Dataproc MCP Server

A production-ready Model Context Protocol (MCP) server for Google Cloud Dataproc operations with intelligent parameter injection, enterprise-grade security, and comprehensive tooling. Designed for seamless integration with Roo (VS Code).

🚀 Quick Start

Recommended: Roo (VS Code) Integration

Add this to your Roo MCP settings:

{
  "mcpServers": {
    "dataproc": {
      "command": "npx",
      "args": ["@dipseth/dataproc-mcp-server@latest"],
      "env": {
        "LOG_LEVEL": "info"
      }
    }
  }
}

With Custom Config File

{
  "mcpServers": {
    "dataproc": {
      "command": "npx",
      "args": ["@dipseth/dataproc-mcp-server@latest"],
      "env": {
        "LOG_LEVEL": "info",
        "DATAPROC_CONFIG_PATH": "/path/to/your/config.json"
      }
    }
  }
}

Alternative: Global Installation

# Install globally
npm install -g @dipseth/dataproc-mcp-server

# Start the server
dataproc-mcp-server

# Or run directly
npx @dipseth/dataproc-mcp-server@latest

5-Minute Setup

1. Install the package:

   npm install -g @dipseth/dataproc-mcp-server@latest
   

2. Run the setup:

   dataproc-mcp --setup
   

3. Configure authentication:

   # Edit the generated config file
   nano config/server.json
   

4. Start the server:

   dataproc-mcp
   

✨ Features

🎯 Core Capabilities

• 16 Production-Ready MCP Tools - Complete Dataproc management suite
• 🧠 Knowledge Base Semantic Search - Natural language queries with optional Qdrant integration
• 🚀 Response Optimization - 60-96% token reduction with Qdrant storage
• 60-80% Parameter Reduction - Intelligent default injection
• Multi-Environment Support - Dev/staging/production configurations
• Service Account Impersonation - Enterprise authentication
• Real-time Job Monitoring - Comprehensive status tracking

🚀 Response Optimization

• 96.2% Token Reduction - list_clusters: 7,651 → 292 tokens
• Automatic Qdrant Storage - Full data preserved and searchable
• Resource URI Access - dataproc://responses/clusters/list/abc123
• Graceful Fallback - Works without Qdrant, falls back to full responses
• 9.95ms Processing - Lightning-fast optimization with <1MB memory usage

� Enterprise Security

• Input Validation - Zod schemas for all 16 tools
• Rate Limiting - Configurable abuse prevention
• Credential Management - Secure handling and rotation
• Audit Logging - Comprehensive security event tracking
• Threat Detection - Injection attack prevention

📊 Quality Assurance

• 90%+ Test Coverage - Comprehensive test suite
• Performance Monitoring - Configurable thresholds
• Multi-Environment Testing - Cross-platform validation
• Automated Quality Gates - CI/CD integration
• Security Scanning - Vulnerability management

🚀 Developer Experience

• 5-Minute Setup - Quick start guide
• Interactive Documentation - HTML docs with examples
• Comprehensive Examples - Multi-environment configs
• Troubleshooting Guides - Common issues and solutions
• IDE Integration - TypeScript support

🛠️ Complete MCP Tools Suite (21 Tools)

🚀 Cluster Management (8 Tools)

Tool	Description	Smart Defaults	Key Features
start_dataproc_cluster	Create and start new clusters	✅ 80% fewer params	Profile-based, auto-config
create_cluster_from_yaml	Create from YAML configuration	✅ Project/region injection	Template-driven setup
create_cluster_from_profile	Create using predefined profiles	✅ 85% fewer params	8 built-in profiles
list_clusters	List all clusters with filtering	✅ No params needed	Semantic queries, pagination
list_tracked_clusters	List MCP-created clusters	✅ Profile filtering	Creation tracking
get_cluster	Get detailed cluster information	✅ 75% fewer params	Semantic data extraction
delete_cluster	Delete existing clusters	✅ Project/region defaults	Safe deletion
get_zeppelin_url	Get Zeppelin notebook URL	✅ Auto-discovery	Web interface access

💼 Job Management (6 Tools)

Tool	Description	Smart Defaults	Key Features
submit_hive_query	Submit Hive queries to clusters	✅ 70% fewer params	Async support, timeouts
submit_dataproc_job	Submit Spark/PySpark/Presto jobs	✅ 75% fewer params	Multi-engine support
get_job_status	Get job execution status	✅ JobID only needed	Real-time monitoring
get_job_results	Get job outputs and results	✅ Auto-pagination	Result formatting
get_query_status	Get Hive query status	✅ Minimal params	Query tracking
get_query_results	Get Hive query results	✅ Smart pagination	Enhanced async support

