AI-powered assistant that connects to Metabase and PostgreSQL databases directly via Model Context Protocol (MCP) for Claude Desktop and Claude Code. Creates models, SQL queries, metrics, and dashboards using both Metabase API and direct database connections.
🚀 MCP Server for Claude Desktop & Claude Code - Metabase + Direct DB Access
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🚀 Features
🔌 MCP Integration (Claude Desktop & Claude Code)
- Model Context Protocol: Native integration with Claude Desktop and Claude Code
- Direct Database Access: Direct PostgreSQL database connections
- Metabase API Integration: Full integration with Metabase instances
- Schema Discovery: Automatic database schema discovery and analysis
- Relationship Detection: Table relationship detection and suggestions
🤖 AI-Powered Features
- Natural Language SQL: Generate SQL queries from natural language descriptions
- Smart Model Building: AI-assisted Metabase model creation
- Intelligent Dashboards: Automatic dashboard layout and widget suggestions
- Query Optimization: SQL query performance optimization
- Data Insights: Data analysis and pattern detection
- DDL Operations: Safe table/view/index creation (prefix-protected)
- Batch Operations: Bulk data processing operations
- Connection Management: Hybrid connection management (API + Direct)
- Security Controls: AI object prefix control and approval workflows
- Performance Monitoring: Operation timing and timeout controls
📋 Requirements
🖥️ System
- Node.js 18+
- Claude Desktop (for MCP support) OR Claude Code
- PostgreSQL Database (for direct connections)
🔗 Services
- Metabase instance (v0.48+)
- Anthropic API (included in Claude Desktop/Code)
🔧 Installation
# Clone the repository
git clone https://github.com/onmartech/metabase-ai-assistant.git
cd metabase-ai-assistant
# Install dependencies
npm install
# Create environment file
cp .env.example .env
⚙️ Configuration
Edit the .env file:
# Metabase Configuration
METABASE_URL=http://your-metabase-instance.com
METABASE_USERNAME=your_username
METABASE_PASSWORD=your_password
METABASE_API_KEY=your_metabase_api_key
# AI Provider (at least one required)
ANTHROPIC_API_KEY=your_anthropic_key
# or
OPENAI_API_KEY=your_openai_key
# Application Settings
LOG_LEVEL=info
⚠️ Security Warning: Never commit the .env file to version control. This file is already included in .gitignore.
🔌 Claude Desktop & Claude Code Integration (MCP)
This project integrates with Claude Desktop and Claude Code via Model Context Protocol (MCP):
For Claude Desktop:
- Edit Claude Desktop Config:
~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"metabase-ai-assistant": {
"command": "node",
"args": ["/path/to/your/metabase-ai-assistant/src/mcp/server.js"],
"env": {
"METABASE_URL": "http://your-metabase-instance.com",
"METABASE_USERNAME": "your_username",
"METABASE_PASSWORD": "your_password",
"ANTHROPIC_API_KEY": "your_anthropic_key"
}
}
}
}
- Restart Claude Desktop and MCP tools will be available.
For Claude Code:
Claude Code can use this MCP server directly via global installation:
Step 1: Global Installation
# Install the MCP server globally
npm link
# Verify installation
which metabase-ai-mcp
npm list -g | grep metabase-ai-assistant
Step 2: Environment Setup
Ensure your .env file is properly configured with your Metabase credentials:
METABASE_URL=http://your-metabase-instance.com
METABASE_USERNAME=your_username
METABASE_PASSWORD=your_password
METABASE_API_KEY=your_api_key
ANTHROPIC_API_KEY=your_anthropic_key
Step 3: Test MCP Server
# Test the MCP server directly
node src/mcp/server.js
# Test with environment variables
export METABASE_URL="http://your-instance.com"
export METABASE_USERNAME="your_username"
export METABASE_PASSWORD="your_password"
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | node src/mcp/server.js
Step 4: Verify Integration
In Claude Code, ask: "What MCP tools do you have available?"
