How to Use the ValueSERP MCP in Mastra AI

Q: Can I use ValueSERP to analyze local data with Mastra AI?

Yes. You can set up a workflow that first calls googleplacessearch and then uses the returned location data in subsequent steps. This makes multi-step market analysis straightforward.

Q: What if I need to find video evidence using ValueSERP with Mastra AI?

You simply add a step that calls googlevideosearch. The workflow engine will process those results, allowing you to incorporate video links into your final, structured output report.

ValueSERP for Mastra AI: Build complex workflows that handle search failures and retry automatically.

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Connect ValueSERP MCP to Mastra AI

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Setup ValueSERP with Mastra AI

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Automated Search Retries

If a primary data source fails, your workflow doesn't stop. By integrating ValueSERP, you can build retries around tools like `google_search`. The system automatically back-offs and tries again until it gets clean organic results. This reliable search capability means your complex workflows aren't derailed by transient API errors or rate limits from the underlying MCP Server.

Multi-Step Data Gathering

Need a full picture? You can build conditional branches. For instance, first run `google_places_search` to get coordinates; if those exist, then use `google_shopping_search` with the location context. This chaining of search actions allows your agent to build highly specific profiles or market analyses automatically.

Structured Academic Research

Complex research demands precision. Use `google_scholar_search` within a structured workflow. Your agent can pull abstracts, analyze them for keywords, and then branch out to run a secondary search using `get_related_questions`. This ensures that every piece of data—from initial discovery to related context—is captured in one robust process.

Setup guide

Set up ValueSERP 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 ValueSERP 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: "valueserp-mcp-client",
  servers: {
    "valueserp-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent ValueSERP 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 ValueSERP. 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 ValueSERP MCP in Mastra AI

Mastra AI's workflow engine manages this natively. You can wrap any tool call, like `google_search`, in a retry block. If the search fails, it automatically tries again, saving you from writing complex error handling logic.

Yes. You can set up a workflow that first calls `google_places_search` and then uses the returned location data in subsequent steps. This makes multi-step market analysis straightforward.

The server handles diverse public web content: general search results, local business listings, academic abstracts, product pricing, and more. It's all read-only information.

You simply add a step that calls `google_video_search`. The workflow engine will process those results, allowing you to incorporate video links into your final, structured output report.

You can run a primary search and immediately follow up using `get_related_questions`. The workflow handles this sequence automatically, ensuring your agent captures the full scope of user intent.

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