wehub-resource-sync
36 KiB
36 KiB
VS Code Integratsioon
🎯 Mida see labor hõlmab
See labor pakub põhjalikku juhendit MCP serveri integreerimiseks VS Code'iga, et võimaldada loomuliku keele päringuid AI Chat'i kaudu. Õpid, kuidas konfigureerida VS Code'i MCP kasutamiseks, siluda serveriühendusi ja kasutada AI-põhiseid andmebaasi interaktsioone maksimaalselt.
Ülevaade
VS Code'i MCP integratsioon muudab arendajate suhtlemist andmebaaside ja API-dega loomuliku keele kaudu. Ühendades oma jaemüügi MCP serveri VS Code Chat'iga, saad kasutada vestluslikku AI-d, et teha intelligentsed päringud müügiandmete, tootekataloogide ja ärianalüütika kohta.
See integratsioon võimaldab arendajatel esitada küsimusi nagu "Näita mulle selle kuu enimmüüdud tooteid" või "Leia kliendid, kes pole 90 päeva jooksul ostnud" ja saada struktureeritud andmeid ilma SQL-päringuid kirjutamata.
Õpieesmärgid
Labori lõpuks oskad:
- Konfigureerida VS Code'i MCP seadeid oma jaemüügi serveri jaoks
- Integreerida MCP servereid VS Code AI Chat funktsionaalsusega
- Siluda MCP serveriühendusi ja lahendada probleeme
- Optimeerida loomuliku keele päringumustreid paremate tulemuste saavutamiseks
- Kohandada VS Code'i tööruumi MCP arenduseks
- Rakendada mitme serveri konfiguratsioone keerukate stsenaariumide jaoks
🔧 VS Code MCP konfiguratsioon
Esmane seadistamine ja paigaldamine
// .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"
}
Keskkonna konfiguratsioon
# .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
Tööruumi konfiguratsioon
// .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"
]
}
]
}
Ülesannete konfiguratsioon
// .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 integratsioon
Loomuliku keele päringumustrid
// 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"]
}
];
Vestluse integratsiooni näited
<!-- 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
Vestluse vastuste vormindamine
# 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
🔍 Silumine ja probleemide lahendamine
VS Code'i silumise konfiguratsioon
# 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()
Ühenduse probleemide lahendamine
# 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())
🚀 Täiustatud konfiguratsioon
Mitme serveri seadistamine
// .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
}
}
}
}
Kohandatud VS Code'i laiendus
// 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() {}
Laienduse paketi konfiguratsioon
// 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"
}
}
🎯 Olulised järeldused
Pärast labori lõpetamist peaksid olema saavutanud:
✅ VS Code MCP konfiguratsioon: Täielik seadistus MCP integreerimiseks
✅ AI Chat integratsioon: Loomuliku keele päringuvõimalused VS Code'is
✅ Silumisvahendid: Põhjalik probleemide lahendamine ja ühenduse diagnostika
✅ Mitme serveri seadistus: Konfiguratsioon mitme MCP serveri jaoks
✅ Kohandatud laiendused: Täiustatud VS Code'i kogemus jaemüügi-spetsiifiliste funktsioonidega
✅ Tootmisvalmidus: Ettevõtte tasemel VS Code'i arenduskeskkond
🚀 Mis edasi?
Jätka Labor 10: Rakendamise strateegiad, et:
- Rakendada MCP servereid tootmiskeskkondadesse
- Konfigureerida pilve infrastruktuuri skaleeritavuse jaoks
- Rakendada CI/CD torujuhtmeid automaatseks juurutamiseks
- Jälgida tootmise MCP serveri jõudlust
📚 Täiendavad ressursid
VS Code arendus
- VS Code Extension API - Ametlik laienduste arendamise juhend
- VS Code MCP dokumentatsioon - MCP integratsiooni dokumentatsioon
- TypeScript VS Code'is - TypeScript arendus VS Code'is
MCP protokoll
- Model Context Protocol Specification - Ametlik MCP spetsifikatsioon
- MCP parimad praktikad - Rakendamise parimad praktikad
- FastMCP raamistik - Python MCP rakendus
Arendustööriistad
- Python VS Code'is - Python arenduse seadistamine
- Silumine VS Code'is - Täiustatud silumistehnikad
- VS Code'i ülesanded - Ülesannete automatiseerimine ja konfiguratsioon
Eelmine: Labor 08: Testimine ja silumine
Järgmine: Labor 10: Rakendamise strateegiad
Lahtiütlus:
See dokument on tõlgitud AI tõlketeenuse Co-op Translator abil. Kuigi püüame tagada täpsust, palume arvestada, et automaatsed tõlked võivad sisaldada vigu või ebatäpsusi. Algne dokument selle algses keeles tuleks pidada autoriteetseks allikaks. Olulise teabe puhul soovitame kasutada professionaalset inimtõlget. Me ei vastuta selle tõlke kasutamisest tulenevate arusaamatuste või valesti tõlgenduste eest.