Sentry MCP 错误监控

本指南介绍如何使用 Sentry MCP 来构建智能错误监控和调试系统，让 ByteBuddy 能够自动化错误处理流程。

概述

Sentry MCP 提供了对 Sentry 错误监控平台的深度集成，让 ByteBuddy 能够：

• 自动化错误分析和分类
• 智能错误修复建议
• 性能问题检测
• 错误趋势分析

配置

1. 安装 Sentry MCP

npm install -g sentry-mcp-server

2. 配置 ByteBuddy

{
  "mcpServers": {
    "sentry": {
      "command": "node",
      "args": ["sentry-mcp-server"],
      "env": {
        "SENTRY_DSN": "your-sentry-dsn",
        "SENTRY_AUTH_TOKEN": "your-auth-token",
        "SENTRY_ORGANIZATION": "your-org",
        "SENTRY_PROJECT": "your-project"
      }
    }
  }
}

使用场景

场景 1: 智能错误分析

// 智能错误分析器
class ErrorAnalyzer {
  async analyzeError(errorId: string) {
    // 获取错误详情
    const error = await mcp.call("sentry.getError", {
      id: errorId,
    });

    // 分析错误模式
    const pattern = await this.identifyErrorPattern(error);

    // 生成修复建议
    const suggestions = await this.generateFixSuggestions(error, pattern);

    // 查找相似错误
    const similarErrors = await this.findSimilarErrors(error);

    // 创建修复任务
    const task = await this.createFixTask(error, suggestions);

    return {
      error,
      pattern,
      suggestions,
      similarErrors,
      task,
    };
  }

  private async identifyErrorPattern(error: any) {
    const analysis = {
      type: this.classifyErrorType(error),
      severity: this.calculateSeverity(error),
      frequency: this.getErrorFrequency(error),
      impact: this.calculateImpact(error),
      rootCause: await this.identifyRootCause(error),
    };

    return analysis;
  }

  private async generateFixSuggestions(error: any, pattern: any) {
    const suggestions = [];

    // 基于错误类型的建议
    switch (pattern.type) {
      case "NullPointerException":
        suggestions.push({
          title: "添加空值检查",
          code: `
// 在访问对象属性前检查空值
if (obj != null) {
  return obj.property;
} else {
  return defaultValue;
}
          `,
          confidence: 0.9,
        });
        break;

      case "TypeError":
        suggestions.push({
          title: "添加类型检查",
          code: `
// 确保参数类型正确
function processData(data: any): ProcessedData {
  if (typeof data !== 'object') {
    throw new TypeError('Expected object');
  }
  // 处理逻辑
}
          `,
          confidence: 0.85,
        });
        break;
    }

    // 基于上下文的建议
    if (pattern.frequency > 10) {
      suggestions.push({
        title: "实现错误边界",
        code: `
// React 错误边界示例
class ErrorBoundary extends React.Component {
  constructor(props) {
    super(props);
    this.state = { hasError: false };
  }
  
  static getDerivedStateFromError(error) {
    return { hasError: true };
  }
  
  render() {
    if (this.state.hasError) {
      return <h1>Something went wrong.</h1>;
    }
    return this.props.children;
  }
}
        `,
        confidence: 0.8,
      });
    }

    return suggestions;
  }

  private async createFixTask(error: any, suggestions: any[]) {
    const taskTitle = `修复错误: ${error.title}`;
    const taskBody = this.generateTaskBody(error, suggestions);

    return await mcp.call("github.createIssue", {
      owner: "your-org",
      repo: "your-repo",
      title: taskTitle,
      body: taskBody,
      labels: ["bug", "auto-generated", error.level],
      assignees: this.findAssignee(error),
    });
  }
}

场景 2: 错误趋势分析

// 错误趋势分析
class ErrorTrendAnalyzer {
  async analyzeTrends(timeRange: string = "7d") {
    // 获取错误数据
    const errorData = await mcp.call("sentry.getIssues", {
      organization: "your-org",
      project: "your-project",
      query: `is:unresolved timestamp:${timeRange}`,
      limit: 100,
    });

