Claude Code MCP协议深度解析：构建AI驱动的全能开发生态系统

async def intelligent_bug_lifecycle_management(issue_number: int):
    """智能Bug全生命周期管理工作流"""
    
    workflow_id = f"bug-analysis-{issue_number}-{int(time.time())}"
    print(f"🔍 启动智能Bug分析工作流: {workflow_id}")
    
    # 第1步：多维度信息收集
    print("📊 第1步：多维度数据收集...")
    
    # GitHub Issue详细信息
    github_issue = await mcp.github_enterprise.get_issue_with_context(
        owner="company-org", 
        repo="main-product", 
        issue_number=issue_number,
        include_related_prs=True,
        include_commit_history=True,
        include_discussion_thread=True
    )
    
    # 提取错误关键词并搜索相关Sentry错误
    error_keywords = await extract_intelligent_keywords(github_issue.body)
    sentry_errors = await mcp.sentry_enterprise.search_errors(
        query=error_keywords,
        time_range="14d",  # 最近14天
        include_performance_data=True,
        include_user_impact=True,
        limit=20
    )
    
    # 检查是否有相关的Linear任务
    linear_issues = await mcp.linear_advanced.search_issues(
        query=f"#{issue_number} OR {error_keywords[:3]}",  # 前3个关键词
        include_completed=True,
        include_archived=False
    )
    
    # 获取相关代码文件的最近变更
    if sentry_errors:
        affected_files = await extract_affected_files(sentry_errors[0].stack_trace)
        recent_commits = await mcp.github_enterprise.get_file_history(
            files=affected_files,
            since="14 days ago",
            include_pr_context=True
        )
    
    print(f"✅ 数据收集完成:")
    print(f"   - GitHub Issue: {github_issue.title}")
    print(f"   - 关联Sentry错误: {len(sentry_errors)}个")
    print(f"   - 现有Linear任务: {len(linear_issues)}个")
    print(f"   - 相关代码提交: {len(recent_commits) if 'recent_commits' in locals() else 0}个")
    
    # 第2步：智能影响面分析
    print("🎯 第2步：智能影响面分析...")
    
    impact_analysis = await analyze_bug_impact(
        github_issue=github_issue,
        sentry_errors=sentry_errors,
        recent_commits=recent_commits if 'recent_commits' in locals() else [],
        user_feedback=await collect_user_feedback(issue_number)
    )
    
    # 业务影响评估
    business_impact = await assess_business_impact(
        error_frequency=sentry_errors[0].count if sentry_errors else 0,
        affected_users=impact_analysis.unique_users,
        revenue_impact=impact_analysis.estimated_revenue_loss,
        feature_criticality=impact_analysis.feature_importance
    )
    
    print(f"📈 影响面分析结果:")
    print(f"   - 影响用户数: {impact_analysis.unique_users:,}")
    print(f"   - 错误发生频率: {sentry_errors[0].count if sentry_errors else 0}/天")
    print(f"   - 业务优先级: {business_impact.priority}")
    print(f"   - 预估收入影响: ${business_impact.revenue_impact:,.2f}")
    
    # 第3步：AI驱动的根因分析
    print("🧠 第3步：AI驱动的根因分析...")
    
    root_cause_analysis = await perform_ai_root_cause_analysis({
        "issue_description": github_issue.body,
        "error_patterns": [error.exception_type for error in sentry_errors[:5]],
        "stack_traces": [error.stack_trace for error in sentry_errors[:3]],
        "recent_changes": [commit.message for commit in recent_commits[:10]] if 'recent_commits' in locals() else [],
        "system_context": await get_system_context_for_analysis()
    })
    
    print(f"🔬 根因分析结果:")
    print(f"   - 可能原因: {root_cause_analysis.likely_causes[0]}")
    print(f"   - 置信度: {root_cause_analysis.confidence:.1%}")
    print(f"   - 影响范围: {root_cause_analysis.scope}")
    
    # 第4步：生成智能修复方案
    print("💡 第4步：生成智能修复方案...")
    
    fix_recommendations = await generate_intelligent_fix_plan({
        "root_cause": root_cause_analysis,
        "codebase_context": await analyze_codebase_patterns(affected_files if 'affected_files' in locals() else []),
        "team_expertise": await get_team_expertise_mapping(),
        "similar_fixes": await find_similar_historical_fixes(error_keywords)
    })
    
    print(f"🛠️ 修复方案生成:")
    for i, fix in enumerate(fix_recommendations.solutions, 1):
        print(f"   方案{i}: {fix.title} (难度: {fix.complexity}, 预估: {fix.estimated_hours}小时)")
    
