# Databricks MCP Server

> Databricks MCP Server — niraj-hitpump-databricks-mcp-server. Use this tool when you need to interact with Databricks for data management and analytics tasks, such as running SQL queries, managing clusters, and triggering jobs. It solves problems related to big data processing, data exploration, and workflow automation by providing a unified interface for various Databricks operations. The tool accepts inputs like SQL queries, job triggers, and notebook commands, and outputs results, metadata, and job statuses, making it ideal for data engineers, analysts, and scientists working with Databricks.

Canonical page: https://skillsregistry.net/skills/niraj-hitpump-databricks-mcp-server  
JSON: https://api.skillsregistry.net/v1/skills/niraj-hitpump-databricks-mcp-server

## Description

Enables interaction with Databricks for running SQL queries, managing clusters, triggering jobs, running notebooks, exploring Unity Catalog metadata, managing secrets, and interacting with DBFS.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/vi15h68znq)
- **Repository:** <https://github.com/Niraj-Hitpump/Databricks-MCP-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": "niraj-hitpump-databricks-mcp-server"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/niraj-hitpump-databricks-mcp-server` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/niraj-hitpump-databricks-mcp-server/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