---
# System prepended metadata

title: 3-4 熱門 MCP 伺服器實戰指南
tags: [MCP 白皮書]

---

# 3-4 熱門 MCP 伺服器實戰指南

回到白皮書首頁：[MCP 全方位技術白皮書](/@thc1006/mcp-whitepaper-home)

---

## 2025年最實用的 MCP 伺服器深度實戰

在 MCP 生態快速發展的 2025 年，選對伺服器往往比寫對代碼更重要。本章將深入解析 15 個最熱門、最實用的 MCP 伺服器，提供從安裝配置到企業級部署的完整實戰指南。

## 企業級資料平台類

### 1. K2view MCP Server：企業資料虛擬化之王

**為什麼選擇 K2view？**
K2view 解決了企業最頭痛的問題：**資料孤島**。它能夠即時整合跨多個系統的資料，而不需要傳統的 ETL 過程。

**核心能力：**
- **即時資料虛擬化**：不移動資料，直接虛擬整合
- **實體級資料存取**：以客戶、產品等實體為中心組織資料
- **跨系統安全存取**：統一的權限控制
- **企業級效能**：支援大規模並行處理

**實戰配置：**
```python
# K2view MCP 配置範例
class K2viewMCPConfig:
    def __init__(self):
        self.config = {
            'server_url': 'https://k2view.company.com:9443',
            'authentication': {
                'method': 'oauth2',
                'client_id': 'mcp-client',
                'client_secret': '${K2VIEW_CLIENT_SECRET}',
                'token_url': 'https://auth.company.com/oauth/token'
            },
            'data_sources': {
                'crm': 'salesforce_connector',
                'erp': 'sap_connector', 
                'warehouse': 'snowflake_connector',
                'logs': 'elasticsearch_connector'
            },
            'entities': [
                'Customer360',
                'Product360', 
                'Order360',
                'Campaign360'
            ]
        }
        
    async def setup_customer_360_view(self):
        """設定客戶360度視圖"""
        return {
            'entity': 'Customer',
            'data_sources': [
                'CRM.customers',
                'ERP.customer_accounts', 
                'Web.user_profiles',
                'Support.tickets',
                'Marketing.campaigns'
            ],
            'real_time_sync': True,
            'privacy_controls': 'GDPR_compliant'
        }
```

**台灣企業應用案例：**
某大型電信公司使用 K2view 整合客戶資料：
```
整合前：查詢客戶資料需要登入 5 個不同系統
整合後：透過 AI 一次性獲得客戶完整視圖
效果：客服處理時間從 15 分鐘縮短到 2 分鐘
```

### 2. Vectara RAG Server：語義搜尋專家

**核心優勢：**
- **零設定 RAG**：開箱即用的檢索增強生成
- **多語言支援**：包含繁體中文最佳化
- **即時索引**：資料更新立即反映在搜尋結果

**實戰部署：**
```python
class VectaraRAGImplementation:
    def __init__(self):
        self.vectara_config = {
            'customer_id': '${VECTARA_CUSTOMER_ID}',
            'corpus_id': '${VECTARA_CORPUS_ID}',
            'api_key': '${VECTARA_API_KEY}',
            'language': 'zh-TW'  # 繁體中文最佳化
        }
        
    async def setup_knowledge_base(self, documents):
        """建立知識庫"""
        
        knowledge_base = {
            'company_policies': {
                'documents': ['employee_handbook.pdf', 'it_policy.docx'],
                'language': 'zh-TW',
                'chunk_size': 512,
                'overlap': 50
            },
            'technical_docs': {
                'documents': ['api_documentation/', 'system_manuals/'],
                'language': 'en',
                'metadata': {'department': 'engineering'}
            },
            'customer_faqs': {
                'documents': ['faq_database.json'],
                'language': 'zh-TW',
                'auto_update': True
            }
        }
        
        return await self._index_documents(knowledge_base)
        
    async def semantic_search(self, query, context=None):
        """語義搜尋實現"""
        
        search_params = {
            'query': query,
            'num_results': 10,
            'language': 'zh-TW',
            'filters': context or {},
            'rerank': True,
            'highlight': True
        }
        
        results = await self._execute_search(search_params)
        return self._format_results(results)
```

