How to Use the Greenhouse MCP in Mastra AI

Build reliable hiring workflows in Mastra AI using the Greenhouse MCP Server.

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…and any MCP-compatible client

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Connect Greenhouse MCP to Mastra AI

Create your Vinkius account to connect Greenhouse 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 Greenhouse with Mastra AI

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Mastra AI workflow automation

Chain together `list_applications` and `reject_application` to clean up your pipeline automatically. Mastra handles the retries if the network blips, keeping your recruiting tasks moving. Define your logic in a workflow file and let the agent handle the heavy lifting. You set the rules, and the agent executes them against your Greenhouse data.

Conditional recruitment logic

Use `get_job_details` inside a Mastra agent to branch your logic based on specific role requirements. If the job matches your criteria, the agent proceeds to list candidates. This keeps your recruitment process consistent. The agent checks the data, evaluates the conditions, and only acts when the criteria are met.

Human-in-the-loop candidate screening

Set up your agent to pause before calling `advance_application` by using the built-in approval step. You review the candidate details, hit approve, and the agent finishes the job. It keeps you in control of your hiring pipeline. You get the speed of automation with the final say on every move.

Setup guide

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

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

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

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Common questions about Greenhouse MCP in Mastra AI

Install the @mastra/mcp package and add the server URL to your client config. You can then spread the tools directly into your agent definition.

The workflow engine handles retries automatically. If a tool call to Greenhouse fails, Mastra tries again based on your backoff settings.

Yes, you can enable tool approval in your agent config. This forces the agent to wait for your sign-off before executing changes.

Vinkius manages the transport and auth. Your credentials stay secure, and all communication happens over a trusted connection.

We use a zero-trust model. Your candidate PII is only accessed when your agent explicitly calls a tool, and it isn't cached anywhere.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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