wehub-resource-sync
36 KiB
36 KiB
Integrasi VS Code
🎯 Apa Yang Diliputi Dalam Makmal Ini
Makmal ini menyediakan panduan lengkap untuk mengintegrasikan pelayan MCP anda dengan VS Code bagi membolehkan pertanyaan bahasa semula jadi melalui AI Chat. Anda akan belajar cara mengkonfigurasi VS Code untuk penggunaan MCP yang optimum, menyelesaikan masalah sambungan pelayan, dan memanfaatkan sepenuhnya interaksi pangkalan data yang dibantu AI.
Gambaran Keseluruhan
Integrasi MCP dengan VS Code mengubah cara pemaju berinteraksi dengan pangkalan data dan API melalui bahasa semula jadi. Dengan menyambungkan pelayan MCP runcit anda ke VS Code Chat, anda boleh membuat pertanyaan pintar mengenai data jualan, katalog produk, dan analitik perniagaan menggunakan AI perbualan.
Integrasi ini membolehkan pemaju bertanya soalan seperti "Tunjukkan produk terlaris bulan ini" atau "Cari pelanggan yang belum membuat pembelian dalam 90 hari" dan mendapatkan respons data berstruktur tanpa perlu menulis pertanyaan SQL.
Objektif Pembelajaran
Menjelang akhir makmal ini, anda akan dapat:
- Mengkonfigurasi tetapan MCP VS Code untuk pelayan runcit anda
- Mengintegrasikan pelayan MCP dengan fungsi AI Chat VS Code
- Menyelesaikan masalah sambungan pelayan MCP dan mengatasi isu
- Mengoptimumkan corak pertanyaan bahasa semula jadi untuk hasil yang lebih baik
- Menyesuaikan ruang kerja VS Code untuk pembangunan MCP
- Menyebarkan konfigurasi pelbagai pelayan untuk senario kompleks
🔧 Konfigurasi MCP VS Code
Persediaan Awal dan Pemasangan
// .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"
}
Konfigurasi Persekitaran
# .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
Konfigurasi Ruang Kerja
// .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"
]
}
]
}
Konfigurasi Tugas
// .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"
}
]
}
💬 Integrasi AI Chat
Corak Pertanyaan Bahasa Semula Jadi
// 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"]
}
];
Contoh Integrasi 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
Pemformatan Respons 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
🔍 Penyelesaian Masalah dan Debugging
Konfigurasi Debug 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()
Penyelesaian Masalah Sambungan
# 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())
🚀 Konfigurasi Lanjutan
Persediaan Pelbagai Pelayan
// .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
}
}
}
}
Sambungan VS Code Tersuai
// 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() {}
Konfigurasi Pakej Sambungan
// 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"
}
}
🎯 Poin Penting
Selepas menyelesaikan makmal ini, anda seharusnya mempunyai:
✅ Konfigurasi MCP VS Code: Persediaan lengkap untuk integrasi MCP yang optimum
✅ Integrasi AI Chat: Keupayaan pertanyaan bahasa semula jadi dalam VS Code
✅ Alat Debugging: Penyelesaian masalah dan diagnostik sambungan yang komprehensif
✅ Persediaan Pelbagai Pelayan: Konfigurasi untuk pelbagai instans pelayan MCP
✅ Sambungan Tersuai: Pengalaman VS Code yang dipertingkatkan dengan ciri khusus runcit
✅ Kesediaan Pengeluaran: Persekitaran pembangunan VS Code yang sedia untuk perusahaan
🚀 Langkah Seterusnya
Teruskan dengan Makmal 10: Strategi Penyebaran untuk:
- Menyebarkan pelayan MCP ke persekitaran pengeluaran
- Mengkonfigurasi infrastruktur awan untuk skalabiliti
- Melaksanakan saluran CI/CD untuk penyebaran automatik
- Memantau prestasi pelayan MCP pengeluaran
📚 Sumber Tambahan
Pembangunan VS Code
- VS Code Extension API - Panduan rasmi pembangunan sambungan
- Dokumentasi MCP VS Code - Dokumentasi integrasi MCP
- TypeScript untuk VS Code - Pembangunan TypeScript dalam VS Code
Protokol MCP
- Spesifikasi Protokol Model Context - Spesifikasi MCP rasmi
- Amalan Terbaik MCP - Amalan terbaik pelaksanaan
- Kerangka FastMCP - Pelaksanaan MCP Python
Alat Pembangunan
- Python dalam VS Code - Persediaan pembangunan Python
- Debugging dalam VS Code - Teknik debugging lanjutan
- Tugas VS Code - Automasi dan konfigurasi tugas
Sebelumnya: Makmal 08: Ujian dan Debugging
Seterusnya: Makmal 10: Strategi Penyebaran
Penafian:
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