How to Use the WebHR MCP in Mastra AI

Q: Can I build a workflow checking departmental roles using WebHR with Mastra AI?

Yes. You can chain tools together: first call listcompanydepartments, then use the department names to filter out specific groups of employees via listemployees. The whole sequence is fault-tolerant.

Q: Is this MCP Server suitable for complex payroll tasks?

The server contains attendance data, including listattendancelogs, which is vital for payroll. Mastra AI's ability to retry failed steps makes it highly reliable for mission-critical financial workflows.

Build complex, failure-proof WebHR workflows using Mastra AI for mission-critical automation.

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Connect WebHR MCP to Mastra AI

Create your Vinkius account to connect WebHR 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 WebHR with Mastra AI

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Automate Leave Request Processing

To handle leave requests automatically, the agent uses `list_leave_requests` to check history. It can then call `get_attendance_summary` to ensure the employee has sufficient accrued time. Mastra AI's workflow engine means that if one step fails (say, the payroll system is down), it won't just quit; it retries automatically or notifies an admin.

Manage Hiring Pipelines

The MCP Server exposes job tools like `list_job_postings` and `list_job_candidates`. A complex workflow can be built: check for open jobs, then pull candidates, and finally send a notification. If the email service fails during this multi-step process, Mastra AI’s backoff logic ensures the whole job retries later without human intervention.

Get Employee Data with Retries

Need an employee's current details? The `get_employee_details` tool pulls that data. Mastra AI guarantees reliability here; if the initial connection hiccups, it automatically retries until the core HR information is retrieved. Furthermore, you can list all departments using `list_company_departments`, ensuring your workflow always gets a complete organizational picture.

Setup guide

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

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

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

You define a sequence of actions, and the Mastra AI framework executes them. For instance, it can check `list_available_leave_types` first, then use that data to call `get_attendance_summary`. The whole process is handled as one resilient unit.

The server handles employee records and job candidates. Specifically, the `list_employees` tool accesses core personnel directories required for robust workflow execution.

Yes. You can chain tools together: first call `list_company_departments`, then use the department names to filter out specific groups of employees via `list_employees`. The whole sequence is fault-tolerant.

The server contains attendance data, including `list_attendance_logs`, which is vital for payroll. Mastra AI's ability to retry failed steps makes it highly reliable for mission-critical financial workflows.

This MCP Server manages structured employment data, including job requisitions (`list_job_requests`) and employee details. This is the core organizational information that powers your automation.

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