How to Use the Vertex AI Vector Search MCP in Cursor

Q: How does Cursor use Vertex AI Vector Search to inform my coding?

The agent mode uses the searchnearestneighbors tool. You provide the endpoint ID, deployed index ID, and query vector; it then integrates the real search results directly into the working code you're writing.

Q: Can I monitor indexing jobs in Cursor with Vertex AI Vector Search?

Yep. You can use the listvectoroperations tool to track long-running index operations within your coding session, ensuring background processes finish before you rely on them.

Q: Does Cursor need specific credentials for listing indexes?

You'll use tools like listvectorindexes and listindexendpoints. The agent handles the necessary calls, allowing you to see all available resources without leaving your editor.

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Connect Vertex AI Vector Search MCP to Cursor

Create your Vinkius account to connect Vertex AI Vector Search to Cursor and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Vertex AI Vector Search with Cursor

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Inject Live Search Results Into Code

The `search_nearest_neighbors` tool lets you write code that calls a nearest neighbor vector search and gets live results. You input the required endpoint ID, deployed index ID, and query vector as JSON arrays. The AI agent uses this real API response—not mock data—to build your function. You're getting actual semantic matching output right into the file you're writing.

Verify Index Deployment Status via MCP Server

Before committing, you can run `list_deployed_indexes` to see exactly which indexes are live on a given endpoint. This is essential for making sure your code targets the correct deployment. Checking this list means less time debugging and more time coding. The whole setup works great right inside the editor's agent mode.

Get Full Project Index Visibility

Need to know what vector indexes exist globally? `list_vector_indexes` gives you a complete list of every index in your Google Cloud project. You run this, and the resulting data feeds directly into your code context. It helps keep your local development environment perfectly aligned with your cloud setup.

Setup guide

Set up Vertex AI Vector Search MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Vertex AI Vector Search MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Vertex AI Vector Search transactions." If the MCP tools are loaded correctly, Cursor will call the Vertex AI Vector Search tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "vertex-ai-vector-search-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vertex AI Vector Search. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Vertex AI Vector Search MCP in Cursor

The agent mode uses the `search_nearest_neighbors` tool. You provide the endpoint ID, deployed index ID, and query vector; it then integrates the real search results directly into the working code you're writing.

It manages metadata and configuration related to vector indexes. The server allows you to list index details, track operations, and perform the core semantic embedding search using `search_nearest_neighbors`.

Yep. You can use the `list_vector_operations` tool to track long-running index operations within your coding session, ensuring background processes finish before you rely on them.

You'll use tools like `list_vector_indexes` and `list_index_endpoints`. The agent handles the necessary calls, allowing you to see all available resources without leaving your editor.

This server deals with index definitions and metadata for semantic embeddings. The focus is on the structure and operational status of the vector indices, not source code or user passwords.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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