How to Use the Greenhouse MCP in Google ADK

Q: How do I install and configure Greenhouse with the Google ADK?

Install the package via pip install google-adk and initialize the toolset. Use McpToolset with the server parameters pointing to your Vinkius MCP Server endpoint, then pass it to your LlmAgent.

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Connect Greenhouse MCP to Google ADK

Create your Vinkius account to connect Greenhouse to Google ADK 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 Google ADK

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Match resumes to jobs with Google ADK

The `get_job_details` tool fetches metadata for any role using the Greenhouse MCP Server. This allows your agent to compare thousands of candidate profiles against actual job requirements in a single run. By calling `list_candidates` alongside your BigQuery data, you can build a highly accurate sourcing engine. Our system identifies top talent and matches them to active roles without manual resume screening.

Run candidate pipelines with this MCP Server

The `advance_application` tool lets your Gemini-powered agent push candidates to the next interview stage based on feedback. It reads scorecard data and acts on it immediately, removing bottlenecks from your hiring process. If an applicant fails to meet the core requirements, `reject_application` handles the rejection cleanly with the proper reason code. This keeps your pipeline moving without manual recruiter intervention.

Edit applicant data directly from Gemini

The `update_candidate` tool modifies candidate information on the fly as your agent processes updates. It ensures your hiring database stays in sync with external communication channels. Use `get_candidate_details` to verify existing information before making any changes. This prevents duplicate profiles and keeps your data clean across all departments.

Setup guide

Set up Greenhouse MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Greenhouse tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Greenhouse_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Greenhouse tools via MCP.",
    tools=mcp_tools,
)

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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 Greenhouse. 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 Greenhouse MCP in Google ADK

Install the package via `pip install google-adk` and initialize the toolset. Use `McpToolset` with the server parameters pointing to your Vinkius MCP Server endpoint, then pass it to your `LlmAgent`.

Yes, you can restrict access by passing a list of allowed tools to the `tool_names` parameter. This is useful if you only want your agent to read data using `list_open_jobs` without permission to modify records.

Gemini's long-context window easily processes large payloads from `list_candidates` and `list_applications`. The MCP Server feeds this data directly into the agent context, allowing for deep analysis of your talent pipeline.

This integration supports both Stdio and HTTP transport protocols. You can run the server locally for development or host it on Vinkius for production-grade HTTP access.

Yes, candidate records and application history are protected by the zero-trust architecture of the Vinkius platform. All tool calls run in isolated, ephemeral environments that destroy data upon completion. Your Greenhouse credentials are encrypted and never shared with the agent.

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