**實際應用場景：**
台灣某大型法律事務所的知識管理系統：
```
場景：律師詢問「勞基法關於加班費的最新規定」
過程：
1. Vectara 搜尋相關法條和判例
2. AI 整合最新修法內容  
3. 產生包含引用來源的專業回答
效果：法律研究時間從 3 小時縮短到 15 分鐘
```

## 開發者工具與整合類

### 3. GitHub MCP Server：開發協作神器

**官方品質保證：**
- Anthropic 官方維護
- 完整的 GitHub API 支援
- 企業級安全標準

**完整功能清單：**
```python
class GitHubMCPCapabilities:
    def __init__(self):
        self.tools = {
            'repository_management': [
                'create_repository',
                'list_repositories', 
                'get_repository_info',
                'update_repository_settings'
            ],
            'issue_tracking': [
                'create_issue',
                'list_issues',
                'update_issue',
                'close_issue',
                'add_issue_comment'
            ],
            'pull_requests': [
                'create_pull_request',
                'list_pull_requests',
                'review_pull_request',
                'merge_pull_request'
            ],
            'code_analysis': [
                'get_file_content',
                'search_code',
                'get_commit_history',
                'analyze_code_changes'
            ],
            'project_management': [
                'manage_projects',
                'track_milestones',
                'assign_tasks',
                'generate_reports'
            ]
        }
```

**企業級配置：**
```yaml
# GitHub MCP 企業配置
github_mcp:
  authentication:
    method: "github_app"
    app_id: "${GITHUB_APP_ID}"
    private_key_path: "/secrets/github-app-key.pem"
    installation_id: "${GITHUB_INSTALLATION_ID}"
  
  permissions:
    repositories: "write"
    issues: "write" 
    pull_requests: "write"
    contents: "read"
    metadata: "read"
    
  security:
    rate_limiting: true
    audit_logging: true
    ip_whitelist: ["10.0.0.0/8", "172.16.0.0/12"]
    
  enterprise_features:
    saml_sso: true
    advanced_security: true
    dependency_insights: true
```

**實戰用例：自動化 PR 審查**
```python
async def automated_pr_review(pr_number, repository):
    """自動化 PR 審查流程"""
    
    # 1. 獲取 PR 詳情
    pr_info = await github_mcp.get_pull_request(repository, pr_number)
    
    # 2. 分析代碼變更
    code_changes = await github_mcp.get_pr_changes(repository, pr_number)
    
    # 3. 執行安全掃描
    security_scan = await security_scanner.scan_changes(code_changes)
    
    # 4. 檢查測試覆蓋率
    test_coverage = await coverage_analyzer.analyze(code_changes)
    
    # 5. 生成 AI 審查意見
    review_comments = await ai_reviewer.generate_review(
        code_changes, 
        security_scan, 
        test_coverage
    )
    
    # 6. 自動提交審查
    if security_scan.passed and test_coverage > 80:
        await github_mcp.approve_pull_request(repository, pr_number)
    else:
        await github_mcp.request_changes(repository, pr_number, review_comments)
```

