How to Use the Vertex AI Vector Search MCP in VS Code Copilot

Q: What operational status can I check with this MCP Server using VS Code Copilot?

You can use listvectoroperations to track long-running processes. This keeps your team updated on the progress of index builds or updates, which is crucial for stable releases.

Q: Does Vertex AI Vector Search support listing endpoints in VS Code Copilot?

Yes. The listindexendpoints tool lets you list all available index endpoints within the project. This helps developers pinpoint exactly where they need to direct their search queries.

Q: What is the scope of vector indexes listing in VS Code Copilot?

The listvectorindexes tool provides a comprehensive list covering all vector indexes across your entire Google Cloud project, ensuring full team coverage.

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Connect Vertex AI Vector Search MCP to VS Code Copilot

Create your Vinkius account to connect Vertex AI Vector Search to VS Code Copilot 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 VS Code Copilot

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Perform Nearest Neighbor Searches via MCP Server

The `search_nearest_neighbors` tool lets your team execute a nearest neighbor vector search across semantic embeddings. You just need to pass the endpoint ID, deployed index ID, and query vector as a JSON array. This standardization means every developer on the team gets access to this powerful data capability without needing manual setup.

Audit All Index Deployments

Team leads can use `list_deployed_indexes` to view all indexes currently deployed against a specific endpoint. This central visibility helps maintain consistency across the entire codebase. Knowing which indices are active prevents development silos and ensures everyone is working with verified data sources.

Get Global Index Inventory

If you need an overview of everything, `list_vector_indexes` provides a complete list of all vector indexes in the entire Google Cloud project. This makes onboarding new team members straightforward. It’s great for CI/CD pipelines because you can validate the full set of available resources automatically.

Setup guide

Set up Vertex AI Vector Search MCP in VS Code Copilot

Prerequisites

VS Code 1.99 or later with GitHub Copilot extension
Active Vinkius subscription with a valid endpoint token

1

Open MCP configuration

Open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and run "MCP: Add Server". Select HTTP (Streamable) as the server type. VS Code will create .vscode/mcp.json in your workspace.
2

Add the Vertex AI Vector Search MCP

Paste the JSON snippet shown on the right into your .vscode/mcp.json. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Switch to Agent mode

Open Copilot Chat (Cmd+Shift+I / Ctrl+Shift+I) and switch to Agent mode using the dropdown. MCP tools are only available in Agent mode — they do not appear in Edit or Ask modes.
4

Verify the connection

In the Copilot Chat input, type # to list available tools. You should see the Vertex AI Vector Search tools listed. Try asking: "List my recent Vertex AI Vector Search transactions" and Copilot will invoke them automatically.

.vscode/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 VS Code Copilot

You enable the MCP Server in team configuration, allowing Copilot Chat to call `search_nearest_neighbors`. You pass the necessary endpoint ID, deployed index ID, and query vector as a JSON array for immediate context.

You can use `list_vector_operations` to track long-running processes. This keeps your team updated on the progress of index builds or updates, which is crucial for stable releases.

Yes. The `list_index_endpoints` tool lets you list all available index endpoints within the project. This helps developers pinpoint exactly where they need to direct their search queries.

The `list_vector_indexes` tool provides a comprehensive list covering all vector indexes across your entire Google Cloud project, ensuring full team coverage.

The server touches index definitions and metadata for semantic embeddings. The focus remains on the structure and operational status of vector indices, keeping sensitive source code separate.

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