QA MCP Server for Domino Data Science Platform

Comprehensive UAT & Performance Testing via MCP Protocol

Transform your Domino platform validation with AI-powered testing. This MCP server exposes 32 specialized tools that enable LLMs to perform intelligent platform assessment, automated UAT workflows, and data-driven performance analysis.

🎯 What This Unlocks

Ask your AI assistant:

• "Is our Domino platform ready for production?"
• "Can the system handle 50 concurrent data science jobs?"
• "Why are users experiencing authentication issues?"
• "What's our baseline performance for ML model deployment?"

Get intelligent responses with:

• ✅ Automated test execution across all platform features
• 📊 Performance metrics and capacity analysis
• 🔍 Detailed diagnostics with actionable recommendations
• 🚀 One-command comprehensive UAT suites

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🚀 32 MCP Tools Available

🔧 Core Job Execution (4 tools)

Execute and monitor jobs with MLflow integration

run_domino_job | check_domino_job_run_status | check_domino_job_run_results | open_web_browser

🧪 UAT Testing Suite (12 tools)

Comprehensive platform feature validation

test_user_authentication | test_project_operations | test_job_execution
test_workspace_operations | test_environment_operations | test_dataset_operations  
test_file_management_operations | test_collaboration_features | test_model_operations
enhanced_test_dataset_operations | enhanced_test_model_operations | enhanced_test_advanced_job_operations

⚡ Performance Testing (5 tools)

Load, stress, and capacity testing

performance_test_workspaces | performance_test_jobs | stress_test_api
performance_test_concurrent_jobs | performance_test_data_upload_throughput

🎯 Comprehensive Suites (6 tools)

One-command complete assessments

run_master_comprehensive_uat_suite ← ULTIMATE SUITE
run_comprehensive_advanced_uat_suite | run_admin_uat_suite | run_user_uat_suite
run_comprehensive_split_uat_suite | cleanup_test_resources

🛠️ Platform Management (5 tools)

Project, dataset, and resource management

create_project_if_needed | test_dataset_creation_and_upload 
test_environment_and_hardware_operations | test_advanced_job_operations | enhanced_test_file_management

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📋 Setup

1. Install Dependencies

git clone <your-repo>
cd qa_mcp_server
uv pip install -e .

2. Configure Environment

Create .env file:

DOMINO_API_KEY='your_api_key_here'
DOMINO_HOST='https://your-domino-instance.com'

3. Configure MCP in Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "qa_mcp_server": {
      "command": "uv",
      "args": ["--directory", "/path/to/qa_mcp_server", "run", "domino_qa_mcp_server.py"]
    }
  }
}

4. Start Testing

Ask your AI: "Run a comprehensive UAT assessment of our Domino platform"

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💡 Smart Capabilities

🔄 Intelligent Resource Management

• Auto-generated unique names (timestamp + UUID)
• Automatic cleanup of test resources
• Graceful error handling and recovery

📊 Performance Insights

• Concurrent job capacity testing (20+ parallel jobs)
• Data upload throughput analysis
• API stress testing (100+ requests/sec)
• Resource utilization monitoring

🎯 Comprehensive Coverage

• Authentication workflows → Model deployment
• Infrastructure validation → User experience testing
• Admin operations → Data science workflows
• Performance baselines → Capacity planning

🤖 LLM-Optimized Responses

• Structured JSON with actionable insights
• Pass/fail scoring with improvement recommendations
• Detailed metrics for performance analysis
• Natural language summaries for non-technical stakeholders

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🚀 Example Workflows

Platform Readiness Assessment:

You: "Is our platform ready for 100 data scientists?"
AI: → Runs run_master_comprehensive_uat_suite()
Response: ✅ 85% overall readiness | ⚠️ Scale workspace resources | 📊 Baseline: 45 concurrent jobs

Performance Investigation:

You: "Why are model deployments slow?"
AI: → Runs enhanced_test_model_operations() + performance_test_concurrent_jobs()
Response: 🔍 Model registry bottleneck detected | ⏱️ Avg deployment: 3.2min | 💡 Recommend compute upgrade

Capacity Planning:

You: "What's our current performance baseline?"
AI: → Runs performance testing suite
Response: 📊 20 concurrent jobs max | 🚀 85MB/s upload speed | 💾 65% resource utilization | 📈 Growth capacity: 40%

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Ready to transform your Domino platform validation? Install the MCP server and let AI handle your UAT workflows!

Tech Stack: Python 3.11+ | FastMCP | python-domino v1.4.8 | Domino v6.1+