### 4. Zapier MCP Server：自動化工作流程大師

**6000+ 應用整合：**
Zapier MCP 讓 AI 能夠操作超過 6000 個不同的應用程式，實現真正的工作流程自動化。

**核心優勢：**
- **即插即用**：無需 API 文檔，AI 自動理解應用功能
- **即時同步**：資料變更立即觸發相關動作
- **錯誤處理**：自動重試和異常通知

**企業工作流程範例：**
```python
class ZapierWorkflowAutomation:
    def __init__(self):
        self.zapier_mcp = ZapierMCPClient()
        
    async def setup_customer_onboarding_flow(self):
        """客戶入職自動化流程"""
        
        workflow = {
            'trigger': {
                'app': 'salesforce',
                'event': 'new_customer_created',
                'conditions': {'status': 'active', 'plan': 'enterprise'}
            },
            'actions': [
                {
                    'app': 'slack',
                    'action': 'send_channel_message',
                    'channel': '#customer-success',
                    'message': '新企業客戶 {{customer.name}} 已註冊'
                },
                {
                    'app': 'notion',
                    'action': 'create_page',
                    'database': 'customer_onboarding',
                    'properties': {
                        'customer_name': '{{customer.name}}',
                        'assigned_csm': '{{auto_assign_csm}}',
                        'onboarding_status': 'started'
                    }
                },
                {
                    'app': 'gmail',
                    'action': 'send_email',
                    'to': '{{customer.email}}',
                    'template': 'welcome_enterprise_customer',
                    'attachments': ['onboarding_guide.pdf']
                },
                {
                    'app': 'calendar',
                    'action': 'schedule_meeting',
                    'attendees': ['{{customer.email}}', '{{assigned_csm.email}}'],
                    'title': '客戶入職歡迎會議',
                    'duration': 60,
                    'time_preference': 'next_business_day'
                }
            ]
        }
        
        return await self.zapier_mcp.create_workflow(workflow)
```

**台灣中小企業應用：**
某台灣製造業公司的訂單處理自動化：
```
1. 客戶下單 (Shopify) → 
2. 檢查庫存 (ERP系統) → 
3. 安排生產 (MES系統) → 
4. 通知物流 (物流API) → 
5. 發送確認信 (Gmail) → 
6. 更新財務 (會計軟體)

結果：訂單處理時間從 2 天縮短到 2 小時
```

## 企業系統整合類

### 5. Salesforce MCP Server：CRM 資料智慧化

**深度 CRM 整合：**
```python
class SalesforceMCPIntegration:
    def __init__(self):
        self.sf_config = {
            'instance_url': 'https://company.my.salesforce.com',
            'client_id': '${SALESFORCE_CLIENT_ID}',
            'client_secret': '${SALESFORCE_CLIENT_SECRET}',
            'username': '${SALESFORCE_USERNAME}',
            'password': '${SALESFORCE_PASSWORD}',
            'security_token': '${SALESFORCE_SECURITY_TOKEN}'
        }
        
    async def ai_powered_lead_scoring(self, lead_id):
        """AI 驅動的潛在客戶評分"""
        
        # 獲取潛在客戶資料
        lead_data = await self.get_lead(lead_id)
        
        # 獲取相關的歷史資料
        similar_leads = await self.find_similar_leads(lead_data)
        conversion_patterns = await self.analyze_conversion_patterns(similar_leads)
        
        # AI 分析和評分
        ai_score = await self.ai_analyzer.score_lead({
            'lead_data': lead_data,
            'historical_patterns': conversion_patterns,
            'market_conditions': await self.get_market_data(),
            'company_priorities': await self.get_sales_priorities()
        })
        
        # 更新 Salesforce 記錄
        await self.update_lead(lead_id, {
            'AI_Score__c': ai_score.score,
            'AI_Confidence__c': ai_score.confidence,
            'Recommended_Actions__c': ai_score.recommended_actions,
            'Next_Best_Action__c': ai_score.next_best_action
        })
        
        return ai_score
```

**實際應用案例：**
台灣某 B2B 軟體公司的銷售智能化：
```
場景：銷售人員詢問「這週應該優先跟進哪些潛在客戶？」

AI 分析過程：
1. 掃描所有活躍潛在客戶
2. 分析歷史成交模式
3. 考慮當前市場環境
4. 評估客戶參與度
5. 計算成交機率

輸出結果：
- 高優先級客戶清單（成交機率>70%）
- 每個客戶的建議跟進策略
- 預期成交時間和金額
- 需要注意的風險因素
```

