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Dataproc MCP Server

by warrenzhu25
GitHub
Python
MIT License
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Dataproc MCP Server

A Model Context Protocol (MCP) server that provides tools for managing Google Cloud Dataproc clusters and jobs. This server enables AI assistants to interact with Dataproc resources through a standardized interface.

Features

Cluster Management

  • List Clusters: View all clusters in a project and region

  • Create Cluster: Provision new Dataproc clusters with custom configurations

  • Delete Cluster: Remove existing clusters

  • Get Cluster: Retrieve detailed information about specific clusters

Job Management

  • Submit Jobs: Run Spark, PySpark, Spark SQL, Hive, Pig, and Hadoop jobs

  • List Jobs: View jobs across clusters with filtering options

  • Get Job: Retrieve detailed job information and status

  • Cancel Job: Stop running jobs

Batch Operations

  • Create Batch Jobs: Submit serverless Dataproc batch jobs

  • List Batch Jobs: View all batch jobs in a region

  • Get Batch Job: Retrieve detailed batch job information

  • Delete Batch Job: Remove batch jobs

Installation

Prerequisites

  • Python 3.11 or higher (Python 3.13+ recommended)

  • Google Cloud SDK configured with appropriate permissions

  • Dataproc API enabled in your Google Cloud project

Install from Source

# Clone the repository git clone https://github.com/warrenzhu25/dataproc-mcp.git cd dataproc-mcp # Create virtual environment (recommended for Homebrew Python) python3 -m venv .venv source .venv/bin/activate # Install project dependencies pip install -e . # Install development dependencies (optional) pip install -e ".[dev]"

Alternative Installation Methods

# With uv (if available) uv pip install --system -e . # With uv development dependencies uv pip install --system -e ".[dev]"

Troubleshooting Installation

If you encounter issues:

  1. Python version errors: Ensure you have Python 3.11+ installed

    python --version # Should be 3.11 or higher

  2. Externally managed environment errors: Use a virtual environment

    python3 -m venv .venv source .venv/bin/activate

  3. Missing module errors: Make sure dependencies are installed

    pip install -e .

Configuration

Authentication

The server supports multiple authentication methods:

  1. Service Account Key (Recommended for production):

    export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"

  2. Application Default Credentials:

    gcloud auth application-default login

  3. Compute Engine Service Account (when running on GCE)

Required Permissions

Ensure your service account or user has the following IAM roles:

  • roles/dataproc.editor - For cluster and job management

  • roles/storage.objectViewer - For accessing job files in Cloud Storage

  • roles/compute.networkUser - For VPC network access (if using custom networks)

Usage

Running the Server

First, activate your virtual environment (if using one):

source .venv/bin/activate

The server supports multiple transport protocols:

# STDIO (default) - for command-line tools and MCP clients python -m dataproc_mcp_server # HTTP - REST API over HTTP using streamable-http transport DATAPROC_MCP_TRANSPORT=http python -m dataproc_mcp_server # SSE - Server-Sent Events for real-time communication DATAPROC_MCP_TRANSPORT=sse python -m dataproc_mcp_server # Run with entry point script (STDIO only) dataproc-mcp-server

Transport Configuration

  • STDIO (default): Standard input/output communication for command-line tools and MCP clients

  • HTTP: REST API over HTTP using streamable-http transport

    • Server URL: http://localhost:8000/mcp

    • Accessible via web clients and HTTP-based MCP clients

  • SSE: Server-Sent Events for real-time bidirectional communication

    • Server URL: http://localhost:8000/sse

    • Supports streaming responses and live updates

Environment Variables

# Transport type (stdio, http, sse) export DATAPROC_MCP_TRANSPORT=http # Server host (for HTTP/SSE transports) export DATAPROC_MCP_HOST=0.0.0.0 # Enable debug logging (true, 1, yes to enable) export DATAPROC_MCP_DEBUG=true # Server port (for HTTP/SSE transports) export DATAPROC_MCP_PORT=8080 # Authentication export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"

MCP Client Configuration

Add to your MCP client configuration:

{ "mcpServers": { "dataproc": { "command": "python", "args": ["-m", "dataproc_mcp_server"], "env": { "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/service-account.json", "DATAPROC_MCP_DEBUG": "true" } } } }

Testing with MCP Inspector

You can test the server using the official MCP Inspector:

# Test STDIO transport npx @modelcontextprotocol/inspector python -m dataproc_mcp_server # Test HTTP transport with debug logging DATAPROC_MCP_TRANSPORT=http DATAPROC_MCP_DEBUG=true python -m dataproc_mcp_server & npx @modelcontextprotocol/inspector --transport http --server-url http://127.0.0.1:8000/mcp # Test SSE transport DATAPROC_MCP_TRANSPORT=sse python -m dataproc_mcp_server & npx @modelcontextprotocol/inspector --transport sse --server-url http://127.0.0.1:8000/sse

The MCP Inspector provides a web interface to:

