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warrenzhu25

Dataproc MCP Server

by warrenzhu25
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
DATAPROC_MCP_TRANSPORTNoCustom transport method for the MCP server
GOOGLE_APPLICATION_CREDENTIALSNoPath to service account JSON file for Google Cloud authentication

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
list_clustersB

List Dataproc clusters in a project and region.

Args:
    project_id: Google Cloud project ID (optional, uses gcloud config default)
    region: Dataproc region (optional, uses gcloud config default)
create_clusterB

Create a new Dataproc cluster.

Args:
    cluster_name: Name for the new cluster
    project_id: Google Cloud project ID (optional, uses gcloud config default)
    region: Dataproc region (optional, uses gcloud config default)
    num_instances: Number of worker instances
    machine_type: Machine type for cluster nodes
    disk_size_gb: Boot disk size in GB
    image_version: Dataproc image version
delete_clusterB

Delete a Dataproc cluster.

Args:
    cluster_name: Name of the cluster to delete
    project_id: Google Cloud project ID (optional, uses gcloud config default)
    region: Dataproc region (optional, uses gcloud config default)
get_clusterA

Get details of a specific Dataproc cluster.

Args:
    cluster_name: Name of the cluster
    project_id: Google Cloud project ID (optional, uses gcloud config default)
    region: Dataproc region (optional, uses gcloud config default)
submit_jobB

Submit a job to a Dataproc cluster.

Args:
    project_id: Google Cloud project ID
    region: Dataproc region
    cluster_name: Target cluster name
    job_type: Type of job (spark, pyspark, spark_sql, hive, pig, hadoop)
    main_file: Main file/class for the job
    args: Job arguments
    jar_files: JAR files to include
    properties: Job properties
list_jobsC

List jobs in a Dataproc cluster.

Args:
    project_id: Google Cloud project ID
    region: Dataproc region
    cluster_name: Cluster name (optional)
    job_states: Filter by job states
get_jobC

Get details of a specific job.

Args:
    project_id: Google Cloud project ID
    region: Dataproc region
    job_id: Job ID
cancel_jobA

Cancel a running job.

Args:
    project_id: Google Cloud project ID
    region: Dataproc region
    job_id: Job ID to cancel
create_batch_jobB

Create a Dataproc batch job.

Args:
    project_id: Google Cloud project ID
    region: Dataproc region
    batch_id: Unique identifier for the batch job
    job_type: Type of batch job (spark, pyspark, spark_sql)
    main_file: Main file/class for the job
    args: Job arguments
    jar_files: JAR files to include
    properties: Job properties
    service_account: Service account email
    network_uri: Network URI
    subnetwork_uri: Subnetwork URI
list_batch_jobsC

List Dataproc batch jobs.

Args:
    project_id: Google Cloud project ID
    region: Dataproc region
    page_size: Number of results per page
get_batch_jobB

Get details of a specific batch job.

Args:
    project_id: Google Cloud project ID
    region: Dataproc region
    batch_id: Batch job ID
delete_batch_jobC

Delete a batch job.

Args:
    project_id: Google Cloud project ID
    region: Dataproc region
    batch_id: Batch job ID to delete
compare_batch_jobsA

Compare two Dataproc batch jobs and return detailed differences.

Args:
    batch_id_1: First batch job ID to compare
    batch_id_2: Second batch job ID to compare
    project_id: Google Cloud project ID (optional, uses gcloud config default)
    region: Dataproc region (optional, uses gcloud config default)

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription
get_clusters_resourceGet list of all Dataproc clusters.
get_jobs_resourceGet list of all Dataproc jobs.

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