### 6. Slack MCP Server：團隊協作增強器

**智慧團隊協作：**
```python
class SlackMCPWorkspace:
    def __init__(self):
        self.slack_config = {
            'bot_token': '${SLACK_BOT_TOKEN}',
            'app_token': '${SLACK_APP_TOKEN}',
            'signing_secret': '${SLACK_SIGNING_SECRET}'
        }
        
    async def intelligent_meeting_scheduler(self, request):
        """智慧會議安排"""
        
        # 解析會議需求
        meeting_request = await self.parse_meeting_request(request)
        
        # 檢查參與者行事曆
        attendees_availability = await self.check_calendars(
            meeting_request.attendees
        )
        
        # 分析最佳會議時間
        optimal_times = await self.ai_scheduler.find_optimal_slots(
            attendees_availability,
            meeting_request.duration,
            meeting_request.preferences
        )
        
        # 在 Slack 中提出建議
        message = await self.format_meeting_suggestions(optimal_times)
        await self.send_message(meeting_request.channel, message)
        
        # 等待確認並自動排程
        return await self.wait_for_confirmation_and_schedule()
        
    async def project_status_ai_summary(self, project_channel):
        """專案狀態 AI 摘要"""
        
        # 收集專案相關訊息
        recent_messages = await self.get_channel_messages(
            project_channel, 
            days=7
        )
        
        # 分析任務進度
        task_updates = await self.extract_task_updates(recent_messages)
        
        # 識別阻礙和風險
        blockers = await self.identify_blockers(recent_messages)
        
        # 生成 AI 摘要
        summary = await self.ai_summarizer.generate_project_summary({
            'task_updates': task_updates,
            'blockers': blockers,
            'team_sentiment': await self.analyze_team_sentiment(recent_messages),
            'timeline_status': await self.check_timeline_status(project_channel)
        })
        
        return summary
```

### 7. Notion MCP Server：知識管理中樞

**智慧知識庫：**
```python
class NotionKnowledgeBase:
    def __init__(self):
        self.notion_config = {
            'api_key': '${NOTION_API_KEY}',
            'version': '2022-06-28',
            'databases': {
                'company_wiki': '${WIKI_DATABASE_ID}',
                'meeting_notes': '${MEETINGS_DATABASE_ID}',
                'project_docs': '${PROJECTS_DATABASE_ID}',
                'procedures': '${PROCEDURES_DATABASE_ID}'
            }
        }
        
    async def ai_powered_documentation(self, topic, context=None):
        """AI 驅動的文件生成"""
        
        # 搜尋相關已有文件
        existing_docs = await self.search_related_documents(topic)
        
        # 分析文件結構模式
        doc_patterns = await self.analyze_documentation_patterns(existing_docs)
        
        # 獲取相關的專案上下文
        project_context = context or await self.get_relevant_project_context(topic)
        
        # AI 生成文件大綱
        outline = await self.ai_writer.generate_outline({
            'topic': topic,
            'existing_docs': existing_docs,
            'patterns': doc_patterns,
            'context': project_context
        })
        
        # 創建 Notion 頁面結構
        page = await self.create_structured_page(outline)
        
        # AI 填充內容
        content = await self.ai_writer.generate_content(outline, project_context)
        await self.populate_page_content(page.id, content)
        
        return page
        
    async def smart_meeting_notes_processing(self, meeting_recording):
        """智慧會議記錄處理"""
        
        # 轉錄會議音檔
        transcript = await self.transcribe_meeting(meeting_recording)
        
        # AI 分析會議內容
        analysis = await self.ai_analyzer.analyze_meeting({
            'transcript': transcript,
            'participants': await self.identify_participants(transcript),
            'agenda': await self.extract_agenda_items(transcript)
        })
        
        # 自動建立 Notion 會議記錄
        meeting_page = await self.create_meeting_notes_page({
            'title': analysis.meeting_title,
            'date': analysis.meeting_date,
            'participants': analysis.participants,
            'summary': analysis.executive_summary,
            'action_items': analysis.action_items,
            'decisions': analysis.decisions_made,
            'next_steps': analysis.next_steps
        })
        
        # 自動分派行動項目
        for action_item in analysis.action_items:
            await self.assign_action_item(action_item)
            
        return meeting_page
```