    # 第5步：自动创建Linear任务
    print("📋 第5步：创建Linear跟踪任务...")
    
    linear_task = await mcp.linear_advanced.create_comprehensive_issue(
        title=f"🐛 修复: {github_issue.title}",
        description=format_linear_description({
            "github_issue": github_issue,
            "impact_analysis": impact_analysis,
            "root_cause": root_cause_analysis,
            "fix_recommendations": fix_recommendations,
            "sentry_links": [error.permalink for error in sentry_errors[:3]]
        }),
        priority=map_business_priority_to_linear(business_impact.priority),
        labels=["bug", "customer-impact", f"github-{issue_number}"],
        team_id=determine_optimal_team(root_cause_analysis.affected_systems),
        assignee=recommend_best_assignee(fix_recommendations.required_expertise),
        project_id=await get_current_sprint_project(),
        estimate=calculate_story_points(fix_recommendations.solutions[0].estimated_hours)
    )
    
    # 第6步：GitHub Issue更新
    print("🔗 第6步：更新GitHub Issue...")
    
    github_comment = format_analysis_comment({
        "analysis_id": workflow_id,
        "impact_summary": impact_analysis,
        "root_cause_summary": root_cause_analysis,
        "recommended_solution": fix_recommendations.solutions[0],
        "linear_task_link": linear_task.url,
        "sentry_errors_summary": sentry_errors[:3] if sentry_errors else []
    })
    
    await mcp.github_enterprise.add_comprehensive_comment(
        owner="company-org",
        repo="main-product",
        issue_number=issue_number,
        body=github_comment,
        metadata={
            "analysis_workflow_id": workflow_id,
            "linear_task_id": linear_task.id,
            "priority": business_impact.priority
        }
    )
    
    # 第7步：团队通知和协调
    print("📢 第7步：智能团队通知...")
    
    # 根据严重程度选择通知渠道和方式
    if business_impact.priority in ["urgent", "high"]:
        # 高优先级：多渠道通知
        await mcp.slack_enterprise.send_priority_alert(
            channel="#engineering-urgent",
            title="🚨 高优先级Bug需要立即处理",
            content=format_urgent_bug_alert({
                "issue": github_issue,
                "impact": impact_analysis,
                "linear_task": linear_task,
                "recommended_assignee": linear_task.assignee
            }),
            mention_users=[linear_task.assignee.slack_id],
            require_acknowledgment=True
        )
        
        # PagerDuty告警（如果是critical）
        if business_impact.priority == "urgent":
            await trigger_pagerduty_incident(
                title=f"Critical Bug: {github_issue.title}",
                description=root_cause_analysis.summary,
                urgency="high",
                assignee=linear_task.assignee.email
            )
    else:
        # 普通优先级：标准通知
        await mcp.slack_enterprise.send_message(
            channel="#bug-triage",
            blocks=format_bug_analysis_blocks({
                "github_issue": github_issue,
                "analysis": impact_analysis,
                "linear_task": linear_task,
                "next_steps": fix_recommendations.solutions[0].action_items
            })
        )
    
    # 第8步：监控和跟踪设置
    print("📊 第8步：设置监控和跟踪...")
    
    # 创建Sentry规则，监控修复后的效果
    if sentry_errors:
        await mcp.sentry_enterprise.create_issue_alert_rule(
            name=f"Monitor Fix for Issue #{issue_number}",
            conditions=[
                {"filter_type": "error.type", "value": sentry_errors[0].exception_type},
                {"filter_type": "frequency", "value": "increased"}
            ],
            actions=[
                {"type": "slack", "channel": "#bug-monitoring"},
                {"type": "linear", "project_id": linear_task.project.id}
            ],
            environment=["production"],
            duration_hours=168  # 监控一周
        )
    
    # 在Linear中设置自动提醒
    await mcp.linear_advanced.create_issue_reminder(
        issue_id=linear_task.id,
        reminder_type="progress_check",
        schedule="daily",
        conditions=["not_updated_24h", "no_progress_3d"],
        notify_assignee=True,
        escalate_after_days=5
    )
    
    # 第9步：生成执行报告
    print("📋 第9步：生成完整分析报告...")
    
    comprehensive_report = {
        "workflow_id": workflow_id,
        "github_issue": {
            "number": issue_number,
            "title": github_issue.title,
            "url": github_issue.html_url
        },
        "impact_analysis": {
            "affected_users": impact_analysis.unique_users,
            "business_priority": business_impact.priority,
            "revenue_impact": business_impact.revenue_impact
        },
        "technical_analysis": {
            "root_cause": root_cause_analysis.likely_causes[0],
            "confidence": root_cause_analysis.confidence,
            "affected_systems": root_cause_analysis.affected_systems
        },
        "action_plan": {
            "linear_task_id": linear_task.id,
            "linear_task_url": linear_task.url,
            "assigned_to": linear_task.assignee.name,
            "estimated_completion": fix_recommendations.solutions[0].estimated_completion_date
        },
        "monitoring": {
            "sentry_alert_rule": f"Monitor Fix for Issue #{issue_number}",
            "linear_reminders": "Daily progress check enabled"
        }
    }
    
