Cloud Studio MCP 部署服务

Cloud Studio MCP 部署服务是一个基于FastMCP的服务器，提供Cloud Studio工作空间的管理功能，包括创建工作空间、上传文件和执行命令等操作。

功能特性

创建工作空间：创建新的Cloud Studio工作空间实例
文件管理：上传文件到指定工作空间
命令执行：在工作空间中执行shell命令
MCP集成：通过MCP协议提供标准化接口

安装指南

前置要求

Python 3.8+
API_TOKEN环境变量（Cloud Studio API访问令牌）

安装步骤

克隆仓库：

git clone <repository-url>
cd mcp_deploy

创建并激活虚拟环境：

python -m venv .venv
source .venv/bin/activate  # Linux/macOS
.\.venv\Scripts\activate   # Windows

安装依赖：
```
pip install -e .
```

配置说明

在运行服务前，需要设置以下环境变量：

export API_TOKEN="your_cloud_studio_api_token"
export region="ap-shanghai"  # 可选，默认为ap-shanghai

使用说明

启动服务

python -m mcp_deploy

API 文档

服务提供以下MCP工具：

1. 创建工作空间

create_workspace() -> dict

返回示例：

{
    "workspace_key": "ws-kmhhvqnlogr48",
    "webIDE": "https://ws-kmhhv1pyvc48--ide.ap-shanghai.cloudstudio.club"
}

2. 上传文件

write_files(workspace_key: str, region: str, files: list[File]) -> str

文件格式：

class File:
    save_path: str    # 文件保存路径
    file_content: str # 文件内容(UTF-8编码)

3. 执行命令

execute_command(workspace_key: str, region: str, command: str) -> str

示例：

execute_command("ws-xxxx", "ap-shanghai", "ls -al")

开发指南

项目结构

mcp_deploy/
├── __init__.py
├── __main__.py
├── mcp_handlers.py    # 核心业务逻辑
├── models.py          # 数据模型定义
└── server.py          # FastMCP服务器实现

测试

确保已设置API_TOKEN环境变量
运行测试命令：
```
python -m pytest
```

贡献

欢迎提交Pull Request或Issue报告问题。

许可证

MIT License

Metadata

Release files for mcp-deploy 0.1.12

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