How to Use the LendAPI MCP in Mastra AI

Q: Can I use LendAPI with Mastra AI for conditional branching?

You can. Use the result of getapplicationdetails to decide whether to trigger another tool or finish the workflow, allowing for complex decision trees.

Automate credit workflows in Mastra AI. Build resilient pipelines for LendAPI that handle retries and failures automatically.

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Connect LendAPI MCP to Mastra AI

Create your Vinkius account to connect LendAPI 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 LendAPI with Mastra AI

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Perplexity

Resilient workflow automation for Mastra AI

Chain `create_new_borrower` and `submit_loan_application` into complex workflows. If an API call hits a rate limit, the engine automatically triggers a retry before moving to the next step. Use conditional logic to handle failed submissions. You can route the agent to a different path if the credit decision returns a specific status code.

Complex multi-step loan processing

Build pipelines that verify application details using `get_application_details` before finalizing. The Mastra AI framework ensures that each step finishes correctly before proceeding. This architecture prevents orphaned records. You define the sequence once and let the engine manage the execution order for every single loan application.

Human-in-the-loop credit decisions

Set `requireToolApproval` for sensitive operations like `submit_loan_application`. This forces the agent to pause and wait for your manual sign-off before sending data to the server. You maintain total control over the process. It is the best way to ensure high-stakes financial data is verified by a human before the API receives it.

Setup guide

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

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

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

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Common questions about LendAPI MCP in Mastra AI

Add the server to your agents list in the config. Once registered, you can call any of the 8 tools as part of your agent's automated task sequence.

Yes, you can configure automatic retries with exponential backoff for every tool. This ensures your credit applications don't fail during high-volume periods.

You can. Use the result of `get_application_details` to decide whether to trigger another tool or finish the workflow, allowing for complex decision trees.

The framework logs every tool call by default. You can inspect the history of your workflows to see exactly how your agent interacted with the server.

Access is restricted to your authenticated agent session. We treat borrower records as sensitive data, ensuring they are never exposed outside the secure MCP transport.

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