MCP Server for Vertex AI Search
About
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.
Details
- Author
- ubie-oss
- GitHub stars
- 35
- Downloads
- 411
- Categories
- Search
Jump to
- 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
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
MCP Server for Vertex AI SearchCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp server for vertex ai search": {
"mcp-vertexai-search": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}McpServers
{
"mcp-vertexai-search": {
"command": "uv",
"args": [
"venv"
]
}
}MCP Server for Vertex AI Search
This is a MCP server to search documents using Vertex AI.
Architecture
This solution uses Gemini with Vertex AI grounding to search documents using your private data.
Grounding improves the quality of search results by grounding Gemini's responses in your data stored in Vertex AI Datastore.
We can integrate one or multiple Vertex AI data stores to the MCP server.
For more details on grounding, refer to Vertex AI Grounding Documentation.
How to use
There are two ways to use this MCP server.
If you want to run this on Docker, the first approach would be good as Dockerfile is provided in the project.
1. Clone the repository
```shell
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