# Project Summary - GCP Sales Analytics POC
## Overview
This is a complete proof-of-concept implementation demonstrating an AI-powered sales analytics system that intelligently queries multiple data sources using the Anthropic Claude API and Model Context Protocol (MCP).
## What Was Built
### 1. Infrastructure (Terraform)
- **Cloud SQL PostgreSQL instance** with public IP access
- Automated deployment with configurable regions
- Database creation and user management
- Cost-optimized settings for POC (db-f1-micro)
### 2. Database Schema & Data
- **Three tables**: customers, orders, vendors
- **Synthetic data generation**: 50 records each table
- Realistic transactional data using Faker library
- Includes relationships and indexes for performance
### 3. MCP Server
- Standards-compliant Model Context Protocol server
- **Six tools** for data access:
- `query_cloudsql` - Execute SQL against Cloud SQL
- `query_bigquery` - Execute SQL against BigQuery
- `list_cloudsql_tables` - List Cloud SQL tables
- `list_bigquery_tables` - List BigQuery tables
- `get_cloudsql_schema` - Get Cloud SQL table schema
- `get_bigquery_schema` - Get BigQuery table schema
- Security: SELECT-only queries, connection pooling
- Error handling and logging
### 4. ADK Agent (Two Versions)
**Version 1: agent.py** (Conceptual)
- Demonstrates agentic architecture
- Tool calling framework
- Placeholder tool execution
**Version 2: integrated_agent.py** (Functional)
- Fully integrated with Cloud SQL and BigQuery
- Direct tool execution
- Interactive CLI interface
- Intelligent data source selection
- Context-aware query routing
### 5. Automation Scripts
- **deploy.sh**: One-command deployment
- **cleanup.sh**: Safe resource teardown
- **generate_seed_data.py**: Synthetic data generation
- **test_agent.py**: Automated testing
### 6. Documentation
- **README.md**: Comprehensive documentation
- **QUICKSTART.md**: 15-minute setup guide
- **PROJECT_SUMMARY.md**: This file
- Inline code comments throughout
## Key Features
### Intelligent Query Routing
The agent automatically determines which data source to use:
```
Customer question → Analyze keywords → Choose data source → Execute query
```
**Cloud SQL**: customers, vendors, recent orders
**BigQuery**: products, inventory, analytics, historical data
### Architecture Highlights
1. **Agentic Loop**: Multi-turn conversations with tool calling
2. **MCP Compliance**: Standards-based tool interface
3. **Error Handling**: Graceful degradation and helpful errors
4. **Security**: SQL injection prevention, credential management
5. **Observability**: Logging and query visibility
## File Structure
```
gcp_mcp/
├── README.md # Main documentation
├── QUICKSTART.md # Quick start guide
├── PROJECT_SUMMARY.md # This file
├── requirements.txt # Python dependencies
├── .env.example # Environment template
├── .gitignore # Git ignore rules
│
├── terraform/ # Infrastructure as Code
│ ├── main.tf # Cloud SQL resources
│ ├── variables.tf # Input variables
│ ├── outputs.tf # Output values
│ └── terraform.tfvars.example # Variable values template
│
├── data/
│ └── schema.sql # Database schema (3 tables)
│
├── scripts/
│ ├── deploy.sh # Automated deployment (Bash)
│ ├── cleanup.sh # Resource cleanup (Bash)
│ ├── generate_seed_data.py # Synthetic data generator
│ └── test_agent.py # Automated tests
│
├── mcp_server/
│ └── server.py # MCP server implementation
│
└── adk_agent/
├── agent.py # Conceptual agent (framework)
└── integrated_agent.py # Functional agent (complete)
```
## Technologies Used
- **Cloud Platform**: Google Cloud Platform (GCP)
- Cloud SQL (PostgreSQL 15)
- BigQuery (thelook_ecommerce public dataset)
- **Infrastructure**: Terraform 1.0+
- **Language**: Python 3.9+
- **Key Libraries**:
- `anthropic` - Claude API client
- `google-cloud-bigquery` - BigQuery client
- `psycopg2` - PostgreSQL client
- `mcp` - Model Context Protocol
- `faker` - Synthetic data generation
- **AI Model**: Claude 3.5 Sonnet
## Data Sources
### Cloud SQL Database
```sql
-- customers table (50 records)
- customer_id, first_name, last_name, email
- phone, address, city, state, zip_code, country
- created_at, updated_at
-- vendors table (50 records)
- vendor_id, vendor_name, contact_name, email
- phone, address, city, state, zip_code, country
- created_at, updated_at
-- orders table (50 records)
- order_id, customer_id, vendor_id
- order_date, ship_date, total_amount, status
- product_name, quantity, unit_price
- created_at, updated_at
```
### BigQuery Dataset
```
bigquery-public-data.thelook_ecommerce
- products, users, events
- inventory_items, order_items, orders
- distribution_centers
```
## How to Use
### Quick Start
```bash
# 1. Setup
cp .env.example .env
# Edit .env with your ANTHROPIC_API_KEY
# 2. Deploy
./scripts/deploy.sh
# 3. Run
python3 adk_agent/integrated_agent.py
```
### Example Queries
```
📊 You: What are the total sales from Cloud SQL?