    # 保存到内部知识库
    await save_analysis_to_knowledge_base(comprehensive_report)
    
    print(f"""
🎉 智能Bug分析工作流完成！
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📋 GitHub Issue: #{issue_number} - {github_issue.title}
🎯 Linear任务: {linear_task.identifier} - {linear_task.url}
👤 分配给: {linear_task.assignee.name}
⚡ 优先级: {business_impact.priority}
📈 影响用户: {impact_analysis.unique_users:,}
💰 收入影响: ${business_impact.revenue_impact:,.2f}
🔬 根本原因: {root_cause_analysis.likely_causes[0]}
⏱️ 预估修复: {fix_recommendations.solutions[0].estimated_hours}小时
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
    """)
    
    return comprehensive_report

# 使用示例
bug_analysis = await intelligent_bug_lifecycle_management(456)

async def comprehensive_project_health_monitoring():
    """全方位项目健康监控和报告生成"""
    
    print("🏥 启动项目健康监控分析...")
    
    # 第1步：多维度数据收集
    health_metrics = {}
    
    # 代码质量指标
    code_quality = await mcp.github_enterprise.get_code_quality_metrics(
        repositories=["main-product", "mobile-app", "admin-panel"],
        metrics=["test_coverage", "code_duplication", "security_issues", "technical_debt"]
    )
    
    # 部署和稳定性指标
    deployment_health = await collect_deployment_metrics()
    system_stability = await mcp.sentry_enterprise.get_system_health_overview(
        time_range="7d",
        include_performance=True,
        include_user_satisfaction=True
    )
    
    # 团队效率指标
    team_velocity = await mcp.linear_advanced.get_team_velocity_analysis(
        teams=["engineering", "frontend", "backend", "mobile"],
        time_range="30d",
        include_burndown=True
    )
    
    # 业务指标
    business_metrics = await collect_business_health_indicators()
    
    print(f"📊 健康指标收集完成:")
    print(f"   - 代码质量评分: {code_quality.overall_score}/100")
    print(f"   - 系统稳定性: {system_stability.uptime_percentage:.2%}")
    print(f"   - 团队速度: {team_velocity.average_velocity:.1f} 点/周")
    print(f"   - 用户满意度: {business_metrics.user_satisfaction_score:.1f}/5.0")
    
    # 第2步：智能健康评估
    health_assessment = await perform_intelligent_health_assessment({
        "code_quality": code_quality,
        "system_stability": system_stability,
        "team_performance": team_velocity,
        "business_metrics": business_metrics,
        "historical_trends": await get_historical_health_trends()
    })
    
    # 第3步：生成改进建议
    improvement_recommendations = await generate_health_improvement_plan(health_assessment)
    
    # 第4步：自动创建改进任务
    if improvement_recommendations.urgent_actions:
        for action in improvement_recommendations.urgent_actions:
            linear_task = await mcp.linear_advanced.create_issue(
                title=f"🚨 项目健康改进: {action.title}",
                description=action.detailed_description,
                priority="High",
                labels=["project-health", "urgent"],
                team_id=action.responsible_team_id,
                assignee=action.suggested_assignee
            )
    
    # 第5步：发送健康报告
    await distribute_health_report(health_assessment, improvement_recommendations)
    
    return health_assessment

# 定期执行（每日早上8点）
schedule.every().day.at("08:00").do(comprehensive_project_health_monitoring)

async def enterprise_automated_release_pipeline(version: str, release_notes: str):
    """企业级自动化发布流水线"""
    
    pipeline_id = f"release-{version}-{int(time.time())}"
    print(f"🚀 启动企业级发布流水线: {version} (ID: {pipeline_id})")
    
    try:
        # 第1阶段：预发布质量门禁
        print("🔍 第1阶段：预发布质量门禁检查...")
        
        quality_gates = await execute_quality_gates_in_parallel([
            mcp.sonarqube.get_quality_gate_status(),
            mcp.security_scanner.run_comprehensive_security_scan(),
            mcp.performance_monitor.run_performance_benchmarks(),
            mcp.dependency_checker.audit_dependencies()
        ])
        
        if not all(gate.passed for gate in quality_gates):
            failed_gates = [gate.name for gate in quality_gates if not gate.passed]
            raise ReleaseBlockedException(f"质量门禁失败: {failed_gates}")
        