    // 分析趋势
    const trends = {
      newErrors: this.analyzeNewErrors(errorData),
      recurringErrors: this.analyzeRecurringErrors(errorData),
      performanceIssues: this.analyzePerformanceIssues(errorData),
      userImpact: this.analyzeUserImpact(errorData),
    };

    // 生成报告
    const report = await this.generateTrendReport(trends);

    // 创建趋势提醒
    if (trends.newErrors.count > trends.threshold) {
      await this.createTrendAlert(trends.newErrors);
    }

    return report;
  }

  private analyzeRecurringErrors(errors: any[]) {
    const recurring = errors.filter(
      (error) =>
        error.occurrences > 5 &&
        error.firstSeen < new Date(Date.now() - 7 * 24 * 60 * 60 * 1000),
    );

    // 按影响排序
    const byImpact = recurring.sort(
      (a, b) => b.userCount * b.occurrences - a.userCount * a.occurrences,
    );

    return {
      count: recurring.length,
      topIssues: byImpact.slice(0, 5),
      recommendations: this.generateRecurringRecommendations(byImpact),
    };
  }

  private generateRecurringRecommendations(recurringErrors: any[]) {
    return [
      {
        title: "实施监控仪表板",
        description: "创建实时监控仪表板来跟踪这些重复错误",
        priority: "high",
      },
      {
        title: "加强测试覆盖",
        description: "为重点模块增加自动化测试，特别是边界情况",
        priority: "medium",
      },
      {
        title: "改进错误处理",
        description: "实现更优雅的错误处理机制，避免向用户暴露技术细节",
        priority: "medium",
      },
    ];
  }
}

场景 3: 性能监控

// 性能监控系统
class PerformanceMonitor {
  async monitorPerformance() {
    // 获取性能数据
    const performanceData = await mcp.call("sentry.getPerformanceData", {
      organization: "your-org",
      project: "your-project",
      query: "transaction.duration:>1000ms",
      statsPeriod: "24h",
    });

    // 分析性能问题
    const analysis = {
      slowTransactions: this.analyzeSlowTransactions(performanceData),
      resourceUsage: this.analyzeResourceUsage(performanceData),
      userExperience: this.analyzeUserExperience(performanceData),
      recommendations:
        await this.generatePerformanceRecommendations(performanceData),
    };

    // 创建性能报告
    const report = await this.createPerformanceReport(analysis);

    return report;
  }

  private analyzeSlowTransactions(data: any) {
    const threshold = 3000; // 3秒阈值
    const slowTransactions = data.transactions.filter(
      (tx) => tx.transaction.duration > threshold,
    );

    // 按影响排序
    const byImpact = slowTransactions
      .map((tx) => ({
        name: tx.transaction,
        avgDuration: tx.transaction.duration,
        count: tx.count,
        impact: tx.count * tx.transaction.duration,
      }))
      .sort((a, b) => b.impact - a.impact);

    return {
      count: slowTransactions.length,
      totalImpact: byImpact.reduce((sum, tx) => sum + tx.impact, 0),
      topIssues: byImpact.slice(0, 5),
    };
  }

  private async generatePerformanceRecommendations(data: any) {
    const recommendations = [];

    // 分析慢查询
    if (data.databasePerformance) {
      recommendations.push({
        category: "database",
        title: "优化数据库查询",
        description: "检测到多个慢查询，建议添加索引或优化查询逻辑",
        actions: ["分析查询执行计划", "添加适当的数据库索引", "考虑使用缓存层"],
      });
    }

    // 分析资源大小
    if (data.assetSize) {
      recommendations.push({
        category: "frontend",
        title: "优化资源加载",
        description: "页面资源较大，影响加载性能",
        actions: ["压缩图片资源", "使用代码分割", "启用 HTTP/2 推送"],
      });
    }

    return recommendations;
  }
}

自动化工作流

1: 错误处理自动化

// 错误处理自动化工作流
class ErrorWorkflow {
  async setupAutomatedWorkflow() {
    // 设置错误事件监听
    mcp.on("sentry.error.created", async (event) => {
      await this.handleError(event.error);
    });

    mcp.on("sentry.error.resolved", async (event) => {
      await this.trackResolution(event.error);
    });
  }

  private async handleError(error: any) {
    // 错误分类
    const category = await this.categorizeError(error);

