gloveboxes/Ignite-Zava-MCP-Server-and-PostgreSQL-Sample

Sales Analysis MCP Server

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A Model Context Protocol (MCP) server that provides comprehensive customer sales database access for Zava Retail DIY Business. This server enables AI assistants to query and analyze retail sales data through a secure, schema-aware interface.

Prerequisites

1. Docker Desktop installed
2. Git installed
3. Azure CLI: Install and authenticate with Azure CLI
4. Access to OpenAI text-embedding-3-small model and optionally gpt-4o-mini model.

Getting Started

Open a terminal window and running the following commands:

1. Authenticate with Azure CLI

   az login
   

2. Clone the repository

   git clone https://github.com/gloveboxes/Zava-MCP-Server-and-PostgreSQL-Sample
   

3. Navigate to the project directory

   cd Zava-MCP-Server-and-PostgreSQL-Sample
   

Deploy Azure Resources

Run the following scripts to automate the deployment of Azure resources needed for the MCP server.

The deployment scripts will automatically deploy:

• Azure AI Foundry with both text-embedding-3-small and gpt-4o-mini models
• Azure Database for PostgreSQL (Flexible Server) with pgvector extension
• Application Insights for monitoring
• Service principal with appropriate permissions

The deployment will automatically initialize the PostgreSQL database with the Zava retail sample data.

Choose the script for your platform:

Windows (PowerShell)

# Run from the project root directory
cd infra && ./deploy.ps1

macOS/Linux (Bash)

# Run from the project root directory
cd infra && ./deploy.sh

Running the MCP Server

After deploying Azure resources using the deployment scripts above, the MCP server will automatically connect to your Azure PostgreSQL database using the configuration in .env.

To run the MCP servers:

# Start the MCP servers (they will connect to Azure PostgreSQL)
# Option 1: Using VS Code tasks
# Use Ctrl+Shift+P -> "Tasks: Run Task" -> "Start Supplier and Finance MCP Servers"

# Option 2: Using terminal
python -m app.mcp.supplier_server &
python -m app.mcp.finance_server &

The deployment script automatically creates a .env file with the correct Azure connection details.

Manual Database Initialization

If the automatic database initialization fails during deployment, you can initialize it manually:

cd infra
./init-azure-db.sh

This script will:

• Test the connection to Azure PostgreSQL
• Enable required extensions (pgvector, plpgsql)
• Create the store_manager user
• Restore the Zava retail database from the backup file
• Set up proper permissions

Cleanup Azure Resources

When you're done with the workshop, you can remove all Azure resources:

cd infra
./cleanup.sh

This will remove the resource group and all associated resources, including the PostgreSQL database, AI models, and service principal.

Usage

The following assumes you'll be using the built-in VS Code MCP server support.

1. Open the project in VS Code. From the terminal, run:

   code .
   

2. Start one or more MCP servers using the configurations in .vscode/mcp.json. The file contains four different server configurations, each representing a different store manager role:

   • Each configuration uses a unique RLS (Row Level Security) user ID
   • These user IDs simulate different store manager identities accessing the database
   • The RLS system restricts data access based on the manager's assigned store
   • This mimics real-world scenarios where store managers sign in with different Entra ID accounts

   {
       "servers": {
           "zava-sales-analysis-headoffice": {
               "url": "http://127.0.0.1:8000/mcp",
               "type": "http",
               "headers": {"x-rls-user-id": "00000000-0000-0000-0000-000000000000"}
           },
           "zava-sales-analysis-seattle": {
               "url": "http://127.0.0.1:8000/mcp",
               "type": "http",
               "headers": {"x-rls-user-id": "f47ac10b-58cc-4372-a567-0e02b2c3d479"}
           },
           "zava-sales-analysis-redmond": {
               "url": "http://127.0.0.1:8000/mcp",
               "type": "http",
               "headers": {"x-rls-user-id": "e7f8a9b0-c1d2-3e4f-5678-90abcdef1234"}
           },
           "zava-sales-analysis-online": {
               "url": "http://127.0.0.1:8000/mcp",
               "type": "http",
               "headers": {"x-rls-user-id": "2f4e6d8c-1a3b-5c7e-9f0a-b2d4f6e8c0a2"}
           }
       },
       "inputs": []
   }
   

Open VS Code AI Chat

1. Open AI Chat mode in VS Code
2. Type #zava and select one of the MCP servers you started
3. Ask questions about the sales data - See sample queries below

Sample Queries

1. Show top 20 products by sales revenue
2. Show sales by store
3. What were the last quarter's sales by category?
4. What products do we sell that are similar to "containers for paint"

