Vertex AI MCP Server

Name: Vertex AI MCP Server
Author: shariqriazz

@shariqriazz

Repository

About Vertex AI MCP Server

No overview available yet

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

FAQ from Vertex AI MCP Server

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.

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).

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.

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.

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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