Vertex AI Search Connector for AI agents.
7 live capabilities
Query private enterprise documentation for grounded answers and recommendations.
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Why people use Vertex AI Search
Vertex AI Search for Grounded Enterprise Knowledge
This Connector changes the workflow by giving your AI agent a direct line to your Vertex AI Search setup. Instead of searching manually, you just ask a question. The agent finds the right document, reads it, and gives you a direct answer. You get a knowledge expert that actually knows your company's secrets.
What Vinkius changes
Your AI agent gains a direct line to your private enterprise knowledge.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Answering HR policy questions
An employee asks about the remote work policy, and the agent uses get_grounded_answer to pull the exact policy from the HR folder.
- Real-world use case 02
Auditing indexed content
A manager needs to see what is currently searchable, so the agent uses list_datastore_documents to verify the latest files are included.
- Real-world use case 03
Generating product suggestions
A customer wants a product suggestion, and the agent uses get_recommendations to offer items based on their recent clicks.
Complete set · 7capabilities
The complete Vertex AI Search capability set.
These are the exact actions your AI can choose when you ask it to work with Vertex AI Search.
01—04
4 capabilities in this set.
Part of 7 available through Vertex AI Search.
- 01 Capability
Search documents
Performs a search query across documents in a specific data store. It lets your agent find specific files using natural language.
- 02 Capability
Get grounded answer
Returns a natural language response based on your private data. It ensures the AI stays grounded in your specific documents.
- 03 Capability
Get datastore details
Pulls configuration and metadata for a specific data store. Use this to check the status of your searchable datasets.
- 04 Capability
List data stores
Lists all the data stores in your Vertex AI Search collection. It helps you keep track of your different searchable buckets.
05—07
3 capabilities in this set.
Part of 7 available through Vertex AI Search.
- 05 Capability
List datastore documents
Lists all indexed documents within a specific data store branch. Use this to audit what is actually being indexed.
- 06 Capability
List search engines
Lists all search engines configured in your collection. This is useful for managing different business use cases.
- 07 Capability
Get recommendations
Retrieves personalized recommendations based on user events. It helps you build smarter user experiences.
Set up in minutes
One URL. Then ask Vertex AI Search to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Vertex AI Search from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_tPSiUT085NrK0Q7wJaTFR09NOdWshRlsftXDjz1N/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Vertex AI Search, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Vertex AI Search for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_tPSiUT085NrK0Q7wJaTFR09NOdWshRlsftXDjz1N/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Vertex AI Search URL.
- Step 03
Save and start
Save the connection and enable Vertex AI Search in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"vertex-ai-search": {
"url": "https://edge.vinkius.com/vk_preview_tPSiUT085NrK0Q7wJaTFR09NOdWshRlsftXDjz1N/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Vertex AI Search
Open Agent mode in chat and ask: "Using Vertex AI Search, help me...". 7 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"vertex-ai-search": {
"url": "https://edge.vinkius.com/vk_preview_tPSiUT085NrK0Q7wJaTFR09NOdWshRlsftXDjz1N/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Vertex AI Search
Ask Copilot: "Using Vertex AI Search, help me...". 7 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"vertex-ai-search": {
"url": "https://edge.vinkius.com/vk_preview_tPSiUT085NrK0Q7wJaTFR09NOdWshRlsftXDjz1N/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Vertex AI Search
Open Cascade and ask: "Using Vertex AI Search, help me...". 7 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"vertex-ai-search": {
"url": "https://edge.vinkius.com/vk_preview_tPSiUT085NrK0Q7wJaTFR09NOdWshRlsftXDjz1N/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Vertex AI Search
Ask Cline: "Using Vertex AI Search, help me...". 7 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add vertex-ai-search --transport http "https://edge.vinkius.com/vk_preview_tPSiUT085NrK0Q7wJaTFR09NOdWshRlsftXDjz1N/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Vertex AI Search
Ask Claude: "Using Vertex AI Search, show me...". 7 tools are ready
Where the request belongs
Work Vertex AI Search can move forward.
This is for the knowledge manager tired of answering the same questions over and over, or the developer building internal capabilities who needs to ensure the AI doesn't hallucinate.
Knowledge Manager
Uses this to surface info from massive document repositories for the rest of the company.
Enterprise Developer
Builds grounded AI apps using internal docs without manual indexing headaches.
Data Scientist
Tests and refines search relevance and grounding configurations on real data.
Product Manager
Implements personalized recommendations for users with minimal friction.
Build the capability set
Add more capabilities.
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Amazon Bedrock KB
Connect your AI agent to AWS Bedrock Knowledge Bases. execute semantic searches, managed RAG, and sync vector datasources natively.
Exa
Find exactly the web content you need with semantic search that understands context and returns high-quality curated results.
Bring your own AI
Change the model, client or framework. Keep Vertex AI Search connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Vertex AI Search.
The practical details behind the request, access and result.
Can the Vertex AI Search MCP help my AI agent stop making things up?
Yes. By using the get_grounded_answer capability, your agent is forced to look at your actual private documents before answering. This significantly reduces hallucinations because the AI relies on your data as its source of truth.
Do I need to upload my files to this Connector directly?
No. This Connector connects your AI agent to your existing Vertex AI Search setup in Google Cloud. You manage your data and indexing in Google Cloud, and this Connector provides the bridge for your agent to access it.
Can I use this to manage my data stores?
Yes. You can use the list_data_stores and list_search_engines capabilities to browse and manage your search applications directly through your AI client.
How does the recommendation feature work?
The get_recommendations capability takes user event data and your data store ID to return personalized suggestions. It allows your agent to act like a smart personal shopper for your users.
Is my private data safe with this Connector?
Yes. The Connector acts as a secure bridge to your Vertex AI Search account. It doesn't store your data; it just allows your AI client to query the data you've already authorized in your Google Cloud project.
What kind of documents can I search?
You can search any documents that are indexed in your Vertex AI Search data stores, including PDFs, HTML files, and other text-based enterprise data.
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.
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.
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.
One connection away
Give your agent a direct line to Vertex AI Search.
Connect Vertex AI Search once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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