Vertex AI Vector Search Connector for AI agents.

Q: How do I perform a nearest-neighbor similarity test via chat?

Just write: Search my endpoint '1xxx' against index 'deployedabc1' looking for 3 nearest neighbors to the vector 0.015, -0.042, 0.111. The queryIndexTool bridges to Vertex and returns the IDs and distances of your geometrical matches instantly.

Q: Can I query a status for indices that take hours to build on GCP?

Absolutely. Use the prompt: Check my google cloud vector operations. The listOperationsTool reveals all in-flight Cloud operations indicating completion percentages and precise timestamps, allowing you to sidestep the Google Console completely.

6 live capabilities

Manage Google Cloud vector indexes and perform semantic searches directly in chat.

Live agent request Vertex AI Vector Search / Connector

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AI Agent

Why people use Vertex AI Vector Search

Vertex AI Vector Search for Managing Cloud Vector Infrastructure

This Connector changes that by bringing the data to you. You just ask your agent to list your indexes or check a deployment's status. You get the information you need in a few seconds without ever leaving your code editor.

What Vinkius changes

You get direct, conversational control over Google's vector search infrastructure without leaving your workspace.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

Real-world use case 01

Monitoring long-running index builds

An MLOps engineer is worried a 5TB index build failed.
Real-world use case 02

Testing RAG search precision

A data scientist wants to see if a new embedding model is working.
Real-world use case 03

Auditing cloud infrastructure

A backend dev needs to audit the cloud setup.

Complete set · 6capabilities

The complete Vertex AI Vector Search capability set.

These are the exact actions your AI can choose when you ask it to work with Vertex AI Vector Search.

Capability set01 / 02

01—03

3 capabilities in this set.

Part of 6 available through Vertex AI Vector Search.

01 Capability
List deployed indexes
See every index currently deployed to a specific endpoint. It's the fastest way to check what's live.
02 Capability
List index endpoints
View all index endpoints in your project. Use this to map out your production infrastructure.
03 Capability
List vector indexes
List every vector index in your project. This gives you a full bird's eye view of your data assets.

Capability set02 / 02

04—06

3 capabilities in this set.

Part of 6 available through Vertex AI Vector Search.

04 Capability
List vector operations
Check the status of long-running tasks. It's perfect for tracking multi-terabyte index builds.
05 Capability
Search nearest neighbors
Perform a similarity search using a query vector and an endpoint ID. This is the core capability for RAG applications.
06 Capability
Get index details
Get the configuration and metadata for a specific vector index. This helps you verify dimensions and settings quickly.

Set up in minutes

One URL. Then ask Vertex AI Vector Search to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Vertex AI Vector Search from the conversation.

Choose your client

Live preview

Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_7qmxXVVjzgLzy5Qveg1Au3aDsjeacjAMcHp2XBIt/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 Vector Search, and paste the URL above.
Step 03

Turn it on in chat

Select +, open Connectors, and enable Vertex AI Vector Search for the conversation.

ChatGPT · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_7qmxXVVjzgLzy5Qveg1Au3aDsjeacjAMcHp2XBIt/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 Vector Search URL.
Step 03

Save and start

Save the connection and enable Vertex AI Vector Search in your conversation. Desktop may ask you to restart once.

Cursor · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "vertex-ai-vector-search": {
      "url": "https://edge.vinkius.com/vk_preview_7qmxXVVjzgLzy5Qveg1Au3aDsjeacjAMcHp2XBIt/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 Vector Search

Open Agent mode in chat and ask: "Using Vertex AI Vector Search, help me...". 6 tools available

VS Code Copilot · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "vertex-ai-vector-search": {
      "url": "https://edge.vinkius.com/vk_preview_7qmxXVVjzgLzy5Qveg1Au3aDsjeacjAMcHp2XBIt/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 Vector Search

Ask Copilot: "Using Vertex AI Vector Search, help me...". 6 tools available

Windsurf · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "vertex-ai-vector-search": {
      "url": "https://edge.vinkius.com/vk_preview_7qmxXVVjzgLzy5Qveg1Au3aDsjeacjAMcHp2XBIt/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 Vector Search

Open Cascade and ask: "Using Vertex AI Vector Search, help me...". 6 tools available

Cline · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "vertex-ai-vector-search": {
      "url": "https://edge.vinkius.com/vk_preview_7qmxXVVjzgLzy5Qveg1Au3aDsjeacjAMcHp2XBIt/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 Vector Search

Ask Cline: "Using Vertex AI Vector Search, help me...". 6 tools available

Claude Code · Terminal command

Official guide ↗

Advanced setup

claude mcp add vertex-ai-vector-search --transport http "https://edge.vinkius.com/vk_preview_7qmxXVVjzgLzy5Qveg1Au3aDsjeacjAMcHp2XBIt/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 Vector Search

Ask Claude: "Using Vertex AI Vector Search, show me...". 6 tools are ready

Where the request belongs

Work Vertex AI Vector Search can move forward.

