hzy9981
https://github.com/hzy9981/vertex-ai-mcp-bridge
README
Vertex AI & DashScope Bridge MCP Server
这是一个全能型的 Model Context Protocol (MCP) 服务器，旨在连接 Google Vertex AI 的强大能力与您的本地 AI 助手。它不仅支持提示词管理与自动化优化，还集成了跨平台的工具代理（如阿里云 DashScope）。
✨ 核心特性
- 多传输协议支持:
stdio: 最适合 Cursor, VS Code 等本地 IDE。sse: 标准 Server-Sent Events，适用于 Web 客户端。streamable-http: (New) 更健壮的流式 HTTP 协议，适合云端长连接。hybrid: (New) 同时启动多种协议，适配不同集成需求。
- 远程 SSE 代理模式: 即使您的本地工具（如 Cursor）不支持远程 SSE，您也可以通过
remote_sse传输方式将云端服务透明地桥接到本地。 - 全方位提示词工程: 内置 Vertex AI Prompt Management 的 CRUD 及其最前沿的数据驱动优化工具。
- 跨云工具代理: 支持通过
call_dashscope_mcp直接调用远程 DashScope 服务。
🛠 提供的工具
| 工具类别 | 工具名称 | 功能描述 |
|---|---|---|
| Prompt CRUD | create_prompt, read_prompt, update_prompt, list_prompts, delete_prompt |
Vertex AI 提示词的全生命周期管理 |
| Optimization | run_few_shot_optimization, run_data_driven_optimize, analyze_data_driven_optimize_results |
少样本及数据驱动的提示词自动调优 |
| Proxy | call_dashscope_mcp |
代理调用远程 DashScope MCP 工具 |
🚀 部署与运行
1. 云端部署 (Cloud Run)
直接运行我们提供的全自动部署脚本：
chmod +x deploy_cloud_run.sh
./deploy_cloud_run.sh
2. 本地代理模式 (连接到已部署的服务)
如果您的客户端（如 Cursor）只支持本地 Stdio 命令行，但您希望使用云端部署好的服务：
python -m vertex.server --transport remote_sse --remote_sse_url https://YOUR-CLOUD-RUN-URL/sse
💻 客户端集成示例
Cursor / Claude Desktop (Stdio)
{
"mcpServers": {
"vertex-bridge": {
"command": "python",
"args": ["-m", "vertex.server", "--transport", "stdio"],
"env": {
"GOOGLE_CLOUD_PROJECT": "your-project-id",
"DASHSCOPE_API_KEY": "your-key"
}
}
}
}
📄 开源协议
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