## 雲端基礎設施類

### 8. Supabase MCP Server：現代化後端服務

**無伺服器 Postgres 強化：**
```python
class SupabaseMCPStack:
    def __init__(self):
        self.supabase_config = {
            'url': '${SUPABASE_URL}',
            'anon_key': '${SUPABASE_ANON_KEY}',
            'service_role_key': '${SUPABASE_SERVICE_ROLE_KEY}',
            'features': {
                'auth': True,
                'realtime': True,
                'storage': True,
                'edge_functions': True
            }
        }
        
    async def ai_powered_database_optimization(self):
        """AI 驅動的資料庫最佳化"""
        
        # 分析查詢效能
        query_performance = await self.analyze_query_performance()
        
        # AI 建議索引最佳化
        index_recommendations = await self.ai_optimizer.suggest_indexes(
            query_performance
        )
        
        # 自動套用安全的最佳化
        safe_optimizations = [
            opt for opt in index_recommendations 
            if opt.risk_level == 'low'
        ]
        
        for optimization in safe_optimizations:
            await self.apply_optimization(optimization)
            
        return {
            'applied_optimizations': safe_optimizations,
            'pending_review': [
                opt for opt in index_recommendations 
                if opt.risk_level in ['medium', 'high']
            ],
            'performance_improvement': await self.measure_improvement()
        }
        
    async def realtime_ai_analytics(self, table_name):
        """即時 AI 分析"""
        
        # 設定即時資料流
        realtime_stream = await self.setup_realtime_subscription(table_name)
        
        # AI 分析資料流
        async for data_change in realtime_stream:
            analysis = await self.ai_analyzer.analyze_change({
                'change_type': data_change.eventType,
                'new_data': data_change.new,
                'old_data': data_change.old,
                'table': table_name
            })
            
            # 觸發相關動作
            if analysis.requires_action:
                await self.trigger_automated_response(analysis)
                
        return realtime_stream
```

### 9. Cloudflare MCP Server：全球邊緣運算

**邊緣 AI 部署：**
```python
class CloudflareMCPEdge:
    def __init__(self):
        self.cf_config = {
            'account_id': '${CLOUDFLARE_ACCOUNT_ID}',
            'api_token': '${CLOUDFLARE_API_TOKEN}',
            'zone_id': '${CLOUDFLARE_ZONE_ID}',
            'worker_subdomain': '${WORKER_SUBDOMAIN}'
        }
        
    async def deploy_global_ai_endpoints(self):
        """部署全球 AI 端點"""
        
        edge_locations = [
            'taipei', 'singapore', 'tokyo', 'seoul',
            'sydney', 'los-angeles', 'london', 'frankfurt'
        ]
        
        deployment_results = {}
        
        for location in edge_locations:
            # 部署 MCP Worker 到邊緣位置
            worker = await self.deploy_mcp_worker(location, {
                'model': 'claude-3-haiku',  # 快速回應模型
                'max_tokens': 4000,
                'timeout': 30000,  # 30 秒
                'memory': '512MB'
            })
            
            # 設定智能路由
            await self.configure_smart_routing(location, worker.url)
            
            deployment_results[location] = {
                'worker_url': worker.url,
                'latency': await self.test_latency(worker.url),
                'status': 'active'
            }
            
        return deployment_results
        
    async def intelligent_cache_management(self):
        """智能快取管理"""
        
        # AI 分析快取效率
        cache_analytics = await self.analyze_cache_performance()
        
        # 預測熱門內容
        predicted_hot_content = await self.ai_predictor.predict_popular_content(
            cache_analytics.access_patterns
        )
        
        # 主動預載內容
        for content in predicted_hot_content:
            await self.preload_to_edge_cache(content)
            
        # 清理冷內容
        cold_content = await self.identify_cold_content()
        for content in cold_content:
            await self.purge_from_cache(content)
            
        return {
            'cache_hit_rate_improvement': cache_analytics.improvement,
            'preloaded_content': len(predicted_hot_content),
            'purged_content': len(cold_content)
        }
```

