Vertex AI SearchをChatGPT、Claude、Geminiに接続

• 01 / 03AIの回答を根拠付ける方法
• 02 / 03正確な回答を得る方法
• 03 / 03レコメンドの仕組みを見る

最速日

684ms

最遅日

972ms

14日間の傾向

安定-2%

Vertex AI Search Connector

準備完了です。MCPクライアントを選択し、セットアップ手順に従ってください。

コネクタリンクhttps://edge.vinkius.com/vk_preview_tPSiUT085NrK0Q7wJaTFR09NOdWshRlsftXDjz1N/mcpコピー

Claude Desktop

以下の手順で数秒で接続できます。

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — Vertex AI Search capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "vertex-ai-search-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_tPSiUT085NrK0Q7wJaTFR09NOdWshRlsftXDjz1N/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

各クライアントの手順ガイドはこちらです。 接続方法

Claude向けのガイド付きセットアップ？ Vertex AI SearchをClaudeや他のAIに設定する

このConnectorが対応するすべてのAIクライアントを見る ↑

• 01

  エンタープライズ開発者

  社内ドキュメントやナレッジベースを活用し、手動インデックスなしで根拠に基づいたAIアプリケーションを構築できます。

• 02

  ナレッジマネージャー

  膨大なドキュメントリポジトリから、対話形式で必要な情報を迅速に引き出すことができます。

• 03

  データサイエンティスト

  検索の関連性や生成AIのグラウンディング設定を、迅速にテストおよび微調整できます。

• 04

  プロダクトチーム

  最小限の手間で、パーソナライズされたレコメンデーションやAI検索機能を製品に実装できます。

• 01

  How do I connect my Google Cloud data to my AI client?

  You subscribe to the MCP on Vinkius and provide your Google Cloud Project ID, Location, and Access Token. Once connected, your AI client can immediately access your Vertex AI Search collections.

• 02

  What makes the answers 'grounded'?

  Grounded answers are generated by looking at the specific documents in your data stores. This ensures the AI provides responses based on your actual files rather than general training data.

• 03

  Which AI clients can I use with this MCP?

  You can use any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code.

• 04

  Can I manage my data stores through the AI?

  Yes. You can use the MCP to list data stores, view search engine configurations, and browse indexed documents through your agent.

• 05

  Does this replace my existing search engine?

  This MCP acts as a bridge that allows your AI agent to use your existing Vertex AI Search engines and data stores to provide conversational answers and recommendations.

• 06

  Can I get direct answers from my documents without reading through them?

  Yes. Using the get_grounded_answer capability, your AI agent can process a natural language question and return a precise answer based specifically on the content within your Vertex AI Search data stores. This grounding ensures high accuracy and reduces hallucinations by sticking to your private data as the source of truth.

• 07

  How do I know which data stores are available to search?

  Ask your agent to list your data stores. It will return all configured data stores in your collection along with their IDs and names. You can then use these IDs to perform targeted semantic searches or browse specific document branches.

• 08

  Can I use this for product recommendations on my website?

  Absolutely. The get_recommendations capability allows your agent to retrieve personalized recommendations by providing user event data. This is ideal for testing recommendation engines and surfacing relevant content or products to users based on their historical behavior.

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