09 - VS Code Integration

VS Code Integration

🎯 What This Lab Covers

This lab provides comprehensive guidance on integrating your MCP server with VS Code to enable natural language queries through AI Chat.

You'll learn to configure VS Code for optimal MCP usage, debug server connections, and leverage the full power of AI-assisted database interactions.

Overview

VS Code's MCP integration transforms how developers interact with databases and APIs through natural language.

By connecting your retail MCP server to VS Code Chat, you enable intelligent querying of sales data, product catalogs, and business analytics using conversational AI.

This integration allows developers to ask questions like "Show me top selling products this month" or "Find customers who haven't purchased in 90 days" and get structured data responses without writing SQL queries.

Learning Objectives

By the end of this lab, you will be able to:

🔧 VS Code MCP Configuration

Initial Setup and Installation

// .vscode/settings.json

{

    "mcp.servers": {

        "retail-mcp-server": {

            "command": "python",

            "args": [

                "-m", "mcp_server.main"

            ],

            "env": {

                "POSTGRES_HOST": "localhost",

                "POSTGRES_PORT": "5432",

                "POSTGRES_DB": "retail_db",

                "POSTGRES_USER": "mcp_user",

                "POSTGRES_PASSWORD": "${env:POSTGRES_PASSWORD}",

                "PROJECT_ENDPOINT": "${env:PROJECT_ENDPOINT}",

                "AZURE_CLIENT_ID": "${env:AZURE_CLIENT_ID}",

                "AZURE_CLIENT_SECRET": "${env:AZURE_CLIENT_SECRET}",

                "AZURE_TENANT_ID": "${env:AZURE_TENANT_ID}",

                "LOG_LEVEL": "INFO",

                "MCP_SERVER_DEBUG": "false"

            },

            "cwd": "${workspaceFolder}",

            "initializationOptions": {

                "store_id": "seattle",

                "enable_semantic_search": true,

                "enable_analytics": true,

                "cache_embeddings": true

            }

        }

    },

    "mcp.serverTimeout": 30000,

    "mcp.enableLogging": true,

    "mcp.logLevel": "info"

}

Environment Configuration

# .env file for development

POSTGRES_HOST=localhost

POSTGRES_PORT=5432

POSTGRES_DB=retail_db

POSTGRES_USER=mcp_user

POSTGRES_PASSWORD=your_secure_password



# Azure Configuration

PROJECT_ENDPOINT=https://your-project.openai.azure.com

AZURE_CLIENT_ID=your-client-id

AZURE_CLIENT_SECRET=your-client-secret

AZURE_TENANT_ID=your-tenant-id



# Optional: Azure Key Vault

AZURE_KEY_VAULT_URL=https://your-keyvault.vault.azure.net/



# Server Configuration

MCP_SERVER_PORT=8000

MCP_SERVER_HOST=127.0.0.1

LOG_LEVEL=INFO

Workspace Configuration

// .vscode/launch.json

{

    "version": "0.2.0",

    "configurations": [

        {

            "name": "Debug MCP Server",

            "type": "python",

            "request": "launch",

            "module": "mcp_server.main",

            "console": "integratedTerminal",

            "envFile": "${workspaceFolder}/.env",

            "env": {

                "MCP_SERVER_DEBUG": "true",

                "LOG_LEVEL": "DEBUG"

            },

            "args": [],

            "justMyCode": false,

            "stopOnEntry": false

        },

        {

            "name": "Test MCP Server",

            "type": "python",

            "request": "launch",

            "module": "pytest",

            "console": "integratedTerminal",

            "envFile": "${workspaceFolder}/.env.test",

            "args": [

                "tests/",

                "-v",

                "--tb=short"

            ]

        }

    ]

}

Task Configuration

// .vscode/tasks.json

{

    "version": "2.0.0",

    "tasks": [

        {

            "label": "Start MCP Server",

            "type": "shell",

            "command": "python",

            "args": [

                "-m", "mcp_server.main"

            ],

            "group": "build",

            "presentation": {

                "echo": true,

                "reveal": "always",

                "focus": false,

                "panel": "new"

            },

            "options": {

                "env": {

                    "PYTHONPATH": "${workspaceFolder}"

