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MCP Jira 集成

该项目将 Claude AI 与 Jira 相结合,以自动化和增强项目管理任务。

特征

核心功能

  • 通过 MCP 协议创建和管理 Jira 问题

  • 基于 API 密钥的身份验证

  • 人工智能交互的标准化请求/响应格式

Jira 集成功能

  • 问题创建和更新

  • 基本冲刺追踪

  • 项目和董事会管理

  • 问题搜索和检索

要求

  • Python 3.8 或更高版本

  • 带有 API 令牌的 Jira 帐户

  • 有效的 MCP 实现

设置

  1. 克隆存储库

  2. .env中配置环境变量:

    JIRA_URL=https://your-domain.atlassian.net JIRA_USERNAME=your.email@domain.com JIRA_API_TOKEN=your_api_token PROJECT_KEY=PROJ API_KEY=your_secure_api_key # For MCP authentication

API 使用

创建问题

from mcp_jira.protocol import MCPRequest, MCPContext # Create request context context = MCPContext( conversation_id=\"conv-123\", user_id=\"user-123\", api_key=\"your_api_key\" ) # Create issue request request = MCPRequest( function=\"create_issue\", parameters={ \"summary\": \"Implement feature X\", \"description\": \"Detailed description\", \"issue_type\": \"Story\", \"priority\": \"High\" }, context=context ) response = await mcp_handler.process_request(request)

搜索问题

request = MCPRequest( function=\"search_issues\", parameters={ \"jql\": \"project = PROJ AND status = 'In Progress'\" }, context=context ) response = await mcp_handler.process_request(request)

验证

所有请求都需要在请求标头中输入 API 密钥:

headers = { \"X-API-Key\": \"your_api_key\" }

与人工智能助手集成

此 MCP 实现旨在与支持 MCP 协议的 AI 助手配合使用:

  1. 配置环境变量

  2. 在 AI 助手的配置中设置 MCP 端点

  3. 使用标准化的 MCP 协议进行 Jira 交互

贡献

  1. 分叉存储库

  2. 创建功能分支

  3. 提交拉取请求

执照

MIT 许可证 - 请参阅 LICENSE 文件,消息:更新 README 以反映当前功能` }

-
security - not tested
A
license - permissive license
-
quality - not tested

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