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Vertex AI MCP Server

@shariqriazz

代码仓库

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

shariqriazz

关于 Vertex AI MCP Server

暂无概览

Vertex AI MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "vertex-ai-mcp-server": {
      "command": "bun",
      "args": [
        "run",
        "build"
      ]
    }
  }
}

Vertex AI MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Vertex AI MCP Server?

Vertex AI MCP Server is a Model Context Protocol (MCP) server that provides a comprehensive suite of tools for interacting with Google Cloud's Vertex AI Gemini models, focusing on coding assistance and general query answering.

How to use Vertex AI MCP Server?

Install dependencies with bun install, build with bun run build, then run via bunx vertex-ai-mcp-server or configure it in your MCP client (e.g., Cline) by specifying the command and environment variables. Required configurations include setting AI_PROVIDER and either GOOGLE_CLOUD_PROJECT (for Vertex) or GEMINI_API_KEY (for Gemini).

Key features of Vertex AI MCP Server

  • Provides AI-powered query answering with and without web search grounding.
  • Offers 20+ specialized tools for code analysis, documentation, security, and architecture.
  • Supports filesystem operations (read, write, edit, move, search files).
  • Combines AI generation with filesystem actions for saving results.
  • Configurable model ID, temperature, streaming, and retry settings via environment variables.
  • Uses streaming API by default with basic retry logic for transient errors.

Use cases of Vertex AI MCP Server

  • Answer technical questions using Google Search‑enhanced or direct AI knowledge.
  • Analyze code snippets for bugs, performance issues, and security vulnerabilities.
  • Generate structured project guidelines, documentation, and testing strategies.
  • Compare technologies and recommend architecture patterns for specific domains.
  • Perform filesystem tasks (read, write, edit, organize files) alongside AI assistance.

常见问题

1

What models does Vertex AI MCP Server use?

It provides tools for Vertex AI Gemini models, configurable via the `VERTEX_MODEL_ID` or `GEMINI_MODEL_ID` environment variables.

2

What are the prerequisites?

Node.js v18+, Bun installed globally, a Google Cloud project with billing and Vertex AI API enabled, and configured authentication (Application Default Credentials or a service account key).

3

Can I use it without a Google Cloud project?

Yes, set `AI_PROVIDER` to `"gemini"` and provide a `GEMINI_API_KEY` instead of a Google Cloud project.

4

What authentication methods are supported?

For Vertex AI, use Application Default Credentials (recommended via `gcloud auth application-default login`) or a service account key set via `GOOGLE_APPLICATION_CREDENTIALS`.

5

How do I customize the AI behavior?

Set environment variables such as `AI_TEMPERATURE`, `AI_USE_STREAMING`, `AI_MAX_OUTPUT_TOKENS`, `AI_MAX_RETRIES`, and `AI_RETRY_DELAY_MS` when configuring the MCP client.

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