# Databricks

> Use this tool when you need to execute SQL queries against large datasets, perform data exploration and analysis, or manage complex data tasks. It solves problems related to data querying, schema management, and result formatting, and accepts SQL queries as input, producing readable tables as output. Ideal for use cases involving data-intensive operations, it provides a robust interface for AI agents to interact with Databricks data.

Canonical page: https://skillsregistry.net/skills/rafaelcartenet-databricks  
JSON: https://api.skillsregistry.net/v1/skills/rafaelcartenet-databricks

## Description

This Databricks MCP server enables AI assistants to execute SQL queries against Databricks using the Statement Execution API. Built with Python using FastMCP, it provides tools for executing SQL queries, listing schemas and tables, and describing table schemas. The implementation handles authentication through environment variables, manages long-running queries with polling, and formats query results into readable tables. It's designed to work with Cursor and other MCP clients, making it ideal for data exploration, analysis, and complex tasks when coupled with Unity Catalog Metadata.

## Trust

- **Trust score (0–1):** 0.96
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/rafaelcartenet-databricks)
- **Repository:** <https://github.com/rafaelcartenet/mcp-databricks-server>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "rafaelcartenet-databricks"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/rafaelcartenet-databricks` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/rafaelcartenet-databricks/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt