How to Use the Franchimp MCP in Mastra AI

Q: How do I initialize Franchimp inside Mastra AI?

Run npm install @mastra/mcp@latest. Instantiate a new MCPClient with your server URL, fetch the available tools, and spread those directly into your Agent configuration.

Q: Can Mastra AI retry failed Franchimp searches automatically?

Yes. If a call to findfranchiseebyemail fails, the built-in workflow engine uses exponential backoff to try the request again. You define the retry limits in your workflow steps.

Q: Does Mastra AI support conditional logic with Franchimp MCP data?

It does. You can configure a workflow to call listfranchisees and branch into different notification paths depending on the number of records returned.

Automate complex franchise lead generation workflows and handle API failures gracefully using Franchimp and Mastra AI.

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Connect Franchimp MCP to Mastra AI

Create your Vinkius account to connect Franchimp 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 Franchimp with Mastra AI

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Automate Multi-Step Lead Routing

Your Mastra AI workflow uses `list_multi_unit_operators` to identify high-value targets before conditionally branching based on their portfolio size. If the operator owns enough units, the agent moves down the pipeline to pull their exact contact info with `get_franchisee_details`. You build logical paths that adapt to the data. When an endpoint times out, Mastra AI triggers automatic retries with exponential backoff. Your workflow does not break just because a query to `search_franchisors` hit a temporary snag. The agent pauses, tries again, and keeps the lead generation engine running without manual intervention.

Human-in-the-Loop FDD Verification

`get_fdd_metadata` and `list_fdd_documents` pull sensitive disclosure documents that often require a final human review. You can set tool approval requirements in Mastra AI for these specific MCP tools. The workflow pauses, alerts your compliance team, and waits for a green light before proceeding. Once approved, the agent continues the sequence. It might query `get_franchise_details` to append franchisor metadata to the verified FDD record. You get a production-ready pipeline that balances automated speed with mandatory oversight.

Resilient Investment Data Pipelines with Mastra AI

Your agent calls `get_investment_stats` to evaluate potential franchise profitability across different markets. Mastra AI handles the transport layer for this MCP Server automatically, detecting whether to use Streamable HTTP or SSE based on your environment. You write the logic while the framework manages the connection. Deploy this entire intelligence operation to any cloud provider with a single command. The workflow checks `list_available_credits` via `get_account_info` at startup, ensuring the pipeline has the resources it needs before executing heavy data pulls.

Setup guide

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

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

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

Run `npm install @mastra/mcp@latest`. Instantiate a new `MCPClient` with your server URL, fetch the available tools, and spread those directly into your Agent configuration.

Yes. If a call to `find_franchisee_by_email` fails, the built-in workflow engine uses exponential backoff to try the request again. You define the retry limits in your workflow steps.

Enable approval requirements for the document retrieval tool. The agent will halt execution and wait for human authorization before fetching the disclosure files.

It does. You can configure a workflow to call `list_franchisees` and branch into different notification paths depending on the number of records returned.

The server handles proprietary financial metrics pulled via `get_investment_stats`. Every connection routes through an ephemeral V8 isolate that spins up solely for your request. Once the data transfers, the sandbox terminates, wiping all trace of the financial data.

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