MCP Atlassian深度测评：解锁Jira与Confluence的AI智能引擎

# 安装uv包管理器
brew install uv

# 直接运行mcp-atlassian（无需显式安装）
uvx mcp-atlassian

# 使用pip安装uv
pip install uv

# 通过uvx运行
uvx mcp-atlassian

# 使用curl安装uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# 重启终端后运行
uvx mcp-atlassian

# 安装Python 3.8+（前置条件）
# 访问 https://www.python.org/downloads/

# 使用pip安装mcp-atlassian
pip install mcp-atlassian

# 验证安装
python -c "import mcp_atlassian; print('安装成功')"

# 使用npm安装（需先安装Node.js）
npx -y @smithery/cli install mcp-atlassian --client claude

# 使用官方镜像
docker pull mcp/atlassian:latest

# 运行容器（需设置环境变量）
docker run -it --rm \
  -e ATLASSIAN_BASE_URL="your-instance.atlassian.net" \
  -e ATLASSIAN_API_TOKEN="your-api-token" \
  -e ATLASSIAN_USER_EMAIL="your-email@company.com" \
  mcp/atlassian:latest

python -m venv mcp-env
source mcp-env/bin/activate  # Linux/macOS
mcp-env\Scripts\activate     # Windows
pip install mcp-atlassian

{
  "mcpServers": {
    "mcp-atlassian": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/your/mcp-server",
        "run",
        "mcp-atlassian"
      ],
      "env": {
        "ATLASSIAN_BASE_URL": "your-company.atlassian.net",
        "ATLASSIAN_USER_EMAIL": "your-email@company.com",
        "ATLASSIAN_API_TOKEN": "your-api-token"
      }
    }
  }
}

import asyncio
from mcp_atlassian import AtlassianClient
from mcp_atlassian.models import JiraIssue, ConfluencePage
import json
from datetime import datetime, timedelta

class ProjectAnalysisAutomation:
    def __init__(self, base_url: str, email: str, api_token: str):
        self.client = AtlassianClient(
            base_url=base_url,
            user_email=email,
            api_token=api_token
        )
        
    async def analyze_pending_bugs(self, project_key: str, confluence_space: str):
        """分析待处理的Bug问题并关联Confluence文档"""
        
        # 搜索过去一周创建的Bug类型待办事项
        jql_query = (
            f'project = {project_key} AND '
            f'type = Bug AND '
            f'status = "To Do" AND '
            f'created >= "-7d"'
        )
        
        print("🔍 搜索符合条件的Jira问题...")
        bugs = await self.client.jira_search(
            jql=jql_query,
            limit=50,
            fields="key,summary,description,assignee,created"
        )
        
        analysis_results = []
        
        for bug in bugs:
            print(f"📋 处理问题: {bug.key} - {bug.summary}")
            
            # 为每个问题搜索相关的Confluence文档
            confluence_results = await self.client.confluence_search(
                query=f'siteSearch ~ "{bug.summary}" AND space = "{confluence_space}"',
                limit=3
            )
            
            # 提取相关文档信息
            related_docs = []
            for doc in confluence_results:
                related_docs.append({
                    "title": doc.title,
                    "url": doc.url,
                    "excerpt": doc.excerpt[:200] + "..." if len(doc.excerpt) > 200 else doc.excerpt
                })
            
            # 构建问题分析结果
            analysis = {
                "issue_key": bug.key,
                "summary": bug.summary,
                "description": bug.description,
                "assignee": getattr(bug, 'assignee', '未分配'),
                "related_documents": related_docs,
                "analysis_timestamp": datetime.now().isoformat()
            }
            
            analysis_results.append(analysis)
            
            # 避免速率限制，添加短暂延迟
            await asyncio.sleep(0.5)
        
        return analysis_results
    
    async def generate_confluence_report(self, analysis_results: list, space_key: str, 
                                       page_title: str = "每周Bug分析报告"):
        """在Confluence中生成分析报告"""
        
        if not analysis_results:
            print("⚠️ 未找到需要分析的问题，跳过报告生成")
            return None
        
        # 构建报告内容
        report_content = self._build_report_content(analysis_results)
        
        # 在Confluence中创建页面
        print("📝 在Confluence中生成分析报告...")
        
        page_result = await self.client.confluence_create_page(
            space_key=space_key,
            title=f"{page_title} - {datetime.now().strftime('%Y-%m-%d')}",
            body=report_content
        )
        
        print(f"✅ 报告已创建: {page_result.url}")
        return page_result
    
    def _build_report_content(self, analysis_results: list) -> str:
        """构建报告内容"""
        
        total_bugs = len(analysis_results)
        assigned_count = sum(1 for result in result.analysis_results if result.get('assignee') != '未分配')
        
        content = f"""
        <h2>每周Bug分析报告</h2>
        <p>生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M')}</p>
        
        <h3>📊 执行摘要</h3>
        <ul>
            <li><strong>待处理Bug总数</strong>: {total_bugs}</li>
            <li><strong>已分配问题</strong>: {assigned_count}</li>
            <li><strong>未分配问题</strong>: {total_bugs - assigned_count}</li>
        </ul>
        
        <h3>🔍 详细分析</h3>
        """
        
        for result in analysis_results:
            content += f"""
            <h4>问题: {result['issue_key']} - {result['summary']}</h4>
            <p><strong>负责人</strong>: {result['assignee']}</p>
            <p><strong>描述</strong>: {result['description'][:300] if result['description'] else '无描述'}...</p>
            
            <h5>相关文档:</h5>
            """
            
            if result['related_documents']:
                for doc in result['related_documents']:
                    content += f"""
                    <ul>
                        <li><a href="{doc['url']}">{doc['title']}</a> - {doc['excerpt']}</li>
                    </ul>
                    """
            else:
                content += "<p>⚠️ 未找到相关文档</p>"
            
            content += "<hr/>"
        
        content += """
        <h3>💡 建议行动</h3>
        <ol>
            <li>优先处理已分配但长期未动工的问题</li>
            <li>为未分配问题确定合适的负责人</li>
            <li>考虑为缺乏文档的问题创建解决方案指南</li>
        </ol>
        """
        
        return content

# 使用示例
async def main():
    """执行自动化分析"""
    
    # 初始化客户端
    automator = ProjectAnalysisAutomation(
        base_url="your-company.atlassian.net",
        email="your-email@company.com",
        api_token="your-api-token"
    )
    
    try:
        # 分析待处理Bug
        analysis_results = await automator.analyze_pending_bugs(
            project_key="PROJ",
            confluence_space="DEV"
        )
        
        # 生成Confluence报告
        if analysis_results:
            await automator.generate_confluence_report(
                analysis_results=analysis_results,
                space_key="DEV",
                page_title="自动化Bug分析报告"
            )
        else:
            print("🎉 恭喜！本周没有待处理的Bug问题。")
            
    except Exception as e:
        print(f"❌ 执行过程中出错: {str(e)}")

# 运行脚本
if __name__ == "__main__":
    asyncio.run(main())