You should see 27 Metabase AI Assistant tools available:
📊 Database Tools:
db_list - List all Metabase databasesdb_schemas - Get schema information db_tables - List tables with detailssql_execute - Run SQL queries
🎯 Metabase Tools:
mb_question_create - Create questions/chartsmb_dashboard_create - Create dashboardsmb_dashboard_template_executive - Auto-generate executive dashboardsmb_question_create_parametric - Create parametric questions
🔍 AI-Powered Tools:
ai_sql_generate - Generate SQL from natural languageai_sql_optimize - Optimize SQL performanceai_sql_explain - Explain SQL queries
📚 Documentation Tools:
web_explore_metabase_docs - Crawl Metabase documentationweb_search_metabase_docs - Search documentation
The server provides comprehensive Metabase and PostgreSQL integration with 27 tools for:
- Database schema exploration and analysis
- Natural language SQL query generation and optimization
- Executive dashboard templates and parametric questions
- Direct DDL operations with security controls
- Metabase documentation crawling and search
- Table relationship detection and mapping
🎯 Usage
Interactive CLI
Programmatic Usage
import { MetabaseClient } from './src/metabase/client.js';
import { MetabaseAIAssistant } from './src/ai/assistant.js';
// Client oluştur
const client = new MetabaseClient({
url: 'http://your-metabase.com',
username: 'user',
password: 'pass'
});
// AI Assistant başlat
const assistant = new MetabaseAIAssistant({
metabaseClient: client,
aiProvider: 'anthropic',
anthropicApiKey: 'your-key'
});
// Model oluştur
const model = await assistant.createModel(
'Müşteri segmentasyon modeli',
databaseId
);
// SQL sorgusu üret
const sql = await assistant.generateSQL(
'Son 30 günün satış toplamı',
schema
);
📚 Örnek Senaryolar
1. E-Ticaret Dashboard'u
// Satış modeli oluştur
await assistant.createModel(
'Günlük satış özeti - ürün, kategori, tutar',
databaseId
);
// Metrikler tanımla
await assistant.createMetric(
'Ortalama sepet değeri',
tableId
);
// Dashboard oluştur
await assistant.createDashboard(
'E-Ticaret Yönetici Paneli',
questions
);
2. Müşteri Analizi
// Müşteri segmentasyon sorgusu
const sql = await assistant.generateSQL(
'RFM analizi ile müşteri segmentleri',
schema
);
// Churn prediction modeli
await assistant.createModel(
'Müşteri kayıp tahmin modeli',
databaseId
);
3. Finansal Raporlama
// Gelir-gider analizi
await assistant.createQuestion(
'Aylık kar-zarar tablosu',
databaseId
);
// Bütçe karşılaştırma dashboard'u
await assistant.createDashboard(
'Bütçe vs Gerçekleşen',
budgetQuestions
);
🛠️ CLI Komutları
Interaktif CLI'da kullanılabilir komutlar:
- 📊 Create Model: AI ile model oluştur
- ❓ Create Question: SQL sorgusu oluştur
- 📈 Create Metric: Metrik tanımla
- 📋 Create Dashboard: Dashboard hazırla
- 🔍 Explore Schema: Veritabanı şemasını incele
- 🚀 Execute SQL: SQL sorgusu çalıştır
- 🔧 Optimize Query: Sorgu optimize et
- 💡 AI Query Builder: Doğal dilde sorgu oluştur
📂 Proje Yapısı
metabase-ai-assistant/
├── src/
│ ├── mcp/
│ │ └── server.js # MCP Server (Claude Desktop entegrasyonu)
│ ├── metabase/
│ │ └── client.js # Metabase API client
│ ├── database/
│ │ ├── direct-client.js # Direct PostgreSQL client
│ │ └── connection-manager.js # Hybrid connection manager
│ ├── ai/
│ │ └── assistant.js # AI helper functions
│ ├── cli/
│ │ └── interactive.js # Interactive CLI (standalone)
│ ├── utils/
│ │ └── logger.js # Logging utilities
│ └── index.js # Main entry point (CLI mode)
├── tests/ # Test files
├── .env.example # Environment template
├── package.json
└── README.md
🔍 API Referansı
// Veritabanları
getDatabases()
getDatabase(id)
getDatabaseSchemas(databaseId)
getDatabaseTables(databaseId)
// Modeller
getModels()
createModel(modelData)
// Sorgular
getQuestions(collectionId)
createQuestion(questionData)
executeNativeQuery(databaseId, sql)
// Metrikler
getMetrics()
createMetric(metricData)
// Dashboard'lar
getDashboards()
createDashboard(dashboardData)
addCardToDashboard(dashboardId, cardId, options)
// AI İşlemleri
analyzeRequest(userRequest)
generateSQL(description, schema)
suggestVisualization(data, questionType)
optimizeQuery(sql)
explainQuery(sql)
// Oluşturma İşlemleri
createModel(description, databaseId)
createQuestion(description, databaseId, collectionId)
createMetric(description, tableId)
createDashboard(description, questions)
🧪 Test
# Tüm testleri çalıştır
npm test
# Bağlantı testi
npm run test:connection
# Coverage raporu
npm run test:coverage
🔒 Security
Data Security
- Environment Variables: All sensitive data (API keys, passwords) stored in
.env file - Git Ignore:
.env file excluded from version control - SQL Injection Protection: Parameterized queries and input validation
- Rate Limiting: API request rate limiting applied
- Audit Logging: All database operations logged for security monitoring
- No Hardcoded Credentials: Security-first approach prevents credential exposure
