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MCP Databricks Server
RafaelCartenet/mcp-databricks-server
This MCP server provides a strong justification for that effort. It enables Large Language Models (LLMs) to directly access and utilize this Unity Catalog metadata. The more comprehensively your data is described in UC, the more effectively an LLM agent can understand your Databricks environment.
Install
Client configuration
{
"mcpServers": {
"databricks": {
"command": "uv",
"args": [
"--directory",
"/path/to/your/mcp-databricks-server",
"run",
"main.py"
]
}
}
}Environment variables
DATABRICKS_HOSTDATABRICKS_TOKENDATABRICKS_SQL_WAREHOUSE_ID- GitHub stars
- 6
- Author
- @RafaelCartenet
- Category
- Database, Developer Tools
- Updated
- Oct 6, 2026
About MCP Databricks Server
This deeper understanding is crucial for the agent to autonomously construct more intelligent and accurate SQL queries to fulfill data requests.
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 practical benefits of this detailed metadata entry?
Features
Execute SQL Queries: Run arbitrary SQL queries using the Databricks SDK via the execute_sql_query(sql: str) tool. This is ideal for targeted data retrieval or complex operations.
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.
list_uc_catalogs() -> str
describe_uc_catalog(catalog_name: str) -> str
describe_uc_schema(catalog_name: str, schema_name: str, include_columns: Optional[bool] = False) -> str
describe_uc_table(full_table_name: str, include_lineage: Optional[bool] = False) -> str
execute_sql_query(sql: str) -> str
Details on this page are taken from the project's README. Open README
Supported clients
Clients mentioned in this server's README:
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