databricks-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MAX_ROWS | No | Maximum number of rows any query may return. Default is 1000. | 1000 |
| DB_BACKEND | No | Backend to use: 'duckdb' or 'databricks'. Default is 'duckdb'. | duckdb |
| DATABRICKS_TOKEN | No | Databricks personal access token (required when DB_BACKEND=databricks). | |
| DATABRICKS_HTTP_PATH | No | Databricks HTTP path (required when DB_BACKEND=databricks). E.g. /sql/1.0/warehouses/abc123 | |
| DATABRICKS_SERVER_HOSTNAME | No | Databricks server hostname (required when DB_BACKEND=databricks). E.g. dbc-xxxxxxxx-xxxx.cloud.databricks.com |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_tablesA | List the tables available in the warehouse with their column counts. |
| describe_tableB | Return columns, types, and row count for a table. |
| sample_rowsB | Return up to |
| run_sqlA | Run a read-only SELECT query. DDL/DML/multi-statement queries are rejected; a row LIMIT is enforced automatically. |
| profile_tableA | Return per-column null fraction, distinct count, and min/max for a table. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool has a distinct purpose: schema description, table listing, profiling statistics, SQL querying, and row sampling. No overlaps.
All tool names follow a consistent verb_noun snake_case pattern (e.g., describe_table, list_tables), making them predictable.
With 5 tools, the set is well-scoped for table exploration and profiling without being too sparse or bloated.
The tool surface covers the core workflow of a Databricks warehouse explorer: list, describe, profile, query, and sample—no obvious gaps for read-only operations.