How to Use the Unanet MCP in Mastra AI

Q: What kind of data does Mastra AI use from Unanet?

It uses all four tools: projects(), users(), timesheets(), and expenses(). You can combine them into a single, resilient workflow to process comprehensive operational data.

Q: What specific data types are exposed by this MCP Server for privacy considerations?

The server touches user profiles (users()), project metadata (projects()), and financial activity logs like time sheets and expense reports. Treat all these data streams with care.

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Connect Unanet MCP to Mastra AI

Create your Vinkius account to connect Unanet 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 Unanet with Mastra AI

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Process Payroll using the MCP Server in Mastra AI

Imagine a multi-step workflow: First, check project status using `projects()`. If it’s 'active,' then pull all associated time logs via `timesheets()` for the relevant period. Mastra AI handles the whole sequence. It won't just call the tool; it will analyze the output and move to the next step, managing any potential failures along the way.

Audit Expenses with Unanet in Mastra AI

Set up a workflow that requires expense review. You can start by retrieving all user profiles using `users()`, then conditionally check each person's reports via `expenses()`. If the data fails validation on one report, Mastra AI automatically retries the step with different parameters or flags an admin without you having to write complex retry logic.

Automate User and Project Syncing with MCP Server

You can create a flow that first lists all employees (`users()`) and then cross-references them against active project assignments using `projects()`. This sequence is perfect for synchronizing data across systems. Mastra AI makes sure the process runs until it reaches a defined success state, even if intermediate steps fail.

Setup guide

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

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

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

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Common questions about Unanet MCP in Mastra AI

Mastra AI lets you define conditional branching. For example, 'if the project list is empty, then notify the manager.' It manages these multi-step decisions automatically and reliably.

Yes. The framework supports automatic retries with exponential backoff built in. This is crucial for dealing with external services like the MCP Server that might experience temporary outages.

It uses all four tools: `projects()`, `users()`, `timesheets()`, and `expenses()`. You can combine them into a single, resilient workflow to process comprehensive operational data.

Absolutely. Because it handles failures gracefully and runs workflows end-to-end, it’s ideal for scheduled or asynchronous batch operations involving Unanet's records.

The server touches user profiles (`users()`), project metadata (`projects()`), and financial activity logs like time sheets and expense reports. Treat all these data streams with care.

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