MCP Server for Vertex AI Search

@ubie-oss

Basic information

About MCP Server for Vertex AI Search

A MCP server for Vertex AI Search

MCP Server for Vertex AI Search config

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

{
  "mcpServers": {
    "mcp-vertexai-search": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

MCP Server for Vertex AI Search tools

Tool list not available yet

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

Overview

What is MCP Server for Vertex AI Search?

MCP Server for Vertex AI Search is a Model Context Protocol server that searches documents using Vertex AI grounding with Gemini. It integrates one or multiple Vertex AI data stores to ground responses in private data.

How to use MCP Server for Vertex AI Search?

Clone the repository, create a virtual environment with uv, and install dependencies. Then run the server with uv run mcp-vertexai-search serve --config config.yml --transport <stdio|sse>. Alternatively, install the Python package from GitHub and use the same command. A YAML config file (template provided) is required.

Key features of MCP Server for Vertex AI Search

Uses Gemini with Vertex AI grounding for accurate search
Supports one or multiple Vertex AI data stores
Offers both SSE and stdio transport modes
Provides a dedicated search test command
Can run locally or via Docker
Configurable YAML configuration file

Use cases of MCP Server for Vertex AI Search

Perform AI‑powered search over enterprise documents stored in Vertex AI
Ground Gemini responses in private, organization‑specific data
Build MCP clients that query multiple data stores simultaneously
Test search quality directly without starting the full MCP server

Frequently asked questions

What data sources does MCP Server for Vertex AI Search support?

It supports one or multiple Vertex AI data stores. The data stores are configured via a YAML config file.

What are the runtime dependencies?

Requires Python with `uv` (or pip) and access to Vertex AI (Gemini model and data store). A Google Cloud project with Vertex AI enabled is necessary.

What transports does the server support?

It supports SSE (Server‑Sent Events) and stdio (Standard Input Output), controlled by the `--transport` flag.

Is the package published to PyPI?

No, the package is not yet published to PyPI. It can be installed directly from the GitHub repository.

How to test the search without the MCP server?

Use the command: `uv run mcp-vertexai-search search --config config.yml --query <your-query>`.

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