📊 You: How many customers do we have?
📊 You: Show me orders with status 'delivered'
📊 You: What's the average order value?
📊 You: List the top 5 vendors by order volume
```
## Agent Decision Making
The agent uses a sophisticated decision-making process:
1. **Question Analysis**: Parse user intent and keywords
2. **Schema Discovery**: Optionally list tables to understand data
3. **Source Selection**: Choose Cloud SQL or BigQuery based on:
- Data type (transactional vs analytical)
- Keywords (customers/vendors → Cloud SQL)
- Query complexity (aggregations, joins)
- Data recency requirements
4. **Query Formulation**: Generate appropriate SQL
5. **Execution**: Run query against selected source
6. **Presentation**: Format results with context and insights
## Security Considerations
### Current (POC)
- Public IP with authorized networks
- Environment variable credentials
- SELECT-only queries
- Basic SQL injection prevention
### Production Recommendations
- Cloud SQL Proxy or private IP
- Secret Manager for credentials
- VPC networking
- IAM authentication
- Query validation and sanitization
- Rate limiting
- Audit logging
- Cloud SQL backups
## Cost Optimization
- db-f1-micro instance (~$7-10/month)
- Backups disabled for POC
- Public dataset (BigQuery) - minimal cost
- Manual cleanup script provided
**Remember to run `./scripts/cleanup.sh` when done!**
## Extensibility
This POC can be extended with:
1. **Additional Data Sources**
- Cloud Spanner
- Firestore
- External APIs
- CSV/JSON files
2. **Advanced Features**
- Data visualization
- Report generation
- Scheduled queries
- Email notifications
- Slack integration
3. **Enhanced Agent Capabilities**
- Multi-step reasoning
- Query optimization suggestions
- Data quality checks
- Anomaly detection
- Predictive analytics
4. **Production Features**
- Caching layer (Redis)
- Query result pagination
- Streaming responses
- Monitoring (Cloud Monitoring)
- Alerting (Cloud Alerting)
- CI/CD pipeline
## Testing
```bash
# Run automated tests
python3 scripts/test_agent.py
# Test individual components
python3 -c "from adk_agent.integrated_agent import IntegratedSalesAgent; print('OK')"
# Test Cloud SQL connection
psql -h $DB_HOST -U $CLOUDSQL_USER -d $CLOUDSQL_DATABASE -c "SELECT COUNT(*) FROM orders"
# Test BigQuery access
bq query --use_legacy_sql=false "SELECT COUNT(*) FROM \`bigquery-public-data.thelook_ecommerce.products\` LIMIT 10"
```
## Troubleshooting
Common issues and solutions are documented in:
- [README.md](README.md#troubleshooting)
- [QUICKSTART.md](QUICKSTART.md#troubleshooting)
## Success Criteria
✅ Cloud SQL deployed and accessible
✅ Database schema created with 3 tables
✅ 150 synthetic records generated (50 each table)
✅ BigQuery public dataset accessible
✅ MCP server implements 6 tools
✅ ADK agent intelligently routes queries
✅ Automated deployment script
✅ Comprehensive documentation
✅ Working example queries
## Lessons Learned
1. **MCP Standard**: Provides clean abstraction for tool interfaces
2. **Agentic Patterns**: Multi-turn conversations enable complex tasks
3. **Data Source Selection**: Clear tool descriptions guide agent decisions
4. **Error Handling**: Critical for production reliability
5. **Documentation**: Essential for POC handoff and maintenance
## Next Steps
1. **Immediate**:
- Test with real data
- Add more example queries
- Performance benchmarking
2. **Short Term**:
- Implement caching
- Add data visualization
- Create web interface
3. **Long Term**:
- Production hardening
- Multi-tenant support
- Advanced analytics
- MLOps integration
## Resources
- [Anthropic Claude API](https://docs.anthropic.com/)
- [Model Context Protocol](https://modelcontextprotocol.io/)
- [Google Cloud SQL](https://cloud.google.com/sql/docs)
- [BigQuery Public Datasets](https://cloud.google.com/bigquery/public-data)
- [Terraform GCP Provider](https://registry.terraform.io/providers/hashicorp/google/latest/docs)
## Credits
Built as a proof of concept demonstrating:
- Multi-source data querying
- Intelligent agent decision making
- MCP server implementation
- Infrastructure as Code
- Production-ready patterns
## License
Proof of concept for demonstration purposes.
---
**Questions?** Check the [README.md](README.md) or [QUICKSTART.md](QUICKSTART.md)
