How to Use the Vertex AI Vector Search MCP in Windsurf

Q: How do I find out what indexes are available using Vertex AI Vector Search and Windsurf?

Just run listvectorindexes through your agent. It pulls a full list of all vector indexes in the cloud project, giving you names and basic IDs to start with.

Q: If I run into deployment issues with Vertex AI Vector Search using Windsurf, what tools help?

Try listing the operations with listvectoroperations. This shows long-running status updates related to your vector indexes, helping you figure out why a search hasn't finished.

Q: Does Vertex AI Vector Search support metadata checks with Windsurf?

Yes. You can use getindexdetails to retrieve the full metadata and configuration for any specific vector index, helping you validate its setup before running a search.

Run complex vector similarity searches with Windsurf’s autonomous agent.

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Connect Vertex AI Vector Search MCP to Windsurf

Create your Vinkius account to connect Vertex AI Vector Search to Windsurf 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 Windsurf

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Search nearest neighbors in a single step.

Use the `search_nearest_neighbors` tool to query vast sets of semantic embeddings. You just need three things: an endpoint ID, the index ID you want to use, and your query vector array. Windsurf’s Cascade reads this context, plans the search, executes it, and shows you the full result set without needing step-by-step prompts.

Check project index status.

Need to know what indexes are available? The `list_vector_indexes` tool pulls a complete list of all vector indexes in your Google Cloud project. It’s great for planning. If you're unsure which endpoint ID to use, running `list_index_endpoints` helps narrow down the options before you start chaining actions.

Inspect index metadata.

The `get_index_details` tool retrieves all metadata and configuration for a specific vector index. This tells you exactly how the index is built and what it expects. If an autonomous task fails, checking these details helps diagnose whether the problem is with the index setup or the query itself.

Setup guide

Set up Vertex AI Vector Search MCP in Windsurf

Prerequisites

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

1

Open MCP configuration

Click the Cascade assistant icon in the sidebar, then click the hammer icon (🔨) at the top of the panel. Select "Configure" to open ~/.codeium/windsurf/mcp_config.json.
2

Add the Vertex AI Vector Search MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Refresh MCPs

Go back to the hammer icon (🔨) in Cascade and click "Refresh". Windsurf will detect the new server. No full restart is needed — the connection is hot-reloaded.
4

Verify in Cascade

Start a new Cascade conversation and ask something like "Show my Vertex AI Vector Search payment history." If connected, Cascade will call the Vertex AI Vector Search tools directly. You will see a green dot next to the server name in the MCP panel.

mcp_config.json

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

Get your connection token →

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 Windsurf

Just run `list_vector_indexes` through your agent. It pulls a full list of all vector indexes in the cloud project, giving you names and basic IDs to start with.

You need an endpoint ID, a deployed index ID, and your query vector. The `search_nearest_neighbors` tool requires these three inputs to execute any search.

Try listing the operations with `list_vector_operations`. This shows long-running status updates related to your vector indexes, helping you figure out why a search hasn't finished.

Yes. You can use `get_index_details` to retrieve the full metadata and configuration for any specific vector index, helping you validate its setup before running a search.

This server touches Vector Index Metadata, which includes names, configurations, and list of endpoints. It handles the structural definitions for your embeddings.

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