# Dataproc MCP Server

> Use this tool when you need to manage Google Cloud Dataproc clusters and jobs in a standardized way, enabling efficient batch operations and job submissions for various workloads like Spark, PySpark, and Hive. It solves problems related to cluster management, job execution, and batch processing, providing a streamlined interface for AI assistants. Ideal for use cases requiring automated cluster creation, job submission, and serverless operations in a cloud-based environment.

Canonical page: https://skillsregistry.net/skills/warrenzhu25-dataproc-mcp  
JSON: https://api.skillsregistry.net/v1/skills/warrenzhu25-dataproc-mcp

## Description

Enables AI assistants to manage Google Cloud Dataproc clusters and jobs through a standardized interface. Supports cluster creation/deletion, job submission (Spark, PySpark, Hive, etc.), and serverless batch operations.

## 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:** cloud-infra
- **Updated:** 2026-06-15

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/he3srcb1uj)
- **Repository:** <https://github.com/warrenzhu25/dataproc-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": "warrenzhu25-dataproc-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/warrenzhu25-dataproc-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/warrenzhu25-dataproc-mcp/pull`

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