# Databricks

> Use this tool when you need to manage Databricks resources, automate workspace management, or execute data operations. It solves problems such as manual cluster management, job execution, and data file handling by providing a standardized interface for interacting with Databricks APIs. With inputs including Databricks tokens and SQL queries, and outputs featuring well-formatted responses, it is ideal for data scientists and engineers seeking to streamline their workflow through AI assistant integration.

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

## Description

Databricks MCP Server provides tools for interacting with Databricks APIs through a standardized protocol, enabling AI assistants to manage clusters, jobs, notebooks, DBFS files, and execute SQL queries. Built with Python using the FastMCP framework, it authenticates with Databricks tokens and exposes functionality through well-documented tools that handle proper error reporting and response formatting. The server can be run via command line or integrated into applications, making it particularly valuable for data scientists and engineers who need to automate Databricks workspace management, monitor resources, or execute data operations without leaving their AI assistant interface.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-28

## Source

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

## 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": "markov-kernel-databricks"
    }
  }
}
```

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