  • Browse available tools and resources

  • Test tool calls with custom parameters

  • View real-time protocol messages

  • Debug server responses

Example Tool Usage

Create a Cluster

{ "name": "create_cluster", "arguments": { "project_id": "my-project", "region": "us-central1", "cluster_name": "my-cluster", "num_instances": 3, "machine_type": "n1-standard-4", "disk_size_gb": 100, "image_version": "2.1-debian11" } }

Submit a PySpark Job

{ "name": "submit_job", "arguments": { "project_id": "my-project", "region": "us-central1", "cluster_name": "my-cluster", "job_type": "pyspark", "main_file": "gs://my-bucket/my-script.py", "args": ["--input", "gs://my-bucket/input", "--output", "gs://my-bucket/output"], "properties": { "spark.executor.memory": "4g", "spark.executor.instances": "3" } } }

Create a Batch Job

{ "name": "create_batch_job", "arguments": { "project_id": "my-project", "region": "us-central1", "batch_id": "my-batch-job", "job_type": "pyspark", "main_file": "gs://my-bucket/batch-script.py", "service_account": "my-service-account@my-project.iam.gserviceaccount.com" } }

Development

Setup Development Environment

# Install development dependencies uv pip install --system -e ".[dev]" # Or with pip pip install -e ".[dev]"

Running Tests

# Run all tests pytest # Run with coverage python -m pytest --cov=src/dataproc_mcp_server tests/ # Run specific test file pytest tests/test_dataproc_client.py -v

Code Quality

# Format code ruff format src/ tests/ # Lint code ruff check src/ tests/ # Type checking (with VS Code + Pylance or mypy) mypy src/

Project Structure

dataproc-mcp/ ├── src/dataproc_mcp_server/ │ ├── __init__.py │ ├── __main__.py # Entry point │ ├── server.py # MCP server implementation │ ├── dataproc_client.py # Dataproc cluster/job operations │ └── batch_client.py # Dataproc batch operations ├── tests/ │ ├── __init__.py │ ├── test_server.py │ └── test_dataproc_client.py ├── examples/ │ ├── mcp_server_config.json │ └── example_usage.py ├── pyproject.toml ├── CLAUDE.md # Development guide └── README.md

Troubleshooting

Common Issues

  1. Authentication Errors:

    • Verify GOOGLE_APPLICATION_CREDENTIALS is set correctly

    • Ensure service account has required permissions

    • Check that Dataproc API is enabled

  2. Network Errors:

    • Verify VPC/subnet configurations for custom networks

    • Check firewall rules for cluster communication

    • Ensure clusters are in the correct region

  3. Job Submission Failures:

    • Verify file paths in Cloud Storage are accessible

    • Check cluster has sufficient resources

    • Validate job configuration parameters

Debug Mode

Enable debug logging:

export PYTHONPATH=/path/to/dataproc-mcp/src python -c " import logging logging.basicConfig(level=logging.DEBUG) from dataproc_mcp_server import __main__ import asyncio asyncio.run(__main__.main()) "

API Reference

Tools

Cluster Management

  • list_clusters(project_id, region) - List all clusters

  • create_cluster(project_id, region, cluster_name, ...) - Create cluster

  • delete_cluster(project_id, region, cluster_name) - Delete cluster

  • get_cluster(project_id, region, cluster_name) - Get cluster details

Job Management

  • submit_job(project_id, region, cluster_name, job_type, main_file, ...) - Submit job

  • list_jobs(project_id, region, cluster_name?, job_states?) - List jobs

  • get_job(project_id, region, job_id) - Get job details

  • cancel_job(project_id, region, job_id) - Cancel job

Batch Operations

  • create_batch_job(project_id, region, batch_id, job_type, main_file, ...) - Create batch job

  • list_batch_jobs(project_id, region, page_size?) - List batch jobs

  • get_batch_job(project_id, region, batch_id) - Get batch job details

  • delete_batch_job(project_id, region, batch_id) - Delete batch job

Resources

  • dataproc://clusters - Access cluster information

  • dataproc://jobs - Access job information

Contributing

  1. Fork the repository

  2. Create a feature branch

  3. Make your changes

  4. Add tests for new functionality

  5. Run the test suite and linting

  6. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

For issues and questions:

  1. Check the troubleshooting section

  2. Review Google Cloud Dataproc documentation

  3. Open an issue in the repository

Deploy Server
-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Tools

View all tools

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.

  1. Features
    1. Cluster Management
    2. Job Management
    3. Batch Operations
  2. Installation
    1. Prerequisites
    2. Install from Source
    3. Alternative Installation Methods
    4. Troubleshooting Installation
  3. Configuration
    1. Authentication
    2. Required Permissions
  4. Usage
    1. Running the Server
    2. MCP Client Configuration
    3. Testing with MCP Inspector
    4. Example Tool Usage
  5. Development
    1. Setup Development Environment
    2. Running Tests
    3. Code Quality
    4. Project Structure
  6. Troubleshooting
    1. Common Issues
    2. Debug Mode
  7. API Reference
    1. Tools
    2. Resources
  8. Contributing
    1. License
      1. Support

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