## 專業領域應用類

### 10. Google Calendar MCP：智慧行程管理

**AI 行程最佳化：**
```python
class GoogleCalendarMCPAI:
    def __init__(self):
        self.calendar_config = {
            'credentials_path': '/secrets/google-calendar-credentials.json',
            'scopes': [
                'https://www.googleapis.com/auth/calendar',
                'https://www.googleapis.com/auth/calendar.events'
            ]
        }
        
    async def ai_schedule_optimization(self, user_id, time_period='week'):
        """AI 行程最佳化"""
        
        # 獲取當前行程
        current_schedule = await self.get_user_schedule(user_id, time_period)
        
        # 分析工作模式
        work_patterns = await self.analyze_work_patterns(user_id, current_schedule)
        
        # AI 建議最佳化
        optimizations = await self.ai_scheduler.optimize_schedule({
            'current_schedule': current_schedule,
            'work_patterns': work_patterns,
            'user_preferences': await self.get_user_preferences(user_id),
            'team_availability': await self.get_team_availability(user_id)
        })
        
        # 生成最佳化建議
        suggestions = {
            'meeting_consolidation': optimizations.consolidate_meetings,
            'focus_time_blocks': optimizations.focus_blocks,
            'break_recommendations': optimizations.break_suggestions,
            'commute_optimization': optimizations.location_based_scheduling
        }
        
        return suggestions
        
    async def intelligent_meeting_conflict_resolution(self, conflict_event):
        """智慧會議衝突解決"""
        
        # 分析衝突會議的重要性
        conflict_analysis = await self.analyze_meeting_importance(conflict_event)
        
        # 尋找替代時間
        alternative_slots = await self.find_alternative_slots(
            conflict_event.attendees,
            conflict_event.duration,
            conflict_event.constraints
        )
        
        # AI 推薦最佳解決方案
        resolution = await self.ai_resolver.recommend_resolution({
            'conflict_details': conflict_analysis,
            'alternatives': alternative_slots,
            'business_impact': await self.assess_business_impact(conflict_event)
        })
        
        return resolution
```

## 實戰部署流程

### 快速開始：本地開發環境

**一鍵部署腳本：**
```bash
#!/bin/bash
# MCP 伺服器快速部署腳本

echo "🚀 開始部署 MCP 開發環境..."

# 1. 安裝依賴
npm install -g @modelcontextprotocol/cli

# 2. 建立專案目錄
mkdir mcp-servers && cd mcp-servers

# 3. 初始化常用伺服器
echo "📦 安裝 GitHub MCP Server..."
git clone https://github.com/anthropic/mcp-server-github.git
cd mcp-server-github && npm install && cd ..

echo "📦 安裝 Filesystem MCP Server..."
git clone https://github.com/anthropic/mcp-server-filesystem.git
cd mcp-server-filesystem && npm install && cd ..

echo "📦 安裝 SQLite MCP Server..."
git clone https://github.com/anthropic/mcp-server-sqlite.git
cd mcp-server-sqlite && npm install && cd ..

# 4. 建立配置檔案
cat > claude_desktop_config.json << 'EOF'
{
  "mcpServers": {
    "github": {
      "command": "node",
      "args": ["./mcp-servers/mcp-server-github/dist/index.js"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "your_token_here"
      }
    },
    "filesystem": {
      "command": "node", 
      "args": ["./mcp-servers/mcp-server-filesystem/dist/index.js"],
      "env": {
        "ALLOWED_DIRECTORIES": "/Users/username/Documents"
      }
    },
    "sqlite": {
      "command": "node",
      "args": ["./mcp-servers/mcp-server-sqlite/dist/index.js"],
      "env": {
        "DB_PATH": "./database.sqlite"
      }
    }
  }
}
EOF

echo "✅ MCP 開發環境部署完成！"
echo "請將 claude_desktop_config.json 複製到正確位置："
echo "macOS: ~/Library/Application Support/Claude/"
echo "Windows: %APPDATA%/Claude/"
```