                }

            },

            "isBackground": true,

            "problemMatcher": {

                "pattern": {

                    "regexp": "^(.*):(\\d+):(\\d+):\\s+(warning|error):\\s+(.*)$",

                    "file": 1,

                    "line": 2,

                    "column": 3,

                    "severity": 4,

                    "message": 5

                },

                "background": {

                    "activeOnStart": true,

                    "beginsPattern": "^.*Starting MCP server.*$",

                    "endsPattern": "^.*MCP server ready.*$"

                }

            }

        },

        {

            "label": "Run Tests",

            "type": "shell",

            "command": "python",

            "args": [

                "-m", "pytest",

                "tests/",

                "-v"

            ],

            "group": "test",

            "presentation": {

                "echo": true,

                "reveal": "always",

                "focus": false,

                "panel": "shared"

            }

        },

        {

            "label": "Generate Sample Data",

            "type": "shell",

            "command": "python",

            "args": [

                "scripts/generate_sample_data.py"

            ],

            "group": "build",

            "presentation": {

                "echo": true,

                "reveal": "always",

                "focus": false,

                "panel": "shared"

            }

        },

        {

            "label": "Create Database Schema",

            "type": "shell",

            "command": "psql",

            "args": [

                "-h", "${env:POSTGRES_HOST}",

                "-p", "${env:POSTGRES_PORT}",

                "-U", "${env:POSTGRES_USER}",

                "-d", "${env:POSTGRES_DB}",

                "-f", "scripts/create_schema.sql"

            ],

            "group": "build"

        }

    ]

}

💬 AI Chat Integration

Natural Language Query Patterns

// Example query patterns for VS Code Chat

interface QueryPattern {

    intent: string;

    examples: string[];

    expectedTools: string[];

}



const retailQueryPatterns: QueryPattern[] = [

    {

        intent: "sales_analysis",

        examples: [

            "Show me daily sales for the last 30 days",

            "What are our top selling products this month?",

            "Which customers have spent the most this quarter?",

            "Compare sales performance between stores"

        ],

        expectedTools: ["execute_sales_query"]

    },

    {

        intent: "product_search",

        examples: [

            "Find running shoes for women",

            "Show me electronics under $500",

            "What laptops do we have in stock?",

            "Search for wireless headphones"

        ],

        expectedTools: ["semantic_search_products", "hybrid_product_search"]

    },

    {

        intent: "inventory_management",

        examples: [

            "Which products are low on stock?",

            "Show me products that need reordering",

            "What's our current inventory value?",

            "Find products with zero stock"

        ],

        expectedTools: ["execute_sales_query"]

    },

    {

        intent: "customer_analysis",

        examples: [

            "Show me customers who haven't purchased in 90 days",

            "What's the average customer lifetime value?",

            "Which customers are in the gold tier?",

            "Find customers with returns"

        ],

        expectedTools: ["execute_sales_query"]

    },

    {

        intent: "business_intelligence",

        examples: [

            "Generate a business summary for this month",

            "Show me seasonal trends",

            "What are our best performing categories?",

            "Create a sales forecast"

        ],

        expectedTools: ["generate_business_insights"]

    },

    {

        intent: "recommendations",

        examples: [

            "Recommend products similar to product X",

            "What should we recommend to customer Y?",

            "Show me trending products",

            "Find cross-sell opportunities"

        ],

        expectedTools: ["get_product_recommendations"]

    }

];

Chat Integration Examples

<!-- Examples of VS Code Chat interactions -->



## Sales Analysis Queries



**User**: Show me the top 10 selling products in the Seattle store for the last month



**Expected Response**: 

- Tool: execute_sales_query

- Parameters: query_type="top_products", store_id="seattle", start_date="2025-08-29", end_date="2025-09-29", limit=10

- Result: Formatted table with product names, quantities sold, revenue, and performance metrics



**User**: What was our daily revenue trend last week?



**Expected Response**:

- Tool: execute_sales_query  

- Parameters: query_type="daily_sales", store_id="seattle", start_date="2025-09-22", end_date="2025-09-29"

- Result: Chart-ready data with daily revenue figures and growth percentages



## Product Search Queries



**User**: Find comfortable running shoes for outdoor activities



**Expected Response**:

- Tool: semantic_search_products

- Parameters: query="comfortable running shoes outdoor activities", store_id="seattle", similarity_threshold=0.7

- Result: Ranked list of relevant products with similarity scores and detailed information



**User**: Search for laptops under $1500 with good reviews



**Expected Response**:

- Tool: hybrid_product_search

- Parameters: query="laptops under $1500 good reviews", store_id="seattle", semantic_weight=0.6, keyword_weight=0.4

- Result: Combined keyword and semantic search results with price and rating filters



## Business Intelligence Queries



**User**: Generate a comprehensive business summary for September



**Expected Response**:

- Tool: generate_business_insights

- Parameters: analysis_type="summary", store_id="seattle", days=30

- Result: KPI dashboard with revenue, customer metrics, top categories, and growth trends

Chat Response Formatting

# mcp_server/chat/response_formatter.py

"""

Format MCP tool responses for optimal VS Code Chat display.

