How to Use the Wherefour MCP in Mastra AI

Q: Can I use Mastra AI to build human-in-the-loop approval steps?

Yes. The framework supports requireToolApproval. This means the workflow pauses and waits for a human user to explicitly confirm or deny an action before proceeding, perfect for sensitive transactions.

Q: What data privacy measures does Mastra AI manage when using Wherefour?

This server touches customer records, purchase histories (listpurchases), and order details. The built-in workflow engine ensures that data manipulation follows a controlled, auditable path.

Build complex workflows with Mastra AI on the Wherefour MCP Server.

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Connect Wherefour MCP to Mastra AI

Create your Vinkius account to connect Wherefour 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 Wherefour with Mastra AI

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Automated Order Processing for Mastra AI

The `get_order` tool pulls all necessary details for a single sales order. With Mastra AI, you can build multi-step workflows that check the order status first; if it's incomplete, the agent automatically retries or notifies an admin. This handles failure gracefully. You don't write code to handle every potential API hiccup—the workflow engine manages conditional branching and exponential backoff for reliability.

Multi-Step Inventory Management with Mastra AI

Need to check product availability and vendor status? The agent can chain tools like `search_inventory` and `list_vendors`. If the inventory is low, the workflow branches and triggers a purchase record list using `list_purchases`. This means you build complex business logic—like 'if X happens, then try Y, otherwise escalate to Z'—all in one command.

Complex Customer Lifecycle Workflows with Mastra AI

Building out a full customer onboarding flow? Use `list_customers` as the starting point. The agent can verify billing details by checking invoices using `list_invoices`, and if those fail, it automatically prompts for corrected data. This workflow engine lets you build sophisticated operations that handle exceptions without needing massive amounts of boilerplate error-checking code.

Setup guide

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

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

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

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Common questions about Wherefour MCP in Mastra AI

Mastra AI's workflow engine supports automatic retries with exponential backoff. If a tool call fails (like hitting an API limit), the agent doesn't crash; it tries again, backing off slightly each time until successful.

Yes. The framework supports `requireToolApproval`. This means the workflow pauses and waits for a human user to explicitly confirm or deny an action before proceeding, perfect for sensitive transactions.

You can build anything from simple data retrieval (like checking `list_orders`) to complex processes involving conditional branching and sequential tool execution across multiple steps.

The framework auto-detects transports, supporting streamable HTTP or SSE. This makes integrating the powerful tools from the Wherefour MCP Server straightforward into your existing application structure.

This server touches customer records, purchase histories (`list_purchases`), and order details. The built-in workflow engine ensures that data manipulation follows a controlled, auditable path.

Use it with your favorite AI tools

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