09-VS-Code_VSCode集成
文档摘要
VS Code 集成 本实验内容 本实验为您提供全面的指导，帮助您将 MCP 服务器与 VS Code 集成，以通过 AI 聊天实现自然语言查询。您将学习如何配置 VS Code 以优化 MCP 的使用、调试服务器连接，并充分利用 AI 辅助的数据库交互功能。 概述 VS Code 的 MCP 集成彻底改变了开发者通过自然语言与数据库和 API 交互的方式。通过将您的零售 MCP 服务器连接到 VS Code Chat，您可以使用对话式 AI 智能查询销售数据、产品目录和业务分析。 这种集成允许开发者提出诸如“显示本月的畅销产品”或“查找 90 天内未购买的客户”之类的问题，并获得结构化数据响应，而无需编写 SQL 查询。
VS Code 集成
本实验内容
本实验为您提供全面的指导，帮助您将 MCP 服务器与 VS Code 集成，以通过 AI 聊天实现自然语言查询。您将学习如何配置 VS Code 以优化 MCP 的使用、调试服务器连接，并充分利用 AI 辅助的数据库交互功能。
概述
VS Code 的 MCP 集成彻底改变了开发者通过自然语言与数据库和 API 交互的方式。通过将您的零售 MCP 服务器连接到 VS Code Chat，您可以使用对话式 AI 智能查询销售数据、产品目录和业务分析。
这种集成允许开发者提出诸如“显示本月的畅销产品”或“查找 90 天内未购买的客户”之类的问题，并获得结构化数据响应，而无需编写 SQL 查询。
学习目标
完成本实验后，您将能够：
- 配置 VS Code MCP 设置以连接您的零售服务器
- 集成 MCP 服务器与 VS Code AI Chat 功能
- 调试 MCP 服务器连接并解决问题
- 优化 自然语言查询模式以获得更好的结果
- 自定义 VS Code 工作区以支持 MCP 开发
- 部署 多服务器配置以应对复杂场景
VS Code MCP 配置
初始设置与安装
// .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" }
环境配置
# .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
工作区配置
// .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" ] } ] }
任务配置
// .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 聊天集成
自然语言查询模式
// 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"] } ];
聊天集成示例
<!-- 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 $1500 good reviews", store_id="seattle", semantic_weight=0.6, keyword_weight=0.4 - Result: Combined keyword and semantic search results with price and rating filters ## Business Intelligence Queries **User**: Generate a comprehensive business summary for September **Expected Response**: - Tool: generate_business_insights - Parameters: analysis_type="summary", store_id="seattle", days=30 - Result: KPI dashboard with revenue, customer metrics, top categories, and growth trends
聊天响应格式化
# mcp_server/chat/response_formatter.py """ Format MCP tool responses for optimal VS Code Chat display. """ from typing import Dict, Any, List import json from datetime import datetime class ChatResponseFormatter: """Format tool responses for VS Code Chat consumption.""" @staticmethod def format_sales_data(data: List[Dict[str, Any]], query_type: str) -> str: """Format sales data for chat display.""" if not data: return "No sales data found for the specified criteria." if query_type == "daily_sales": return ChatResponseFormatter._format_daily_sales(data) elif query_type == "top_products": return ChatResponseFormatter._format_top_products(data) elif query_type == "customer_analysis": return ChatResponseFormatter._format_customer_analysis(data) else: return ChatResponseFormatter._format_generic_table(data) @staticmethod def _format_daily_sales(data: List[Dict[str, Any]]) -> str: """Format daily sales data.""" response = "## Daily Sales Summary\n\n" response += "| Date | Revenue | Transactions | Avg Order Value | Customers |\n" response += "|------|---------|-------------|----------------|----------|\n" total_revenue = 0 total_transactions = 0 for day in data: revenue = float(day.get('total_revenue', 0)) transactions = int(day.get('transaction_count', 0)) avg_value = float(day.get('avg_transaction_value', 0)) customers = int(day.get('unique_customers', 0)) total_revenue += revenue total_transactions += transactions response += f"| {day.get('sales_date', 'N/A')} | " response += f"${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
调试与故障排除
VS Code 调试配置
# 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()
连接故障排除
# 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("\n️ 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())
高级配置
多服务器设置
// .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 } } } }
自定义 VS Code 扩展
// 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() {}
扩展包配置
// 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" } }
关键收获
完成本实验后，您将掌握：
✅ VS Code MCP 配置：完成 MCP 集成的最佳设置
✅ AI 聊天集成：在 VS Code 中实现自然语言查询功能
✅ 调试工具：全面的故障排除和连接诊断能力
✅ 多服务器设置：配置多个 MCP 服务器实例
✅ 自定义扩展：通过零售特定功能增强 VS Code 体验
✅ 生产环境准备：企业级 VS Code 开发环境
下一步
继续学习 实验 10：部署策略，以了解：
- 将 MCP 服务器部署到生产环境
- 配置云基础设施以实现可扩展性
- 实现 CI/CD 流水线以自动化部署
- 监控生产环境 MCP 服务器性能
其他资源
VS Code 开发
- VS Code Extension API - 官方扩展开发指南
- VS Code MCP 文档 - MCP 集成文档
- VS Code 的 TypeScript - 在 VS Code 中进行 TypeScript 开发
MCP 协议
- 模型上下文协议规范 - 官方 MCP 规范
- MCP 最佳实践 - 实现最佳实践
- FastMCP 框架 - Python MCP 实现
开发工具
- VS Code 中的 Python - Python 开发设置
- VS Code 调试 - 高级调试技术
- VS Code 任务 - 任务自动化与配置
上一节: 实验 08：测试与调试
下一节: 实验 10：部署策略
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