### 企業級部署：Docker 容器化

**Docker Compose 配置：**
```yaml
# docker-compose.yml
version: '3.8'

services:
  mcp-gateway:
    build: ./mcp-gateway
    ports:
      - "8080:8080"
    environment:
      - JWT_SECRET=${JWT_SECRET}
      - OAUTH_CLIENT_ID=${OAUTH_CLIENT_ID}
      - OAUTH_CLIENT_SECRET=${OAUTH_CLIENT_SECRET}
    depends_on:
      - redis
      - postgres
    networks:
      - mcp-network

  github-mcp:
    build: ./servers/github-mcp
    environment:
      - GITHUB_APP_ID=${GITHUB_APP_ID}
      - GITHUB_PRIVATE_KEY=${GITHUB_PRIVATE_KEY}
      - GITHUB_WEBHOOK_SECRET=${GITHUB_WEBHOOK_SECRET}
    networks:
      - mcp-network
    volumes:
      - ./secrets:/app/secrets:ro

  salesforce-mcp:
    build: ./servers/salesforce-mcp
    environment:
      - SALESFORCE_CLIENT_ID=${SALESFORCE_CLIENT_ID}
      - SALESFORCE_CLIENT_SECRET=${SALESFORCE_CLIENT_SECRET}
      - SALESFORCE_USERNAME=${SALESFORCE_USERNAME}
      - SALESFORCE_PASSWORD=${SALESFORCE_PASSWORD}
    networks:
      - mcp-network

  slack-mcp:
    build: ./servers/slack-mcp
    environment:
      - SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN}
      - SLACK_APP_TOKEN=${SLACK_APP_TOKEN}
      - SLACK_SIGNING_SECRET=${SLACK_SIGNING_SECRET}
    networks:
      - mcp-network

  redis:
    image: redis:7-alpine
    volumes:
      - redis_data:/data
    networks:
      - mcp-network

  postgres:
    image: postgres:15
    environment:
      - POSTGRES_DB=mcp_gateway
      - POSTGRES_USER=mcp_user
      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
    volumes:
      - postgres_data:/var/lib/postgresql/data
    networks:
      - mcp-network

  prometheus:
    image: prom/prometheus
    ports:
      - "9090:9090"
    volumes:
      - ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml
    networks:
      - mcp-network

  grafana:
    image: grafana/grafana
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_PASSWORD}
    volumes:
      - grafana_data:/var/lib/grafana
    networks:
      - mcp-network

volumes:
  redis_data:
  postgres_data:
  grafana_data:

networks:
  mcp-network:
    driver: bridge
```

### 監控與維護

**完整監控堆疊：**
```python
class MCPMonitoringStack:
    def __init__(self):
        self.monitoring_config = {
            'metrics': {
                'prometheus': 'http://prometheus:9090',
                'grafana': 'http://grafana:3000',
                'alertmanager': 'http://alertmanager:9093'
            },
            'logging': {
                'elasticsearch': 'http://elasticsearch:9200',
                'kibana': 'http://kibana:5601',
                'logstash': 'http://logstash:5044'
            },
            'tracing': {
                'jaeger': 'http://jaeger:16686',
                'zipkin': 'http://zipkin:9411'
            }
        }
        
    async def setup_monitoring_dashboard(self):
        """設定監控儀表板"""
        
        dashboard_panels = {
            'server_health': {
                'metrics': ['server_uptime', 'response_time', 'error_rate'],
                'alert_thresholds': {
                    'response_time': '>1000ms',
                    'error_rate': '>5%',
                    'uptime': '<99%'
                }
            },
            'resource_usage': {
                'metrics': ['cpu_usage', 'memory_usage', 'disk_usage'],
                'alert_thresholds': {
                    'cpu_usage': '>80%',
                    'memory_usage': '>85%',
                    'disk_usage': '>90%'
                }
            },
            'business_metrics': {
                'metrics': ['requests_per_minute', 'active_users', 'tool_usage'],
                'visualization': 'time_series'
            }
        }
        
        return await self.create_grafana_dashboard(dashboard_panels)
```

## 小結：選擇與實施策略

選擇正確的 MCP 伺服器組合是成功的關鍵：

**決策框架：**
1. **業務需求優先**：先確定要解決的問題
2. **技術適配性**：評估與現有系統的整合難度  
3. **維護能力**：考慮團隊的技術能力
4. **成本效益**：平衡功能與預算
5. **未來擴展**：預留成長空間

**台灣企業建議組合：**
- **新創公司**：GitHub + Notion + Zapier + Supabase
- **中小企業**：GitHub + Salesforce + Slack + Google Calendar  
- **大型企業**：K2view + GitHub + Salesforce + Slack + 客製化 MCP

記住，MCP 的威力在於組合效應。單一伺服器可能只是工具，但多個伺服器的智能協作才能真正釋放 AI 的潛力。

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**下一頁：** [第四章：突破性應用與創新用例](/s/mcp-chapter-4)