How to Use the Vertex AI Search MCP in Claude Code

Q: How do I execute a search query using Vertex AI Search and Claude Code?

You invoke searchdocuments via the terminal command line, supplying both the data store ID and your query text. The result is standard output you can process with shell scripts.

Q: What does MCP Server provide for listing document indexes with Vertex AI Search and Claude Code?

The listdatastoredocuments tool lets you enumerate all individual documents within a specific branch ID. This is necessary for scripting checks on data ingestion status.

Q: Can I use Vertex AI Search to verify private data access in Claude Code?

Yes. The server uses the getdatastoredetails tool, allowing your script to validate required configuration and metadata for the target data store before running any searches.

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Setup Vertex AI Search with Claude Code

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Claude Code: Document Retrieval

To find information, use `search_documents`, providing the data store ID and query text. The output is clean JSON that can be piped directly into subsequent scripts or logging tools. This makes it perfect for CI/CD pipelines where you need to check documentation status before deployment.

MCP Server: System Inventory

You can run `list_data_stores` to get a manifest of all searchable data stores. This is the first step in any automated script that needs to validate its scope. Check available engines with `list_search_engines`. Knowing this lets your script decide if it needs to call a specific search mechanism.

Claude Code: Generating Contextual Data

The `get_grounded_answer` tool executes the core AI function, returning an answer based on private documents. You pipe this output into your logging system or validation script. It’s reliable because it links the generated text back to specific source materials.

Setup guide

Set up Vertex AI Search MCP in Claude Code

Prerequisites

Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
Active Vinkius subscription with a valid endpoint token

1

Run the add command

Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.
2

Verify the connection

Start a Claude Code session and type /mcp to list connected servers. You should see vertex-ai-search-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest Vertex AI Search transactions." It will automatically discover and invoke the available Vertex AI Search tools.

Terminal

claude mcp add --transport http vertex-ai-search-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

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Prerequisites

Claude Code CLI installed
Active Vinkius subscription with a valid endpoint token

1

Open the config file

Create or edit .mcp.json in your project root for project-level scope, or ~/.claude.json for user-level scope.
2

Add the Vertex AI 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

Restart Claude Code

Start a new Claude Code session. Type /mcp to confirm the server is connected. The tools will be automatically available in your conversation.

.mcp.json

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

Get your connection token →

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Common questions about Vertex AI Search MCP in Claude Code

You invoke `search_documents` via the terminal command line, supplying both the data store ID and your query text. The result is standard output you can process with shell scripts.

The `list_datastore_documents` tool lets you enumerate all individual documents within a specific branch ID. This is necessary for scripting checks on data ingestion status.

Yes. The server uses the `get_datastore_details` tool, allowing your script to validate required configuration and metadata for the target data store before running any searches.

This server reads document content and configuration details from designated, private data stores. It only works on the documents you specify in your collection.

It is. The tools provide explicit mechanisms for listing sources (`list_data_stores`) and executing searches (`search_documents`), ensuring predictable, scriptable behavior.

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