"""

from typing import Dict, Any, List

import json

from datetime import datetime



class ChatResponseFormatter:

    """Format tool responses for VS Code Chat consumption."""

    

    @staticmethod

    def format_sales_data(data: List[Dict[str, Any]], query_type: str) -> str:

        """Format sales data for chat display."""

        

        if not data:

            return "No sales data found for the specified criteria."

        

        if query_type == "daily_sales":

            return ChatResponseFormatter._format_daily_sales(data)

        elif query_type == "top_products":

            return ChatResponseFormatter._format_top_products(data)

        elif query_type == "customer_analysis":

            return ChatResponseFormatter._format_customer_analysis(data)

        else:

            return ChatResponseFormatter._format_generic_table(data)

    

    @staticmethod

    def _format_daily_sales(data: List[Dict[str, Any]]) -> str:

        """Format daily sales data."""

        

        response = "## Daily Sales Summary\n\n"

        response += "| Date | Revenue | Transactions | Avg Order Value | Customers |\n"

        response += "|------|---------|-------------|----------------|----------|\n"

        

        total_revenue = 0

        total_transactions = 0

        

        for day in data:

            revenue = float(day.get('total_revenue', 0))

            transactions = int(day.get('transaction_count', 0))

            avg_value = float(day.get('avg_transaction_value', 0))

            customers = int(day.get('unique_customers', 0))

            

            total_revenue += revenue

            total_transactions += transactions

            

            response += f"| {day.get('sales_date', 'N/A')} | "

            response += f"${revenue:,.2f} | "

            response += f"{transactions:,} | "

            response += f"${avg_value:.2f} | "

            response += f"{customers:,} |\n"

        

        response += f"\n**Totals**: ${total_revenue:,.2f} revenue, {total_transactions:,} transactions"

        

        return response

    

    @staticmethod

    def _format_top_products(data: List[Dict[str, Any]]) -> str:

        """Format top products data."""

        

        response = "## Top Selling Products\n\n"

        response += "| Rank | Product | Brand | Revenue | Qty Sold | Avg Price |\n"

        response += "|------|---------|-------|---------|----------|----------|\n"

        

        for i, product in enumerate(data, 1):

            response += f"| {i} | "

            response += f"{product.get('product_name', 'N/A')} | "

            response += f"{product.get('brand', 'N/A')} | "

            response += f"${float(product.get('total_revenue', 0)):,.2f} | "

            response += f"{int(product.get('total_quantity_sold', 0)):,} | "

            response += f"${float(product.get('avg_price', 0)):.2f} |\n"

        

        return response

    

    @staticmethod

    def format_search_results(data: List[Dict[str, Any]], search_type: str) -> str:

        """Format product search results."""

        

        if not data:

            return "No products found matching your search criteria."

        

        response = f"## Product Search Results ({search_type})\n\n"

        

        for i, product in enumerate(data, 1):

            response += f"### {i}. {product.get('product_name', 'Unknown Product')}\n"

            response += f"**Brand**: {product.get('brand', 'N/A')}\n"

            response += f"**Price**: ${float(product.get('price', 0)):.2f}\n"

            response += f"**Stock**: {int(product.get('current_stock', 0))} units\n"

            

            if 'similarity_score' in product:

                score = float(product['similarity_score'])

                response += f"**Relevance**: {score:.1%}\n"

            

            if 'rating_average' in product and product['rating_average']:

                rating = float(product['rating_average'])

                count = int(product.get('rating_count', 0))

                response += f"**Rating**: {rating:.1f}/5.0 ({count:,} reviews)\n"

            

            if product.get('product_description'):

                desc = product['product_description']

                if len(desc) > 150:

                    desc = desc[:150] + "..."

                response += f"**Description**: {desc}\n"

            

            response += "\n---\n\n"

        

        return response

    

    @staticmethod

    def format_business_insights(data: Dict[str, Any]) -> str:

        """Format business intelligence data."""

