• CONFIG
  • STATUS
  • DEPLOY
  • HUMAN

How to audit vector infrastructure configs with your AI

Connect your AI to Vertex AI Vector Search to inspect your deployment settings. Once connected, your agent can pull real-time configuration data from your Google Cloud project.

Ask AI about this page

Short answer

How can I audit my vector infrastructure with AI?

Your AI uses the Vertex AI Vector Search MCP to pull index details, dimensionality, and endpoint status directly from your project. You get a clear view of your deployed indexes and their specific configurations without manual console navigation.

Where every issue lands.

Audit outcomes

Your AI categorizes the data it finds into these specific areas.

CONFIG

Index metadata check

The AI pulls specific dimensionality and configuration details. You'll see if your indexes match your expected architectural requirements.

STATUS

Endpoint health

The agent identifies active endpoints and their current deployment status. This helps you spot idle or misconfigured endpoints quickly.

DEPLOY

Deployment mapping

The AI lists which indexes are currently sitting on which endpoints. You get a clear map of your active vector search footprint.

HUMAN

Manual intervention

The AI identifies discrepancies between your intended setup and reality. You decide if you need to manually update a config in the Google Cloud console.

The workflow

What your AI does when the request arrives.

The AI moves through your Google Cloud project to gather the specific configuration data you asked for.

  1. Locate endpoints

    The agent finds all available vector search endpoints in your project to establish a starting point.

    list_index_endpoints

  2. Identify indexes

    The AI scans your project to find all existing vector indexes.

    list_vector_indexes

  3. Inspect index settings

    The agent looks deep into a specific index to pull its dimensions and metadata.

    get_index_details

  4. Verify deployments

    The AI checks which indexes are actually running on your endpoints.

    list_deployed_indexes

Try it

Copy these to start.

Use these prompts to begin your audit.

Starting points

Replace the project names or index IDs with your actual Google Cloud identifiers.

Vertex AI Vector Search Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_7qmxXVVjzgLzy5Qveg1Au3aDsjeacjAMcHp2XBIt/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 Vector Search capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "vertex-ai-vector-search-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_7qmxXVVjzgLzy5Qveg1Au3aDsjeacjAMcHp2XBIt/mcp"
    }
  }
}
Copy into chat04

  • List all my vector index endpoints in my production project.

  • Show me the dimensionality and metadata for the 'user-embeddings-v1' index.

  • Which indexes are currently deployed to my primary endpoint?

  • Check if any of my vector indexes have mismatched configurations.

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

Start here

Connect Vertex AI Vector Search once, then ask.

Just link your Google Cloud credentials. Your login stays encrypted on our side, and you can immediately start auditing your infrastructure through your preferred AI client.

Connect Vertex AI Vector Search to your AI

FAQ

How this task behaves

  • 01

    Can the AI change my index configurations?

    No. The AI can only read and report on your existing configurations. You must make any changes manually in Google Cloud.

  • 02

    Can the AI delete an index if it finds an error?

    No. This MCP is read-only for your infrastructure. It identifies issues but cannot perform deletions or modifications.

  • 03

    Does the AI see my actual vector data?

    No. The AI only accesses the metadata and configuration settings of the indexes, not the high-dimensional vectors themselves.

  • 04

    How does the AI know which project to look in?

    The AI uses the project context provided by your authenticated Google Cloud session.

  • 05

    Can I use this to check multiple Google Cloud projects?

    Yes, as long as your authenticated credentials have access to those projects.

  • More questions about Vertex AI Vector Search? The Connector page answers them. See everything the Vertex AI Vector Search Connector can do

Connect Vertex AI Vector Search to Claude, Cursor, ChatGPT & more