How to Use the Vertex AI Search MCP in Mastra AI

Q: How does Vertex AI Search integrate with Mastra AI for workflow execution?

You invoke the tools like getgroundedanswer within a sequential step. Mastra AI manages the execution, ensuring that if the call fails, it will retry the entire search action using exponential backoff.

Q: Can I use Vertex AI Search to list data sources in Mastra AI workflows?

Absolutely. Use listdatastores early in your workflow setup. This allows your agent to dynamically discover all available knowledge bases before attempting any search.

Q: Does this MCP Server support structured document querying for Mastra AI?

Yes. The searchdocuments tool is designed for exactly that—running specific, targeted queries across indexed documents using a given data store ID and query text.

Q: If I use Vertex AI Search and Mastra AI, what information can I get about the index?

You can check the indexed content using listdatastoredocuments, which reports on all documents within a specific data store branch. This lets you audit your source material.

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Connect Vertex AI Search MCP to Mastra AI

Create your Vinkius account to connect Vertex AI Search to Mastra AI 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 Search with Mastra AI

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Perplexity

Grounding Answers in Complex Flows

Use `get_grounded_answer` as a reliable step in your workflow. If a multi-step process needs to answer a question based on private data, this tool handles it and returns the natural language response. Because Mastra AI manages retries and failure handling, you can trust that critical steps will attempt to complete using this grounding mechanism.

Automated Recommendation Logic

The `get_recommendations` tool allows your workflow to analyze user events. You pass the data store ID and the JSON event payload, letting Mastra AI figure out the next best action or suggestion. This is ideal for automated customer journey mapping where success depends on sequential, context-aware suggestions.

Structured Search Execution

When your workflow needs to search documents, `search_documents` executes the query across a specific data store. This tool is critical when you need structured results rather than just a single narrative answer. It provides direct access to semantic search functionality, which runs reliably even if other steps in your complex process fail.

Setup guide

Set up Vertex AI Search MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Vertex AI Search tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "vertex-ai-search-mcp-client",
  servers: {
    "vertex-ai-search-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Vertex AI Search Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Vertex AI Search tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Vertex AI Search transactions"
);
console.log(result.text);

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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 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 Search MCP in Mastra AI

You invoke the tools like `get_grounded_answer` within a sequential step. Mastra AI manages the execution, ensuring that if the call fails, it will retry the entire search action using exponential backoff.

Absolutely. Use `list_data_stores` early in your workflow setup. This allows your agent to dynamically discover all available knowledge bases before attempting any search.

Yes. The `search_documents` tool is designed for exactly that—running specific, targeted queries across indexed documents using a given data store ID and query text.

The server handles private textual content within defined data stores. This includes both general document metadata and user event JSON payloads, requiring careful credential management.

You can check the indexed content using `list_datastore_documents`, which reports on all documents within a specific data store branch. This lets you audit your source material.

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