        # 第2阶段：自动化测试执行
        print("🧪 第2阶段：全面自动化测试...")
        
        test_results = await execute_comprehensive_test_suite([
            mcp.testing.run_unit_tests(),
            mcp.testing.run_integration_tests(),
            mcp.testing.run_e2e_tests(),
            mcp.testing.run_performance_tests(),
            mcp.testing.run_security_tests()
        ])
        
        if test_results.failure_count > 0:
            await handle_test_failures(test_results, pipeline_id)
            raise ReleaseBlockedException(f"测试失败: {test_results.failure_count}个")
        
        # 第3阶段：多环境部署协调
        print("🏗️ 第3阶段：多环境部署协调...")
        
        deployment_plan = await create_intelligent_deployment_plan(version, release_notes)
        
        # Staging环境部署
        staging_deployment = await mcp.k8s_staging.deploy_application(
            version=version,
            strategy="blue_green",
            health_checks=True,
            rollback_enabled=True
        )
        
        # 自动化smoke测试
        staging_validation = await mcp.testing.run_smoke_tests(
            environment="staging",
            base_url=staging_deployment.service_url
        )
        
        if not staging_validation.passed:
            await mcp.k8s_staging.rollback_deployment()
            raise DeploymentFailedException("Staging环境验证失败")
        
        # Production环境部署（需要审批）
        if await request_production_deployment_approval(deployment_plan):
            production_deployment = await mcp.k8s_production.deploy_application(
                version=version,
                strategy="canary",
                canary_percentage=10,
                monitoring_duration=1800  # 30分钟观察期
            )
            
            # 生产环境监控
            production_health = await monitor_production_deployment(
                deployment=production_deployment,
                duration_minutes=30
            )
            
            if production_health.is_healthy:
                await mcp.k8s_production.promote_canary_to_full()
                print("✅ 生产环境部署成功并已全量切换")
            else:
                await mcp.k8s_production.rollback_canary()
                raise DeploymentFailedException("生产环境健康检查失败")
        
        # 第4阶段：跨平台状态同步
        print("🔄 第4阶段：跨平台状态同步...")
        
        sync_tasks = await execute_platform_sync_tasks([
            # 更新GitHub Release
            mcp.github_enterprise.create_release(
                tag_name=f"v{version}",
                name=f"Release {version}",
                body=release_notes,
                assets=await build_release_assets()
            ),
            
            # 更新Linear项目状态
            mcp.linear_advanced.update_release_milestone(
                version=version,
                status="completed",
                deployed_at=datetime.now()
            ),
            
            # 关闭相关Issues
            mcp.linear_advanced.bulk_close_issues_for_release(version),
            
            # 发送团队通知
            mcp.slack_enterprise.send_release_announcement(
                channel="#releases",
                version=version,
                highlights=extract_release_highlights(release_notes),
                deployment_metrics=production_deployment.metrics
            ),
            
            # 更新文档
            mcp.confluence.update_release_documentation(
                version=version,
                release_data=deployment_plan
            )
        ])
        
        # 第5阶段：发布后监控和告警设置
        print("📊 第5阶段：发布后监控设置...")
        
        await setup_post_release_monitoring(version, [
            # 错误率监控
            mcp.datadog.create_error_rate_alert(
                name=f"Error Rate Alert - Release {version}",
                threshold=5.0,
                comparison="above",
                timeframe="10m"
            ),
            
            # 性能监控
            mcp.newrelic.create_performance_alert(
                name=f"Performance Regression - Release {version}",
                metric="response_time",
                threshold=2000,
                comparison="above"
            ),
            
            # 业务指标监控
            mcp.mixpanel.create_business_metric_alert(
                name=f"User Engagement - Release {version}",
                metric="daily_active_users",
                threshold=-10,  # 下降10%触发告警
                comparison="percentage_decrease"
            )
        ])
        
        # 发布成功报告
        success_report = generate_release_success_report(
            version=version,
            pipeline_id=pipeline_id,
            quality_metrics=quality_gates,
            test_results=test_results,
            deployment_metrics=production_deployment.metrics,
            duration=time.time() - start_time
        )
        
        await distribute_success_report(success_report)
        
        print(f"""
🎉 企业级发布流程成功完成！
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📦 版本: {version}
🔗 GitHub Release: {github_release.html_url}
🏗️ Staging部署: {staging_deployment.service_url}
🌐 Production部署: {production_deployment.service_url}
📊 测试结果: {test_results.total_tests}个测试全部通过
⏱️ 总耗时: {success_report.total_duration//60:.0f}分钟
🎯 影响范围: {len(linear_issues_closed)}个Linear任务已关闭
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
        """)
        
        return success_report
        
    except Exception as e:
        await handle_release_pipeline_failure(pipeline_id, version, str(e))
        raise

# 使用示例
release_result = await enterprise_automated_release_pipeline("2.5.0", release_notes)