Databricks Unity Catalog (UC) allows for detailed documentation of your data assets, including catalogs, schemas, tables, and columns. Documenting
Most AI and media services work still happens through a UI a human drives. MCP Databricks Server MCP server moves it into the conversation instead. Databricks Unity Catalog (UC) allows for detailed documentation of your data assets, including catalogs, schemas, tables, and columns. Documenting these assets thoroughly requires an investment of time. One common question is: what are the.
This Model Context Protocol (MCP) server is designed to interact with Databricks, with a strong focus on leveraging Unity Catalog (UC) metadata and enabling comprehensive data lineage exploration. The primary goal is to equip an AI agent with a comprehensive set of tools, enabling it to become independent in answering questions about your data. By autonomously exploring UC, understanding data structures, analyzing data lineage (including notebook and job dependencies), and executing SQL queries, the agent can fulfill data requests without direct human intervention for each step.
The server publishes 8 tools. What each one is for:
LLM — Focused Output**: All descriptive tools return information in Markdown format, optimized for consumption by Large Language Models, making it easierDescription — Lists all available Unity Catalogs with their names, descriptions, and typesArgs — * catalog_name: The name of the Unity Catalog to describe (e.g., prod, dev, system)schema_name — The name of the schema to describeinclude_columns — If True, lists tables with their columns. Defaults to False for a briefer summaryinclude_lineage — Set to True to fetch comprehensive lineage (tables, notebooks, jobs). Defaults to False. May take longer to retrieve but provides rich context forNote — This is the same tool listed under "Core Capabilities" but is repeated here in the context of a typical agent workflow involving UC explorationInstallation — Create a .env file in the root directory of this project with your Databricks credentials:Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Configuration is passed through the environment: DATABRICKS_HOST, DATABRICKS_TOKEN, DATABRICKS_SQL_WAREHOUSE_ID. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. MCP Databricks Server's toolset — LLM, Description, Args and 5 more — is a fair guide to whether it matches your workflow. It is maintained by RafaelCartenet; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| LLM | Focused Output**: All descriptive tools return information in Markdown format, optimized for consumption by Large Language Models, making it easier for agents to parse and understand the context. |
| Description | Lists all available Unity Catalogs with their names, descriptions, and types. |
| Args | * catalog_name: The name of the Unity Catalog to describe (e.g., prod, dev, system). |
| schema_name | The name of the schema to describe. |
| include_columns | If True, lists tables with their columns. Defaults to False for a briefer summary. |
| include_lineage | Set to True to fetch comprehensive lineage (tables, notebooks, jobs). Defaults to False. May take longer to retrieve but provides rich context for understanding data dependencies and enabling code exploration. |
| Note | This is the same tool listed under "Core Capabilities" but is repeated here in the context of a typical agent workflow involving UC exploration followed by querying. |
| Installation | Create a .env file in the root directory of this project with your Databricks credentials: |
Example using `python`:
```json
{
"mcpServers": {
"databricks": {
"command": "python",
"args": [
"/path/to/your/mcp-databricks-server/main.py"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| DATABRICKS_HOST | Endpoint or connection string the server talks to. | Optional |
| DATABRICKS_TOKEN | Credential the server authenticates with. | Yes |
| DATABRICKS_SQL_WAREHOUSE_ID | Configuration value read at startup. | Optional |
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