# Google AI Search MCP server

> Run web-grounded search, documentation lookups, code analysis and architecture research through Google Vertex AI or the Gemini API.

- Listing: https://mcp.tc/i/google-ai-search
- Connect: this is a local (stdio) server; install it on your machine (see Install). The listing link is a page, not an MCP endpoint.
- Type: local (stdio)
- Auth: API key
- Category: [Docs & Knowledge](https://mcp.tc/c/docs-knowledge)
- Vendor: shariqriazz
- Repository: <https://github.com/shariqriazz/google-ai-search-mcp>
- Package: npm `google-ai-search-mcp`

## About

A community MCP server that exposes Google AI models (Vertex AI or the Gemini API) as developer-focused tools. It answers queries with web search grounding, explains topics from official documentation, retrieves doc snippets, generates project guidelines, analyzes code against documentation, compares technologies and recommends architecture patterns.

It runs locally over stdio from the npm package google-ai-search-mcp, started with npx or bunx. Set AI\_PROVIDER to vertex (needs GOOGLE\_CLOUD\_PROJECT and Google Cloud credentials) or gemini (needs GEMINI\_API\_KEY). Output is model-generated and should be checked against primary sources.

## What it can do

- Answer technical queries using Google AI with web search grounding
- Explain topics using official documentation
- Retrieve code snippets from official docs
- Generate structured project guidelines for a technology stack
- Analyze code against documentation best practices
- Compare technologies across given criteria
- Recommend architecture patterns with tradeoffs

## Example prompts

- "Search the web for the latest breaking changes in Next.js and summarize them"
- "Explain how Kubernetes network policies work, using the official docs"
- "Get code snippets for using the Gemini API streaming endpoint"
- "Compare Postgres and MongoDB for an event logging workload"

## Install

### Claude Code

1. Run this in a terminal, in your project folder:

```bash
claude mcp add --transport stdio google-ai-search --env "GEMINI_API_KEY=<YOUR_GEMINI_API_KEY>" -- npx -y google-ai-search-mcp
```

2. Start Claude Code and type `/mcp`. **google-ai-search** should show as connected.

Add `--scope user` to make it available in every project. Replace the placeholders with your own values.

### Claude Desktop

1. Open **Settings → Developer → Edit Config**. It opens `claude_desktop_config.json`. Add:

`claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "google-ai-search": {
      "command": "npx",
      "args": [
        "-y",
        "google-ai-search-mcp"
      ],
      "env": {
        "GEMINI_API_KEY": "<YOUR_GEMINI_API_KEY>"
      }
    }
  }
}
```

2. Save the file and restart Claude Desktop. Replace the placeholders with your own values.

Needs Node.js on your computer. The file lives in `~/Library/Application Support/Claude/` on macOS and `%APPDATA%\Claude\` on Windows.

### Cursor

[Add to Cursor](<https://cursor.com/install-mcp?name=google-ai-search&config=eyJjb21tYW5kIjoibnB4IiwiYXJncyI6WyIteSIsImdvb2dsZS1haS1zZWFyY2gtbWNwIl0sImVudiI6eyJHRU1JTklfQVBJX0tFWSI6IjxZT1VSX0dFTUlOSV9BUElfS0VZPiJ9fQ%3D%3D>) (opens Cursor)

Or add it by hand to `~/.cursor/mcp.json` (all projects) or `.cursor/mcp.json` (this project):

`mcp.json`:

```json
{
  "mcpServers": {
    "google-ai-search": {
      "command": "npx",
      "args": [
        "-y",
        "google-ai-search-mcp"
      ],
      "env": {
        "GEMINI_API_KEY": "<YOUR_GEMINI_API_KEY>"
      }
    }
  }
}
```

Needs Node.js on your computer. Replace the placeholders with your own values.

### VS Code

Add it to `.vscode/mcp.json`. VS Code asks for the secret the first time and stores it securely:

`.vscode/mcp.json`:

```json
{
  "servers": {
    "google-ai-search": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "google-ai-search-mcp"
      ],
      "env": {
        "GEMINI_API_KEY": "${input:gemini-api-key}"
      }
    }
  },
  "inputs": [
    {
      "type": "promptString",
      "id": "gemini-api-key",
      "description": "GEMINI_API_KEY",
      "password": true
    }
  ]
}
```

Needs Node.js on your computer.

### Devin Desktop

1. Add it to `~/.config/devin/mcp_config.json` (macOS and Linux) or `%APPDATA%\devin\mcp_config.json` (Windows):

`mcp_config.json`:

```json
{
  "mcpServers": {
    "google-ai-search": {
      "command": "npx",
      "args": [
        "-y",
        "google-ai-search-mcp"
      ],
      "env": {
        "GEMINI_API_KEY": "<YOUR_GEMINI_API_KEY>"
      }
    }
  }
}
```

2. Refresh the MCP server list in Cascade. Replace the placeholders with your own values.

Devin Desktop is the new name for Windsurf.

### Codex

```bash
codex mcp add google-ai-search --env "GEMINI_API_KEY=<YOUR_GEMINI_API_KEY>" -- npx -y google-ai-search-mcp
```

Or edit `~/.codex/config.toml` directly:

`config.toml`:

```toml
[mcp_servers.google-ai-search]
command = "npx"
args = ["-y", "google-ai-search-mcp"]
env = { GEMINI_API_KEY = "<YOUR_GEMINI_API_KEY>" }
```

Needs Node.js on your computer. Replace the placeholders with your own values.

### Gemini CLI

```bash
gemini mcp add -e "GEMINI_API_KEY=<YOUR_GEMINI_API_KEY>" google-ai-search npx -- -y google-ai-search-mcp
```

This adds it to the current project. Add `-s user` to use it everywhere.

### Any client

Most clients that start local servers accept this shape:

```json
{
  "mcpServers": {
    "google-ai-search": {
      "command": "npx",
      "args": [
        "-y",
        "google-ai-search-mcp"
      ],
      "env": {
        "GEMINI_API_KEY": "<YOUR_GEMINI_API_KEY>"
      }
    }
  }
}
```

Zed puts servers under `context_servers` in its settings, with the same `command`, `args` and `env` fields.

Needs Node.js on your computer. Replace the placeholders with your own values.

## Details

- Server version: 1.1.1
- Last checked: 2026-10-04
- Listed: 2026-10-04
- Updated: 2026-10-04

---
Source: https://mcp.tc/i/google-ai-search (mcp.tc is an independent directory, not affiliated with this server's publisher). Corrections: https://mcp.tc/report