        

        response = "## Business Intelligence Summary\n\n"

        

        # Key metrics

        response += "### Key Performance Indicators\n\n"

        response += f"- **Total Revenue**: ${float(data.get('total_revenue', 0)):,.2f}\n"

        response += f"- **Total Transactions**: {int(data.get('total_transactions', 0)):,}\n"

        response += f"- **Unique Customers**: {int(data.get('unique_customers', 0)):,}\n"

        response += f"- **Average Order Value**: ${float(data.get('avg_transaction_value', 0)):.2f}\n"

        response += f"- **Products Sold**: {int(data.get('products_sold', 0)):,} items\n\n"

        

        # Performance indicators

        if 'insights' in data and 'performance_indicators' in data['insights']:

            pi = data['insights']['performance_indicators']

            response += "### Performance Indicators\n\n"

            response += f"- **Transactions per Day**: {float(pi.get('transactions_per_day', 0)):.1f}\n"

            response += f"- **Revenue per Customer**: ${float(pi.get('revenue_per_customer', 0)):,.2f}\n"

            response += f"- **Items per Transaction**: {float(pi.get('items_per_transaction', 0)):.1f}\n\n"

        

        # Top category

        if data.get('top_category'):

            response += f"### Top Performing Category\n\n"

            response += f"**{data['top_category']}** - ${float(data.get('top_category_revenue', 0)):,.2f} revenue\n\n"

        

        return response

    

    @staticmethod

    def format_error_response(error: str, tool_name: str) -> str:

        """Format error responses for chat."""

        

        response = f"## ❌ Error in {tool_name}\n\n"

        response += f"I encountered an issue while processing your request:\n\n"

        response += f"**Error**: {error}\n\n"

        response += "Please try:\n"

        response += "- Checking your query parameters\n"

        response += "- Verifying store access permissions\n"

        response += "- Simplifying your request\n"

        response += "- Contacting support if the issue persists\n"

        

        return response

🔍 Debugging and Troubleshooting

VS Code Debug Configuration

# mcp_server/debug/vscode_debug.py

"""

VS Code specific debugging utilities for MCP server.

"""

import logging

import json

from typing import Dict, Any

from datetime import datetime



class VSCodeDebugLogger:

    """Enhanced logging for VS Code debugging."""

    

    def __init__(self):

        self.logger = logging.getLogger("mcp_vscode_debug")

        self.setup_vscode_logging()

    

    def setup_vscode_logging(self):

        """Configure logging for VS Code debugging."""

        

        # Create VS Code specific formatter

        formatter = logging.Formatter(

            '[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s'

        )

        

        # Console handler for VS Code terminal

        console_handler = logging.StreamHandler()

        console_handler.setFormatter(formatter)

        console_handler.setLevel(logging.DEBUG)

        

        self.logger.addHandler(console_handler)

        self.logger.setLevel(logging.DEBUG)

    

    def log_mcp_request(self, method: str, params: Dict[str, Any]):

        """Log MCP requests for debugging."""

        

        self.logger.info(f"MCP Request: {method}")

        self.logger.debug(f"Parameters: {json.dumps(params, indent=2)}")

    

    def log_tool_execution(self, tool_name: str, result: Dict[str, Any]):

        """Log tool execution results."""

        

        success = result.get('success', False)

        level = logging.INFO if success else logging.ERROR

        

        self.logger.log(level, f"Tool '{tool_name}' - {'Success' if success else 'Failed'}")

        

        if not success and result.get('error'):

            self.logger.error(f"Error: {result['error']}")

        

        if result.get('data'):

            data_summary = self._summarize_data(result['data'])

            self.logger.debug(f"Result summary: {data_summary}")

    

    def _summarize_data(self, data: Any) -> str:

        """Create a summary of result data."""

        

        if isinstance(data, list):

            return f"List with {len(data)} items"

        elif isinstance(data, dict):

            return f"Dict with keys: {list(data.keys())}"

        else:

            return f"Data type: {type(data).__name__}"



# Global debug logger

vscode_debug_logger = VSCodeDebugLogger()

Connection Troubleshooting

# scripts/debug_mcp_connection.py

"""

Debug script for troubleshooting MCP server connections in VS Code.

