How to Use the Fera.ai MCP in Mastra AI

Q: Does Mastra AI support multiple Fera.ai stores?

It does. You can use liststores to map your agent to the correct account, then execute workflows against specific store data.

Build resilient feedback workflows in Mastra AI using the Fera.ai MCP Server to automate review monitoring and store management.

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Connect Fera.ai MCP to Mastra AI

Create your Vinkius account to connect Fera.ai 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 Fera.ai with Mastra AI

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Mastra AI Workflow Automation for Reviews

Trigger complex logic flows based on incoming feedback by using `list_reviews`. You can build a Mastra workflow that branches based on rating thresholds, ensuring negative reviews get flagged immediately. It turns raw review data into an active management system. Your agents handle the escalation logic without you needing to monitor the dashboard manually.

Retry Logic for Fera.ai API Calls

Mastra AI handles the heavy lifting of API reliability with built-in retries for `get_product_rating` and `list_products`. If the network blips, your workflow waits and tries again automatically. This ensures your social proof data is always consistent. You don't have to write custom error handling for every single tool call.

Customer Profile Analysis within Mastra AI

Deep dive into your customer base by invoking `list_customers` and `get_customer` within your agent workflows. Your agents can correlate purchase history with review quality to identify your top brand advocates. It makes your social proof strategy data-backed. You get the specific details needed to tailor your outreach based on actual customer feedback.

Setup guide

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

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

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

Yes. Mastra AI provides built-in exponential backoff for all MCP tools. If a call to Fera.ai fails, the framework handles the retry cycle for you.

Use the tool approval feature in your agent definition. You can force the agent to pause before acting on any `get_review` data it retrieves.

The server provides the data, but you should configure your Mastra workflows to respect API limits. You can control the frequency of your calls in the workflow engine.

It does. You can use `list_stores` to map your agent to the correct account, then execute workflows against specific store data.

Customer names and review content are treated as ephemeral payloads. The data exists only within the memory of your agent process during the workflow execution.

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