A production-ready Model Context Protocol (MCP) server for Google Cloud Dataproc operations. Provides intelligent defaults, comprehensive tooling
A production-ready Model Context Protocol (MCP) server for Google Cloud Dataproc operations. Provides intelligent defaults, comprehensive tooling, and seamless integration with Roo (VS Code) for big data workflows. That is what the dataproc mcp mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
@dipseth/dataproc-mcp-server on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
The server publishes 14 tools. What each one is for:
start_dataproc_cluster — Create and start new clusterscreate_cluster_from_yaml — Create from YAML configurationcreate_cluster_from_profile — Create using predefined profileslist_clusters — List all clusters with filteringlist_tracked_clusters — List MCP-created clustersget_cluster — Get detailed cluster informationdelete_cluster — Delete existing clustersget_zeppelin_url — Get Zeppelin notebook URLsubmit_hive_query — Submit Hive queries to clusterssubmit_dataproc_job — Submit Spark/PySpark/Presto jobscancel_dataproc_job — Cancel running or pending jobsget_job_status — Get job execution statusConfiguration is passed through the environment: LOG_LEVEL, DATAPROC_CONFIG_PATH. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
This sits in the cloud and infrastructure group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Dataproc MCP's toolset — start_dataproc_cluster, create_cluster_from_yaml, create_cluster_from_profile and 11 more — is a fair guide to whether it matches your workflow. It is maintained by dipseth; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| start_dataproc_cluster | Create and start new clusters |
| create_cluster_from_yaml | Create from YAML configuration |
| create_cluster_from_profile | Create using predefined profiles |
| list_clusters | List all clusters with filtering |
| list_tracked_clusters | List MCP-created clusters |
| get_cluster | Get detailed cluster information |
| delete_cluster | Delete existing clusters |
| get_zeppelin_url | Get Zeppelin notebook URL |
| submit_hive_query | Submit Hive queries to clusters |
| submit_dataproc_job | Submit Spark/PySpark/Presto jobs |
| cancel_dataproc_job | Cancel running or pending jobs |
| get_job_status | Get job execution status |
| get_job_results | Get job outputs and results |
| get_query_status | Get Hive query status |
{
"mcpServers": {
"dataproc": {
"command": "npx",
"args": ["@dipseth/dataproc-mcp-server@latest"],
"env": {
"LOG_LEVEL": "info"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| LOG_LEVEL | Configuration value read at startup. | Optional |
| DATAPROC_CONFIG_PATH | Filesystem location the server is allowed to use. | Optional |
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