Features

• Multi-table Schema Access: Retrieve schemas for multiple database tables in a single request
• Secure Query Execution: Execute PostgreSQL queries with Row Level Security (RLS) support
• Real-time Data: Access current sales, inventory, and customer data
• Date/Time Utilities: Get current UTC timestamps for time-sensitive analysis
• Flexible Deployment: Supports HTTP server mode

Supported Tables

The server provides access to the following retail database tables:

• retail.customers - Customer information and profiles
• retail.stores - Store locations and details
• retail.categories - Product categories and hierarchies
• retail.product_types - Product type classifications
• retail.products - Product catalog and specifications
• retail.orders - Customer orders and transactions
• retail.order_items - Individual items within orders
• retail.inventory - Current inventory levels and stock data

Tools Available

get_multiple_table_schemas

Retrieve database schemas for multiple tables in a single request.

Parameters:

• table_names (list[str]): List of valid table names from the supported tables above

Returns: Concatenated schema strings for the requested tables

execute_sales_query

Execute PostgreSQL queries against the sales database with Row Level Security.

Parameters:

• postgresql_query (str): A well-formed PostgreSQL query

Returns: Query results formatted as a string (limited to 20 rows for readability)

Best Practices:

• Always fetch table schemas first
• Use exact column names from schemas
• Join related tables for comprehensive analysis
• Aggregate results when appropriate
• Limit output for readability

get_current_utc_date

Get the current UTC date and time in ISO format.

Returns: Current UTC date/time in ISO format (YYYY-MM-DDTHH:MM:SS.fffffZ)

semantic_search_products

Perform a semantic search for products based on user queries.

Returns: A list of products matching the search criteria

Parameters:

• query (str): The search query string

Returns: A list of products matching the search criteria

Security Features

Row Level Security (RLS)

The server implements Row Level Security to ensure users only access data they're authorized to view:

• HTTP Mode: Uses x-rls-user-id header to identify the requesting user

• Default Fallback: Uses placeholder UUID when no user ID is provided

Store-Specific RLS User IDs

Each Zava Retail store location has a unique RLS user ID that determines which data the user can access:

RLS Implementation

When a user connects with a specific store's RLS User ID, they will only see:

• Customers associated with that store
• Orders placed at that store location
• Inventory data for that specific store
• Store-specific sales and performance metrics

This ensures data isolation between different store locations while maintaining a unified database schema.

Architecture

Application Context

The server uses a managed application context with:

• Database Connection Pool: Efficient connection management for HTTP mode
• Lifecycle Management: Proper resource cleanup on shutdown
• Type Safety: Strongly typed context with AppContext dataclass

Request Context

• Header Extraction: Secure header parsing for user identification
• RLS Integration: Automatic user ID resolution from request context
• Error Handling: Comprehensive error handling with user-friendly messages

Database Integration

The server integrates with a PostgreSQL database through the PostgreSQLSchemaProvider class:

• Connection Pooling: Uses async connection pools for scalability
• Schema Metadata: Provides detailed table schema information
• Query Execution: Secure query execution with RLS support
• Resource Management: Automatic cleanup of database resources

Error Handling

The server implements robust error handling:

• Table Validation: Ensures only valid table names are accessed
• Query Validation: Validates PostgreSQL queries before execution
• Resource Management: Proper cleanup even during errors
• User-Friendly Messages: Clear error messages for troubleshooting

Security Considerations

1. Row Level Security: All queries respect RLS policies based on user identity
2. Store Data Isolation: Each store's RLS User ID ensures access only to that store's data
3. Input Validation: Table names and queries are validated before execution
4. Resource Limits: Query results are limited to prevent excessive resource usage
5. Connection Security: Uses secure database connection practices
6. User Identity Verification: Always ensure the correct RLS User ID is used for the intended store location

Important Security Notes

• Never use production RLS User IDs in development environments
• Always verify the RLS User ID matches the intended store before running queries
• The default UUID (00000000-0000-0000-0000-000000000000) provides limited access
• Each store manager should only have access to their store's RLS User ID

Development

Project Structure

app/
├── sales_analysis.py          # Main MCP server implementation
├── sales_analysis_postgres.py # PostgreSQL integration layer
├── sales_analysis_text_embedding.py # Text embedding for semantic search tool

Key Components

• FastMCP Server: Modern MCP server implementation with async support
• PostgreSQL Provider: Database abstraction layer with RLS support
• Context Management: Type-safe application and request context handling
• Tool Registration: Declarative tool registration with Pydantic validation

Contributing

When contributing to this server:

1. Ensure all database queries respect Row Level Security
2. Add proper error handling for new tools
3. Update this README with any new features or changes
4. Test the HTTP server mode
5. Validate input parameters and provide clear error messages

License

[Include appropriate license information]

This MCP server enables secure, efficient access to Zava Retail sales data for AI-powered analysis and insights.