    // 严重性评估
    const severity = this.assessSeverity(error, category);

    // 自动处理决策
    if (severity === "critical") {
      await this.handleCriticalError(error);
    } else if (severity === "high") {
      await this.handleHighSeverityError(error);
    } else {
      await this.handleNormalError(error);
    }
  }

  private async handleCriticalError(error: any) {
    // 立即通知团队
    await this.sendCriticalAlert(error);

    // 尝试自动修复
    const autoFix = await this.attemptAutoFix(error);

    if (autoFix.success) {
      await this.applyAutoFix(error, autoFix.fix);
    } else {
      // 创建紧急任务
      await this.createUrgentTask(error);
    }
  }

  private async sendCriticalAlert(error: any) {
    const alertMessage = `
🚨 关键错误告警

错误: ${error.title}
环境: ${error.environment}
影响用户: ${error.userCount}
发生次数: ${error.occurrences}

立即查看: ${error.permalink}

此错误需要立即处理！
    `;

    await mcp.call("slack.sendMessage", {
      channel: "#critical-alerts",
      text: alertMessage,
    });

    await mcp.call("email.send", {
      to: ["dev-team@company.com"],
      subject: `关键错误: ${error.title}`,
      body: alertMessage,
    });
  }
}

2: 质量门禁

// 质量门禁系统
class QualityGate {
  async checkQualityBeforeDeployment(buildInfo: any) {
    const checks = [
      this.checkErrorRate(),
      this.checkPerformanceMetrics(),
      this.checkCrashRate(),
      this.checkUserComplaints(),
    ];

    const results = await Promise.allSettled(checks);

    const qualityReport = {
      passed: true,
      issues: [],
      metrics: {},
    };

    results.forEach((result, index) => {
      const checkName = ["errorRate", "performance", "crashRate", "complaints"][
        index
      ];

      if (result.status === "fulfilled") {
        qualityReport.metrics[checkName] = result.value;

        if (!result.value.passed) {
          qualityReport.passed = false;
          qualityReport.issues.push({
            check: checkName,
            issue: result.value.issue,
            threshold: result.value.threshold,
            actual: result.value.actual,
          });
        }
      } else {
        qualityReport.passed = false;
        qualityReport.issues.push({
          check: checkName,
          issue: "Check failed to execute",
          error: result.reason,
        });
      }
    });

    return qualityReport;
  }

  private async checkErrorRate() {
    const errorRate = await mcp.call("sentry.getErrorRate", {
      organization: "your-org",
      project: "your-project",
      timeRange: "24h",
    });

    const threshold = 0.01; // 1%

    return {
      passed: errorRate < threshold,
      actual: errorRate,
      threshold: threshold,
      issue:
        errorRate >= threshold
          ? `错误率 ${(errorRate * 100).toFixed(2)}% 超过阈值 ${(threshold * 100).toFixed(2)}%`
          : null,
    };
  }
}

最佳实践

1: 错误处理策略

// 统一错误处理
class UnifiedErrorHandler {
  async handleAllErrors(error: any) {
    const context = {
      error,
      timestamp: new Date().toISOString(),
      environment: process.env.NODE_ENV,
      version: process.env.APP_VERSION,
    };

    // 记录到 Sentry
    await this.logToSentry(error, context);

    // 本地日志
    await this.logLocally(error, context);

    // 用户友好提示
    const userMessage = this.generateUserFriendlyMessage(error);

    // 内部通知
    if (this.shouldNotify(error)) {
      await this.notifyTeam(error, context);
    }

    return {
      handled: true,
      userMessage,
      trackingId: this.generateTrackingId(),
    };
  }
}

通过 Sentry MCP，ByteBuddy 可以提供强大的错误监控和智能调试能力，让应用更加稳定可靠。