that client is
evicted and the next request revalidates the token. Failed validations are not
cached; concurrent upstream validations are bounded to protect DataHub.

## Demo

Check out the [demo video](https://youtu.be/VXRvHIZ3Eww?t=1878), done in collaboration with the team at Block.

## 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

These tools allow modifying metadata in DataHub. 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

These tools provide information about the authenticated user. Enabled via `TOOLS_IS_USER_ENABLED=true`.

`get_me`

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

### Document Tools

These tools work with documents (knowledge articles, runbooks, FAQs) stored in DataHub. 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.

## Configuration

### Environment Variables

| Variable | Default | Description |
|----------|---------|-------------|
| `DATAHUB_GMS_URL` | none | DataHub server URL. Required in HTTP mode; stdio can also load it from `~/.datahubenv`. |
| `DATAHUB_GMS_TOKEN` | none | Legacy stdio/SSE credential. HTTP mode refuses to start when this shared credential is set. |
| `FASTMCP_HOST` | FastMCP default (`127.0.0.1`); image default (`0.0.0.0`) | HTTP/SSE bind address. |
| `FASTMCP_PORT` | `8000` | HTTP/SSE listen port; also used by the image health check. |
| `TOOLS_IS_MUTATION_ENABLED` | `false` | Enable mutation tools (add/remove tags, owners, etc.) |
| `TOOLS_IS_USER_ENABLED` | `false` | Enable user tools (get_me) |
| `DATAHUB_MCP_DOCUMENT_TOOLS_DISABLED` | `false` | Completely disable document tools |
| `SAVE_DOCUMENT_TOOL_ENABLED` | `true` | Enable/disable the save_document tool |
| `SAVE_DOCUMENT_PARENT_TITLE` | `Shared` | Title for the parent folder of saved documents |
| `SAVE_DOCUMENT_ORGANIZE_BY_USER` | `false` | Organize saved documents by user |
| `SAVE_DOCUMENT_RESTRICT_UPDATES` | `true` | Only allow updating documents in the shared folder |
| `TOOL_RESPONSE_TOKEN_LIMIT` | `80000` | Maximum tokens for tool responses |
| `ENTITY_SCHEMA_TOKEN_BUDGET` | `16000` | Token budget per entity for schema fields |
| `DISABLE_NEWER_GMS_FIELD_DETECTION` | `false` | Disable adaptive GMS field detection |
| `DATAHUB_MCP_DISABLE_DEFAULT_VIEW` | `false` | Disable automatic default view application |
| `SEMANTIC_SEARCH_ENABLED` | `false` | Enable semantic (AI-powered) search |

## Example: Data Discovery & Understanding Flow (for Agents Using DataHub Tools)

This example illustrates how an AI agent could orchestrate DataHub MCP tools to answer a user's data question. It demonstrates the decision-making flow, which tools are called, and how responses are used.

### 1. User Asks a Question

**Example:**
> "How can I find out how many pets were adopted last month?"

The agent recognizes this as a **data discovery → query construction** workflow. It needs to (a) find relevant datasets, (b) inspect metadata, (c) construct a correct SQL query.

### 2. Search for Relevant Datasets

The agent begins with the `search` tool (semantic or keyword depending on configuration).

**Tool:** `search`  
**Input:** natural-language query

**Example Call:**
```json
{
  "query": "pet adoptions"
}
```

**Purpose:** Identify datasets like `adoptions`, `pet_profiles`, `pet_details`.

### 3. Inspect Candidate Datasets

For each dataset returned by search, the agent may fetch metadata.

#### 3.1 List Schema Fields

**Tool:** `list_schema_fields`  
**Input:** URN of dataset  
**Purpose:** Understand schema, datatype, candidate fields for querying.

Example:
```json
{
  "urn": "urn:li:dataset:(urn:li:dataPlatform:snowflake,mydb.public.adoptions,PROD)"
}
```

#### 3.2 Fetch Lineage (optional)

**Tool:** `get_lineage`  
**Purpose:** Determine whether dataset is derived or authoritative.

#### 3.3 Get Example Queries

**Tool:** `get_dataset_queries`  
**Purpose:** Learn typical usage patterns and query templates for the dataset.

### 4. Understand Entity Relationships

If the question requires joining or entity navigation (e.g., connecting pets → adoptions):

#### get_entities
To retrieve entities related to a given URN, such as upstream/downstream tables.

#### get_lineage_paths_between
To calculate exact lineage paths between datasets if needed (e.g., between `pet_profiles` and `adoptions`).

### 5. Construct a Query

The agent now has:

- The correct dataset
- Its schema
- Key fields
- Sample queries
- Relationship and lineage context

The agent constructs an accurate SQL query.

Example:

```sql
SELECT COUNT(*)
FROM mydb.public.adoptions
WHERE adoption_date >= DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1' MONTH)
  AND adoption_date < DATE_TRUNC('month', CURRENT_DATE);
```

### 6. Return the Final Answer

The agent may either:

- return the SQL directly,
- run it (if in an environment where query execution is allowed), or
- provide a natural-language answer based on query output.

### Summary of Tools Used

| Tool Name | Purpose |
|-----------|---------|
| `search` | Find relevant datasets for the question. |
| `list_schema_fields` | Understand dataset structure. |
| `get_lineage` | Assess data authority and provenance. |
| `get_dataset_queries` | Learn how the dataset is typically queried. |
| `get_entities` | Retrieve related entities for context. |
| `get_lineage_paths_between` | Understand deeper relationships between datasets. |

## Developing

See [DEVELOPING.md](DEVELOPING.md).

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