"""

import asyncio

import asyncpg

import os

import sys

from typing import Dict, Any



async def test_database_connection() -> Dict[str, Any]:

    """Test database connectivity."""

    

    try:

        # Get connection parameters from environment

        connection_params = {

            'host': os.getenv('POSTGRES_HOST', 'localhost'),

            'port': int(os.getenv('POSTGRES_PORT', '5432')),

            'database': os.getenv('POSTGRES_DB', 'retail_db'),

            'user': os.getenv('POSTGRES_USER', 'mcp_user'),

            'password': os.getenv('POSTGRES_PASSWORD', '')

        }

        

        print(f"Testing connection to {connection_params['host']}:{connection_params['port']}")

        

        # Test connection

        conn = await asyncpg.connect(**connection_params)

        

        # Test basic query

        result = await conn.fetchval("SELECT version()")

        

        # Test schema access

        tables = await conn.fetch("""

            SELECT table_name FROM information_schema.tables 

            WHERE table_schema = 'retail'

        """)

        

        await conn.close()

        

        return {

            'success': True,

            'database_version': result,

            'retail_tables': len(tables),

            'table_names': [table['table_name'] for table in tables]

        }

        

    except Exception as e:

        return {

            'success': False,

            'error': str(e),

            'connection_params': {k: v for k, v in connection_params.items() if k != 'password'}

        }



async def test_azure_openai_connection() -> Dict[str, Any]:

    """Test Azure OpenAI connectivity."""

    

    try:

        from azure.identity import DefaultAzureCredential

        from azure.ai.projects import AIProjectClient

        

        project_endpoint = os.getenv('PROJECT_ENDPOINT')

        if not project_endpoint:

            return {

                'success': False,

                'error': 'PROJECT_ENDPOINT not configured'

            }

        

        print(f"Testing Azure OpenAI connection to {project_endpoint}")

        

        credential = DefaultAzureCredential()

        client = AIProjectClient(

            endpoint=project_endpoint,

            credential=credential

        )

        

        # Test embedding generation

        response = await client.embeddings.create(

            model="text-embedding-3-small",

            input="test connection"

        )

        

        embedding = response.data[0].embedding

        

        return {

            'success': True,

            'project_endpoint': project_endpoint,

            'embedding_dimension': len(embedding),

            'model': 'text-embedding-3-small'

        }

        

    except Exception as e:

        return {

            'success': False,

            'error': str(e),

            'project_endpoint': os.getenv('PROJECT_ENDPOINT', 'Not configured')

        }



async def test_mcp_tools() -> Dict[str, Any]:

    """Test MCP tool availability."""

    

    try:

        # Import MCP server components

        sys.path.append(os.path.dirname(os.path.dirname(__file__)))

        

        from mcp_server.server import MCPServer

        from mcp_server.database import DatabaseProvider

        from mcp_server.config import Config

        

        # Create test configuration

        config = Config()

        db_provider = DatabaseProvider(config.database.connection_string)

        

        # Initialize server

        server = MCPServer(config, db_provider)

        await server.initialize()

        

        # Get available tools

        tools = server.get_available_tools()

        

        # Test a simple tool

        test_result = await server.execute_tool(

            'get_current_utc_date',

            {'format': 'iso'}

        )

        

        await server.cleanup()

        

        return {

            'success': True,

            'available_tools': [tool.name for tool in tools],

            'tool_count': len(tools),

            'test_tool_result': test_result.get('success', False)

        }

        

    except Exception as e:

        return {

            'success': False,

            'error': str(e)

        }



async def main():

    """Run comprehensive connection tests."""

    

    print("🔍 MCP Server Connection Diagnostics")

    print("=" * 50)

    

    # Test database connection

    print("\n📊 Testing Database Connection...")

    db_result = await test_database_connection()

    

    if db_result['success']:

        print("✅ Database connection successful")

        print(f"   Database version: {db_result['database_version']}")

        print(f"   Retail tables found: {db_result['retail_tables']}")

        print(f"   Table names: {', '.join(db_result['table_names'])}")

    else:

        print("❌ Database connection failed")

        print(f"   Error: {db_result['error']}")

    

    # Test Azure OpenAI connection

    print("\n🤖 Testing Azure OpenAI Connection...")

    azure_result = await test_azure_openai_connection()

