CData JDBC Driver connects to Databricks by exposing them as relational SQL models.
Databricks By Cdata MCP server exists for a simple reason — assistants are far more useful when they can act on Databricks By Cdata directly instead of describing what you should do. CData JDBC Driver connects to Databricks by exposing them as relational SQL models.
Being a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.
Once Databricks By Cdata is connected, these are the calls the assistant has available:
databricks_get_tables — The databricks_get_tables tool exposed by this serverdatabricks_get_columns — The databricks_get_columns tool exposed by this serverdatabricks_run_query — The databricks_run_query tool exposed by this serverAmong the database access options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Databricks By Cdata's toolset — databricks_get_tables, databricks_get_columns, databricks_run_query — is a fair guide to whether it matches your workflow. It is maintained by CDataSoftware; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| databricks_get_tables | The databricks_get_tables tool exposed by this server. |
| databricks_get_columns | The databricks_get_columns tool exposed by this server. |
| databricks_run_query | The databricks_run_query tool exposed by this server. |
**Linux/Mac**
```json
{
"mcpServers": {
"{classname_dash}": {
"command": "/PATH/TO/java",
"args": [
"-jar",
"/PATH/TO/CDataMCP-jar-with-dependencies.jar",
"/PATH/TO/databricks.prp"
]
},
...
}
}Configuration as documented by the project. Restart the client after saving.
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