# Lakeflow MCP Server

> Use this tool when you need to manage and orchestrate complex data experiments on Databricks clusters. It solves problems related to automating job runs, monitoring execution, and streamlining data workflows by providing a structured interface for building and uploading Python wheels and triggering job runs with specific arguments. The Lakeflow MCP Server takes in job configurations and arguments as inputs and outputs execution status and results, making it ideal for use cases involving data-intensive AI workloads.

Canonical page: https://skillsregistry.net/skills/arahimi-hims-lakeflow-mcp  
JSON: https://api.skillsregistry.net/v1/skills/arahimi-hims-lakeflow-mcp

## Description

Enables AI agents to manage Databricks Lakeflow jobs by building and uploading Python wheels and triggering runs with specific arguments. It provides a structured way to orchestrate complex data experiments and monitor execution directly on Databricks clusters.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-04-22

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/d51f2j02ly)
- **Repository:** <https://github.com/arahimi-hims/lakeflow-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": "arahimi-hims-lakeflow-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/arahimi-hims-lakeflow-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/arahimi-hims-lakeflow-mcp/pull`

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