    

    if azure_result['success']:

        print("✅ Azure OpenAI connection successful")

        print(f"   Endpoint: {azure_result['project_endpoint']}")

        print(f"   Embedding dimension: {azure_result['embedding_dimension']}")

    else:

        print("❌ Azure OpenAI connection failed")

        print(f"   Error: {azure_result['error']}")

    

    # Test MCP tools

    print("\n🛠️  Testing MCP Tools...")

    tools_result = await test_mcp_tools()

    

    if tools_result['success']:

        print("✅ MCP tools loaded successfully")

        print(f"   Available tools: {tools_result['tool_count']}")

        print(f"   Tool names: {', '.join(tools_result['available_tools'])}")

        print(f"   Test execution: {'✅' if tools_result['test_tool_result'] else '❌'}")

    else:

        print("❌ MCP tools loading failed")

        print(f"   Error: {tools_result['error']}")

    

    # Overall status

    print("\n📋 Overall Status")

    print("=" * 50)

    

    all_success = all([

        db_result['success'],

        azure_result['success'],

        tools_result['success']

    ])

    

    if all_success:

        print("🎉 All systems ready! MCP server should work correctly in VS Code.")

    else:

        print("⚠️  Some issues detected. Please resolve the errors above.")

        print("\n💡 Troubleshooting tips:")

        print("   - Check environment variables in .env file")

        print("   - Verify database is running and accessible")

        print("   - Confirm Azure credentials are configured")

        print("   - Review VS Code MCP server configuration")



if __name__ == "__main__":

    asyncio.run(main())

🚀 Advanced Configuration

Multi-Server Setup

// .vscode/settings.json - Multiple MCP servers

{

    "mcp.servers": {

        "retail-seattle": {

            "command": "python",

            "args": ["-m", "mcp_server.main"],

            "env": {

                "POSTGRES_HOST": "localhost",

                "POSTGRES_DB": "retail_db",

                "POSTGRES_USER": "mcp_user",

                "POSTGRES_PASSWORD": "${env:POSTGRES_PASSWORD}",

                "PROJECT_ENDPOINT": "${env:PROJECT_ENDPOINT}",

                "DEFAULT_STORE_ID": "seattle"

            },

            "initializationOptions": {

                "store_id": "seattle",

                "server_name": "Seattle Store"

            }

        },

        "retail-redmond": {

            "command": "python",

            "args": ["-m", "mcp_server.main"],

            "env": {

                "POSTGRES_HOST": "localhost",

                "POSTGRES_DB": "retail_db",

                "POSTGRES_USER": "mcp_user",

                "POSTGRES_PASSWORD": "${env:POSTGRES_PASSWORD}",

                "PROJECT_ENDPOINT": "${env:PROJECT_ENDPOINT}",

                "DEFAULT_STORE_ID": "redmond"

            },

            "initializationOptions": {

                "store_id": "redmond",

                "server_name": "Redmond Store"

            }

        },

        "retail-analytics": {

            "command": "python",

            "args": ["-m", "mcp_server.analytics_main"],

            "env": {

                "POSTGRES_HOST": "localhost",

                "POSTGRES_DB": "retail_db",

                "POSTGRES_USER": "analytics_user",

                "POSTGRES_PASSWORD": "${env:ANALYTICS_PASSWORD}",

                "PROJECT_ENDPOINT": "${env:PROJECT_ENDPOINT}"

            },

            "initializationOptions": {

                "mode": "analytics",

                "cross_store_access": true

            }

        }

    }

}

Custom VS Code Extension

// src/extension.ts - Custom MCP retail extension

import * as vscode from 'vscode';



export function activate(context: vscode.ExtensionContext) {

    

    // Register MCP retail commands

    const disposable = vscode.commands.registerCommand(

        'mcp-retail.quickQuery', 

        async () => {

            const quickPick = vscode.window.createQuickPick();

            quickPick.items = [

                {

                    label: '📊 Daily Sales',

                    description: 'Show daily sales for the last 30 days'

                },

                {

                    label: '🏆 Top Products',

                    description: 'Show top selling products this month'

                },

                {

                    label: '👥 Customer Analysis',

                    description: 'Analyze customer behavior and trends'

                },

                {

                    label: '🔍 Product Search',

                    description: 'Search for products using natural language'

                },

                {

                    label: '📈 Business Insights',

                    description: 'Generate comprehensive business summary'