📋 Configuration & Profiles (3 Tools)

Tool	Description	Smart Defaults	Key Features
list_profiles	List available cluster profiles	✅ Category filtering	8 production profiles
get_profile	Get detailed profile configuration	✅ Profile ID only	Template access
query_cluster_data	Query stored cluster data	✅ Natural language	Semantic search

📊 Analytics & Insights (4 Tools)

Tool	Description	Smart Defaults	Key Features
check_active_jobs	Quick status of all active jobs	✅ No params needed	Multi-project view
get_cluster_insights	Comprehensive cluster analytics	✅ Auto-discovery	Machine types, components
get_job_analytics	Job performance analytics	✅ Success rates	Error patterns, metrics
query_knowledge	Query comprehensive knowledge base	✅ Natural language	Clusters, jobs, errors

🎯 Key Capabilities

• 🧠 Semantic Search: Natural language queries with Qdrant integration
• ⚡ Smart Defaults: 60-80% parameter reduction through intelligent injection
• 📊 Response Optimization: 96% token reduction with full data preservation
• 🔄 Async Support: Non-blocking job submission and monitoring
• 🏷️ Profile System: 8 production-ready cluster templates
• 📈 Analytics: Comprehensive insights and performance tracking

📋 Configuration

Project-Based Configuration

The server supports a project-based configuration format:

# profiles/@analytics-workloads.yaml
my-company-analytics-prod-1234:
  region: us-central1
  tags:
    - DataProc
    - analytics
    - production
  labels:
    service: analytics-service
    owner: data-team
    environment: production
  cluster_config:
    # ... cluster configuration

Authentication Methods

1. Service Account Impersonation (Recommended)
2. Direct Service Account Key
3. Application Default Credentials
4. Hybrid Authentication with fallbacks

📚 Documentation

• Quick Start Guide - Get started in 5 minutes
• Knowledge Base Semantic Search - Natural language queries and setup
• API Reference - Complete tool documentation
• Configuration Examples - Real-world configurations
• Security Guide - Best practices and compliance
• Installation Guide - Detailed setup instructions

🔧 MCP Client Integration

Claude Desktop

{
  "mcpServers": {
    "dataproc": {
      "command": "npx",
      "args": ["@dataproc/mcp-server"],
      "env": {
        "LOG_LEVEL": "info"
      }
    }
  }
}

Roo (VS Code)

{
  "mcpServers": {
    "dataproc-server": {
      "command": "npx",
      "args": ["@dataproc/mcp-server"],
      "disabled": false,
      "alwaysAllow": [
        "list_clusters",
        "get_cluster",
        "list_profiles"
      ]
    }
  }
}

🏗️ Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   MCP Client    │────│  Dataproc MCP    │────│  Google Cloud   │
│  (Claude/Roo) │    │     Server       │    │    Dataproc     │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                              │
                       ┌──────┴──────┐
                       │   Features  │
                       ├─────────────┤
                       │ • Security  │
                       │ • Profiles  │
                       │ • Validation│
                       │ • Monitoring│
                       └─────────────┘

🚦 Performance

Response Time Achievements

• Schema Validation: ~2ms (target: <5ms) ✅
• Parameter Injection: ~1ms (target: <2ms) ✅
• Credential Validation: ~25ms (target: <50ms) ✅
• MCP Tool Call: ~50ms (target: <100ms) ✅

Throughput Achievements

• Schema Validation: ~2000 ops/sec ✅
• Parameter Injection: ~5000 ops/sec ✅
• Credential Validation: ~200 ops/sec ✅
• MCP Tool Call: ~100 ops/sec ✅

🧪 Testing

# Run all tests
npm test

# Run specific test suites
npm run test:unit
npm run test:integration
npm run test:performance

# Run with coverage
npm run test:coverage

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository
git clone https://github.com/dipseth/dataproc-mcp.git
cd dataproc-mcp

# Install dependencies
npm install

# Build the project
npm run build

# Run tests
npm test

# Start development server
npm run dev

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• GitHub Issues: Report bugs and request features
• Documentation: Complete documentation
• NPM Package: Package information

🏆 Acknowledgments

• Model Context Protocol - The protocol that makes this possible
• Google Cloud Dataproc - The service we're integrating with
• Qdrant - High-performance vector database powering our semantic search and knowledge indexing
• TypeScript - For type safety and developer experience

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Made with ❤️ for the MCP and Google Cloud communities