Database Security
- AI Object Prefix: All AI-created objects marked with
claude_ai_ prefix for safety - Schema Isolation: Operations limited to specified schemas only
- Read-Only Mode: Default read-only permissions with explicit approval for modifications
- DDL Approval System: Database changes require explicit confirmation
- Prefix Validation: Only AI-prefixed objects can be modified or deleted
MCP Security
- Secure Transport: MCP communication over secure channels
- Environment Isolation: Credentials passed via environment variables
- Tool Validation: All tool inputs validated before execution
- Error Handling: Sensitive information filtered from error messages
Production Deployment
- Use environment-specific configuration files
- Prefer SSL/TLS connections for all database communications
- Grant minimum required permissions to database users
- Protect API endpoints with authentication and authorization
- Regularly rotate API keys and database passwords
- Monitor and log all tool usage for security auditing
🐛 Troubleshooting
Connection Errors
- Verify Metabase URL is accessible
- Ensure API key and credentials are valid
- Check network connectivity and firewall settings
- Confirm environment variables are properly set
MCP Integration Issues
- Ensure
npm link was run successfully - Verify MCP server binary is in PATH:
which metabase-ai-mcp - Check environment variables are exported:
echo $METABASE_URL - Test MCP server directly:
node src/mcp/server.js - Restart Claude Code after global installation
Query Errors
- Validate SQL syntax and formatting
- Verify table and column names exist
- Check database permissions and schema access
- Ensure proper schema selection for operations
Security Warnings
- Never commit
.env files to version control - Avoid hardcoding credentials in source code
- Use prefix validation for AI-created objects
- Monitor database operations for security compliance
🚀 Production Deployment
Option 1: PM2 Process Manager (Recommended)
# Install PM2 globally
npm install -g pm2
# Start MCP server with PM2
npm run pm2:start
# Monitor and manage
npm run pm2:logs
npm run pm2:restart
npm run pm2:stop
# Auto-restart on system reboot
pm2 startup
pm2 save
Option 2: Docker Container
# Build and run with Docker Compose
npm run docker:run
# Monitor logs
npm run docker:logs
# Stop containers
npm run docker:stop
Option 3: Cloud Deployment
- Railway: One-click deploy with
railway.json - Heroku: Deploy with Heroku CLI (see
deploy/heroku-deploy.md) - DigitalOcean: App Platform with Docker
- AWS: ECS Fargate or EC2 with systemd service
Option 4: Systemd Service (Linux)
# Copy service file
sudo cp metabase-ai-mcp.service /etc/systemd/system/
# Enable and start service
sudo systemctl enable metabase-ai-mcp
sudo systemctl start metabase-ai-mcp
# Monitor service
sudo systemctl status metabase-ai-mcp
sudo journalctl -u metabase-ai-mcp -f
Production Scripts
npm run mcp:prod # Production mode
npm run test:connection # Health check
npm run lint # Code quality check
📈 Roadmap
🤝 Katkıda Bulunma
Bu projeyi beğendiyseniz ve geliştirmesine katkıda bulunmak istiyorsanız:
⭐ Projeyi Destekleyin
- GitHub'da Star Verin: Projeyi faydalı bulduysanız ⭐ star verin
- Follow Edin: Güncellemelerden haberdar olmak için @onmartech hesabını takip edin
- Share Edin: Sosyal medyada paylaşın ve arkadaşlarınıza önerin
🔧 Geliştirmeye Katılın
- Fork yapın
- Feature branch oluşturun (
git checkout -b feature/yeni-ozellik) - Değişikliklerinizi commit yapın (
git commit -m 'feat: Yeni özellik eklendi') - Push yapın (
git push origin feature/yeni-ozellik) - Pull Request açın
💡 Katkı Fikirleri
- Yeni AI modeli entegrasyonları
- Dashboard template'leri
- Metabase connector'ları
- Dokümantasyon iyileştirmeleri
- Bug fixes ve performans optimizasyonları
📋 Katkı Kuralları
- Kod değişikliklerinde test yazın
- Commit mesajlarında Conventional Commits kullanın
- ESLint ve Prettier ayarlarına uyun
- Değişikliklerinizi dokümante edin
📄 Lisans
MIT License - Detaylar için LICENSE dosyasına bakın.
Copyright (c) 2024 ONMARTECH LLC
👥 Destek ve İletişim
🐛 Bug Reports & Feature Requests
- GitHub Issues: Issues sayfası
- Bug Template: Issue açarken template'leri kullanın
- Feature Requests: Hangi özelliği istediğinizi detaylandırın
💬 Topluluk
- GitHub Discussions: Soru-cevap ve fikirler için
- Documentation: Wiki sayfalarına katkı yapın
- Examples: Örnek kullanım case'leri paylaşın
🚀 Ticari Destek
ONMARTECH LLC tarafından profesyonel destek ve customization hizmetleri mevcuttur.
🏆 Katkıda Bulunanlar
Bu projeyi mümkün kılan herkese teşekkürler:
- ONMARTECH LLC - Proje geliştirme ve bakım
- Metabase Team - Harika platform
- Open Source Community - Sürekli ilham ve geri bildirim
🌟 Hall of Fame
Önemli katkılarda bulunan geliştiriciler burada listelenecektir.
Bu projeyi faydalı bulduysanız ⭐ star vermeyi ve 🔄 share etmeyi unutmayın!