Built around the request

This is for the MLOps engineer who's tired of clicking through the Cloud Console at 2am to check on index builds or the RAG data scientist who needs to test vector proximity on the fly.

MLOps Engineer

Monitors multi-hour index deployments and checks for errors while staying in the terminal.

RAG Data Scientist

Pushes experimental float arrays to production endpoints to gauge search precision quickly.

Backend Architect

Verifies infrastructure shards and node counts for organization-wide vector databases.

Build the capability set

Each Connector adds new actions and data without changing how you work.

Browse Connectors

01 6 capabilities

Elasticsearch Vector

Empower vector search via Elasticsearch. perform dense vector kNN searches, handle index mappings, and index embedding documents directly from any AI agent.

View Connector

02 6 capabilities

OpenSearch Vector

Run k-NN vector searches on OpenSearch. create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.

View Connector

03 7 capabilities

Milvus (Open-Source Vector Database)

Manage vector storage via Milvus. perform ANN searches, query scalar entities, and audit collections.

View Connector

04 7 capabilities

Weaviate

Search and manage vector data on Weaviate. the AI-native database for building production-grade AI applications.

View Connector

05 6 capabilities

Redis Vector

Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes natively inside your Redis stack.

View Connector

06 7 capabilities

Qdrant

Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.

View Connector

Bring your own AI

Change the model, client or framework. Keep Vertex AI Vector Search connected.

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 Vector Search.

The practical details behind the request, access and result.

Can I use the Vertex AI Vector Search MCP to see my index status?

Yes, you can ask your agent to check the active state and configuration of any index in your project. It will return the dimensionality, configuration details, and current status instantly.

How does this Connector help with RAG applications?

It allows your agent to perform semantic searches by pulling the nearest neighbors for any query vector you provide. This helps ground your agent's responses in your own data.

Can I check if my vector deployments are actually live?

Yes, you can ask the agent to list all deployed indexes for a specific endpoint. This confirms which specific versions are currently live and receiving production traffic.

What happens if a multi-terabyte index build takes a long time?

You can use the Connector to query the operation logs and see the exact progress percentage and timeline. This lets you monitor large-scale builds without refreshing the cloud console.

Does this Connector work with other cloud providers?

No, this Connector is specifically built for Google Cloud's Vertex AI Vector Search service. It connects directly to Google Cloud infrastructure.

Can I see the dimensions of my embeddings?

Yes, the Connector can pull the metadata for any index to show you the dimensionality and other core settings. This is useful for ensuring your embedding models match your index configurations.

How do I perform a nearest-neighbor similarity test via chat?

Just write: Search my endpoint '1xxx' against index 'deployed_abc_1' looking for 3 nearest neighbors to the vector [0.015, -0.042, 0.111]. The queryIndexTool bridges to Vertex and returns the IDs and distances of your geometrical matches instantly.

Can I query a status for indices that take hours to build on GCP?

Absolutely. Use the prompt: Check my google cloud vector operations. The listOperationsTool reveals all in-flight Cloud operations indicating completion percentages and precise timestamps, allowing you to sidestep the Google Console completely.

Where do I easily find the short-lived VERTEX_ACCESS_TOKEN?

On your terminal with gcloud installed and logged in, simply type gcloud auth print-access-token. Copy the output stream starting with ya29... into your configurations and the integration is ready for connection.

One connection away

Give your agent a direct line to Vertex AI Vector Search.

Connect Vertex AI Vector Search once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available

Vertex AI Vector Search for Managing Cloud Vector Infrastructure

You get direct, conversational control over Google's vector search infrastructure without leaving your workspace.

Monitoring long-running index builds

Testing RAG search precision

Auditing cloud infrastructure

List deployed indexes

List index endpoints

List vector indexes

List vector operations

Search nearest neighbors

Get index details

Open Connectors

Add the URL

Turn it on in chat

Open MCP settings

Add the URL

Save and start

Open MCP Settings

Add the server config

Save the file

Start using Vertex AI Vector Search

Create MCP config

Add the server config

Enable Agent mode

Start using Vertex AI Vector Search

Open MCP Settings

Add the server

Save and reload

Start using Vertex AI Vector Search

Open Cline MCP Settings

Add remote server

Enable the server

Start using Vertex AI Vector Search

Install Claude Code

Add the Connector

Verify the connection

Start using Vertex AI Vector Search

MLOps Engineer

RAG Data Scientist

Backend Architect

Elasticsearch Vector

OpenSearch Vector

Milvus (Open-Source Vector Database)

Weaviate

Redis Vector

Qdrant

Give your agent a direct line to Vertex AI Vector Search.