                }

            ];

            

            quickPick.onDidChangeSelection(selection => {

                if (selection[0]) {

                    executeQuickQuery(selection[0].label);

                }

            });

            

            quickPick.onDidHide(() => quickPick.dispose());

            quickPick.show();

        }

    );

    

    context.subscriptions.push(disposable);

    

    // Register store switcher

    const storeSwitcher = vscode.commands.registerCommand(

        'mcp-retail.switchStore',

        async () => {

            const stores = ['seattle', 'redmond', 'bellevue', 'online'];

            const selected = await vscode.window.showQuickPick(stores, {

                placeHolder: 'Select store for queries'

            });

            

            if (selected) {

                // Update configuration

                const config = vscode.workspace.getConfiguration('mcp');

                await config.update('defaultStore', selected, true);

                

                vscode.window.showInformationMessage(

                    `Switched to ${selected.charAt(0).toUpperCase() + selected.slice(1)} store`

                );

            }

        }

    );

    

    context.subscriptions.push(storeSwitcher);

}



async function executeQuickQuery(queryType: string) {

    // Execute predefined queries in VS Code Chat

    const chatCommands = {

        '📊 Daily Sales': '@retail Show me daily sales for the last 30 days',

        '🏆 Top Products': '@retail What are the top 10 selling products this month?',

        '👥 Customer Analysis': '@retail Show me customer analysis for active customers',

        '🔍 Product Search': '@retail Find products matching "laptop computer"',

        '📈 Business Insights': '@retail Generate a business summary for this month'

    };

    

    const command = chatCommands[queryType];

    if (command) {

        await vscode.commands.executeCommand('workbench.action.chat.open');

        await vscode.commands.executeCommand('workbench.action.chat.insert', command);

    }

}



export function deactivate() {}

Extension Package Configuration

// package.json for VS Code extension

{

    "name": "mcp-retail-assistant",

    "displayName": "MCP Retail Assistant",

    "description": "AI-powered retail data analysis through MCP",

    "version": "1.0.0",

    "engines": {

        "vscode": "^1.74.0"

    },

    "categories": [

        "Other",

        "Data Science",

        "Machine Learning"

    ],

    "activationEvents": [

        "onCommand:mcp-retail.quickQuery",

        "onCommand:mcp-retail.switchStore"

    ],

    "main": "./out/extension.js",

    "contributes": {

        "commands": [

            {

                "command": "mcp-retail.quickQuery",

                "title": "Quick Retail Query",

                "category": "MCP Retail"

            },

            {

                "command": "mcp-retail.switchStore",

                "title": "Switch Store",

                "category": "MCP Retail"

            }

        ],

        "keybindings": [

            {

                "command": "mcp-retail.quickQuery",

                "key": "ctrl+shift+r",

                "mac": "cmd+shift+r"

            }

        ],

        "configuration": {

            "title": "MCP Retail",

            "properties": {

                "mcp-retail.defaultStore": {

                    "type": "string",

                    "default": "seattle",

                    "enum": ["seattle", "redmond", "bellevue", "online"],

                    "description": "Default store for retail queries"

                },

                "mcp-retail.enableAnalytics": {

                    "type": "boolean",

                    "default": true,

                    "description": "Enable advanced analytics features"

                }

            }

        }

    },

    "scripts": {

        "vscode:prepublish": "npm run compile",

        "compile": "tsc -p ./",

        "watch": "tsc -watch -p ./"

    },

    "devDependencies": {

        "@types/vscode": "^1.74.0",

        "@types/node": "16.x",

        "typescript": "^4.9.4"

    }

}

🎯 Key Takeaways

After completing this lab, you should have:

✅ VS Code MCP Configuration: Complete setup for optimal MCP integration

✅ AI Chat Integration: Natural language querying capabilities in VS Code

✅ Debugging Tools: Comprehensive troubleshooting and connection diagnostics

✅ Multi-Server Setup: Configuration for multiple MCP server instances

✅ Custom Extensions: Enhanced VS Code experience with retail-specific features

✅ Production Readiness: Enterprise-ready VS Code development environment

🚀 What's Next

Continue with Lab 10: Deployment Strategies to:

📚 Additional Resources

VS Code Development

MCP Protocol

Development Tools

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Previous: Lab 08: Testing and Debugging

Next: Lab 10: Deployment Strategies