wehub-resource-sync
37 KiB
37 KiB
Intégration avec VS Code
🎯 Ce que couvre ce laboratoire
Ce laboratoire fournit des instructions complètes pour intégrer votre serveur MCP avec VS Code afin d'activer les requêtes en langage naturel via AI Chat. Vous apprendrez à configurer VS Code pour une utilisation optimale de MCP, à déboguer les connexions au serveur et à exploiter pleinement la puissance des interactions assistées par l'IA avec les bases de données.
Aperçu
L'intégration de MCP avec VS Code transforme la manière dont les développeurs interagissent avec les bases de données et les API grâce au langage naturel. En connectant votre serveur MCP retail à VS Code Chat, vous permettez des requêtes intelligentes sur les données de ventes, les catalogues de produits et les analyses commerciales via une IA conversationnelle.
Cette intégration permet aux développeurs de poser des questions telles que "Montre-moi les produits les plus vendus ce mois-ci" ou "Trouve les clients qui n'ont pas acheté depuis 90 jours" et d'obtenir des réponses structurées sans écrire de requêtes SQL.
Objectifs d'apprentissage
À la fin de ce laboratoire, vous serez capable de :
- Configurer les paramètres MCP de VS Code pour votre serveur retail
- Intégrer les serveurs MCP avec la fonctionnalité AI Chat de VS Code
- Déboguer les connexions au serveur MCP et résoudre les problèmes
- Optimiser les modèles de requêtes en langage naturel pour de meilleurs résultats
- Personnaliser l'espace de travail VS Code pour le développement MCP
- Déployer des configurations multi-serveurs pour des scénarios complexes
🔧 Configuration de MCP dans VS Code
Configuration initiale et 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"
}
Configuration de l'environnement
# .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
Configuration de l'espace de travail
// .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"
]
}
]
}
Configuration des tâches
// .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"
}
]
}
💬 Intégration avec AI Chat
Modèles de requêtes en langage naturel
// 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"]
}
];
Exemples d'intégration avec le chat
<!-- 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
Formatage des réponses du chat
# 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
🔍 Débogage et résolution des problèmes
Configuration de débogage dans VS Code
# 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()
Résolution des problèmes de connexion
# 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())
🚀 Configuration avancée
Configuration multi-serveurs
// .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
}
}
}
}
Extension personnalisée pour VS Code
// 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() {}
Configuration du package d'extension
// 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"
}
}
🎯 Points clés à retenir
Après avoir terminé ce laboratoire, vous devriez avoir :
✅ Configuration MCP dans VS Code : Configuration complète pour une intégration optimale de MCP
✅ Intégration avec AI Chat : Capacités de requêtes en langage naturel dans VS Code
✅ Outils de débogage : Résolution complète des problèmes et diagnostics de connexion
✅ Configuration multi-serveurs : Configuration pour plusieurs instances de serveur MCP
✅ Extensions personnalisées : Expérience VS Code améliorée avec des fonctionnalités spécifiques au retail
✅ Prêt pour la production : Environnement de développement VS Code adapté aux entreprises
🚀 Et après ?
Continuez avec Lab 10 : Stratégies de déploiement pour :
- Déployer les serveurs MCP dans des environnements de production
- Configurer une infrastructure cloud pour la scalabilité
- Implémenter des pipelines CI/CD pour un déploiement automatisé
- Surveiller les performances des serveurs MCP en production
📚 Ressources supplémentaires
Développement avec VS Code
- API d'extension VS Code - Guide officiel de développement d'extensions
- Documentation MCP pour VS Code - Documentation sur l'intégration MCP
- TypeScript pour VS Code - Développement TypeScript dans VS Code
Protocole MCP
- Spécification du protocole Model Context - Spécification officielle de MCP
- Meilleures pratiques MCP - Meilleures pratiques d'implémentation
- Framework FastMCP - Implémentation MCP en Python
Outils de développement
- Python dans VS Code - Configuration pour le développement Python
- Débogage dans VS Code - Techniques avancées de débogage
- Tâches dans VS Code - Automatisation et configuration des tâches
Précédent : Lab 08 : Tests et débogage
Suivant : Lab 10 : Stratégies de déploiement
Avertissement :
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