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Connect Vertex AI Search to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Turn your private enterprise data into a searchable knowledge base.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 7 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-04

4 capabilities in this set.

Part of 7 available through Vertex AI Search.

  1. 01

    Get datastore details

    This capability pulls the specific configuration and metadata for a single data store.

  2. 02

    List datastore documents

    Use this to see all indexed documents within a specific branch of a data store.

  3. 03

    List search engines

    This capability returns a list of all search engines configured in your collection.

  4. 04

    Search documents

    This capability runs a search query against a specific data store to find relevant documents.

Capability set02 / 02

05-07

3 capabilities in this set.

Part of 7 available through Vertex AI Search.

  1. 05

    List data stores

    This capability provides a list of every data store available in your Vertex AI Search collection.

  2. 06

    Get recommendations

    This capability retrieves personalized recommendations based on specific user event data.

  3. 07

    Get grounded answer

    This capability generates a natural language answer that is strictly grounded in your data store documents.

Real work

What teams actually run

01

Internal Knowledge Retrieval

An employee asks about company policy and gets an answer pulled directly from the HR handbook.

02

Product Catalog Search

A support agent searches for specific product specs within a massive inventory database.

03

Developer Documentation

A developer queries internal API docs to find specific implementation details.

04

Recommendation Engines

A product team uses user event data to surface relevant items to customers.

Automate a task

Put Vertex AI Search to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Vertex AI Search works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Vertex AI Search to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

819ms average. Fast in production.

Vertex AI Search is checked daily against the live service.

Daily averagePeak 972ms
Oct 3Today
Fastest day
684ms
Slowest day
972ms
14-day trend
Stable-2%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 7 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Vertex AI Search, so you can see the experience inside your AI.

It does not authenticate your account with Vertex AI Search. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Vertex AI Search Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_tPSiUT085NrK0Q7wJaTFR09NOdWshRlsftXDjz1N/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  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.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "vertex-ai-search-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_tPSiUT085NrK0Q7wJaTFR09NOdWshRlsftXDjz1N/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Vertex AI Search in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Vertex AI Search owners hand off.

This MCP is built for technical teams managing large-scale enterprise knowledge bases.

  • 01

    Enterprise Developers

    They build grounded AI applications using internal documentation without manual indexing.

  • 02

    Knowledge Managers

    They surface relevant info from massive document repositories through conversation.

  • 03

    Data Scientists

    They test and refine search relevance and generative grounding configurations.

  • 04

    Product Teams

    They implement personalized recommendation features with minimal friction.

FAQ

Questions Vertex AI Search owners ask.

  • 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.

Explore

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