Dataproc MCP Server

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Spark and Hadoop workloads on Google Cloud Dataproc still mean juggling cluster state, job submission, and status checks. With Dataproc connected, Neotask can manage clusters and jobs directly from a request — launching workloads and checking status across both serverless and cluster-based setups.

• Covers both serverless Dataproc and traditional cluster-based jobs
• Launch Spark or Hadoop workloads and check status from the same conversation
• Handles both batch jobs and interactive sessions

What you can automate

Dataproc MCP Server

Manage Google Cloud Dataproc clusters and jobs, including launching Spark or Hadoop workloads and checking job and cluster status. Handle both serverless and cluster-based batch and session processing.

Real workflows

Check on a long-running Spark job

Ask "is my Spark job on the analytics cluster done yet?" and the agent checks job and cluster status through Dataproc, telling you whether it succeeded, failed, or is still running.

Frequently asked questions

What does the Dataproc connection actually do?

Manage Google Cloud Dataproc clusters and jobs, including launching Spark or Hadoop workloads and checking job and cluster status. Handle both serverless and cluster-based batch and session processing.

Does it work with serverless Dataproc or only managed clusters?

Both — serverless and cluster-based batch and session processing are handled the same way.