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

🌐 Claude.ai Web App Compatibility

✅ PRODUCTION-READY: Full Claude.ai Integration with HTTPS Tunneling & OAuth

The Dataproc MCP Server now provides complete Claude.ai web app compatibility with a working solution that includes all 22 MCP tools!

🚀 Working Solution (Tested & Verified)

Terminal 1 - Start MCP Server:

DATAPROC_CONFIG_PATH=config/github-oauth-server.json npm start -- --http --oauth --port 8080

Terminal 2 - Start Cloudflare Tunnel:

cloudflared tunnel --url https://localhost:8443 --origin-server-name localhost --no-tls-verify

Result: Claude.ai can see and use all tools successfully! 🎉

Key Features:

• ✅ Complete Tool Access - All 22 MCP tools available in Claude.ai
• ✅ HTTPS Tunneling - Cloudflare tunnel for secure external access
• ✅ OAuth Authentication - GitHub OAuth for secure authentication
• ✅ Trusted Certificates - No browser warnings or connection issues
• ✅ WebSocket Support - Full WebSocket compatibility with Claude.ai
• ✅ Production Ready - Tested and verified working solution

Quick Setup:

1. Setup GitHub OAuth (5 minutes)

2. Generate SSL certificates: `npm run ssl:generate`

3. Start services (2 terminals as shown above)

4. Connect Claude.ai to your tunnel URL

> 📖 Complete Guide: See `docs/claude-ai-integration.md` for detailed setup instructions, troubleshooting, and advanced features.

> 📖 Certificate Setup: See `docs/trusted-certificates.md` for SSL certificate configuration.

✨ Features

🎯 Core Capabilities

• 22 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
• 🔄 Generic Type Conversion System - Automatic, type-safe data transformations
• 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 🔄 Enhanced with Generic Type Conversion: All tools now benefit from automatic, type-safe data transformations with intelligent compression and field mapping.

🚀 Cluster Management (8 Tools)

💼 Job Management (7 Tools)

📋 Configuration & Profiles (3 Tools)

📊 Analytics & Insights (4 Tools)

🎯 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
• **Generic Type Conversion System** - Architectural design and implementation
• **Generic Converter Migration Guide** - Migration from manual conversions
• **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│
                       │ • Generic    │
                       │   Converter  │
                       └─────────────┘

🔄 Generic Type Conversion System Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│  Source Types   │────│ Generic Converter │────│ Qdrant Payloads │
│ • ClusterData   │    │    System        │    │ • Compressed    │
│ • QueryResults  │    │                  │    │ • Type-Safe     │
│ • JobData       │    │ ┌──────────────┐ │    │ • Optimized     │
└─────────────────┘    │ │Field Analyzer│ │    └─────────────────┘
                       │ │Transformation│ │
                       │ │Engine        │ │
                       │ │Compression   │ │
                       │ │Service       │ │
                       │ └──────────────┘ │
                       └──────────────────┘

🚦 Performance

Response Time Achievements

• Schema Validation: ~2ms (target: <5ms) ✅
• Parameter Injection: ~1ms (target: <2ms) ✅
• Generic Type Conversion: ~0.50ms (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 ✅
• Generic Type Conversion: ~2000 ops/sec ✅
• Credential Validation: ~200 ops/sec ✅
• MCP Tool Call: ~100 ops/sec ✅

Compression Achievements

• Field-Level Compression: Up to 100% compression ratios ✅
• Memory Optimization: 30-60% reduction in memory usage ✅
• Type Safety: Zero runtime type errors with automatic validation ✅

🧪 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

Made with ❤️ for the MCP and Google Cloud communities