How to Use the Ninehire MCP in Google ADK

Q: How do I connect the Google ADK to my Ninehire account?

You use the McpToolset from the ADK library, pointing it to the Vinkius URL for your Ninehire server. The ADK then treats all the Ninehire tools, like listjobpostings, as native functions your Gemini agent can call.

Q: Can a Google ADK agent add new candidates to Ninehire?

Yes. The registernewapplicant tool is available. Your agent can parse a resume from an email, for example, and then use the tool to create a new applicant profile in Ninehire.

Q: How can I use Google ADK and Ninehire to track hiring manager feedback?

Your agent can use listcandidateevaluations for a specific applicant in Ninehire. You can build a workflow where the agent periodically checks for new evaluations and summarizes them or sends a notification to the recruiting team.

Q: What about the privacy of candidate information with Google ADK and Ninehire?

Your Ninehire applicant data, like profiles from getapplicantprofile, is only moved when your agent explicitly requests it. Vinkius manages the secure MCP endpoint, and Google ADK communicates over that encrypted channel. The data is pulled on a need-to-know basis.

Connect Google ADK to Ninehire and build Gemini-powered agents that act on your recruiting data inside Google Cloud.

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Connect Ninehire MCP to Google ADK

Create your Vinkius account to connect Ninehire 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 Ninehire with Google ADK

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Run Analytics on Hiring Data

Pull your entire applicant pipeline right into your Google Cloud environment. Use `list_job_applicants` and `list_job_postings` to get the raw data from Ninehire. From there, you can feed it directly into a BigQuery table. Once the data is in BigQuery, your Gemini agent can analyze it. Ask it to find bottlenecks in your hiring process or identify the best sources for candidates. It connects your HR data to your analytics stack.

Reason Over Your Entire Org

The Gemini models in Google ADK have a massive context window. This means your agent can ingest your whole company structure in one go. It can call `list_organization_departments`, `list_hiring_teams`, and `list_job_locations` and hold it all in memory. This isn't just about fetching lists. Your agent can reason about the relationships between departments and teams to answer complex questions, like 'Which departments are hiring for remote senior engineers?'. This MCP Server provides the raw facts.

Build Long-Running Hiring Workflows

Use the ADK to create agents that manage the full lifecycle of a job posting. The agent can start by checking `get_job_details` to confirm a role is open, then monitor incoming candidates with `list_job_applicants`. As candidates come in, the agent can use `get_applicant_profile` and `list_candidate_evaluations` to pre-screen them based on your criteria. Because it's running in Google Cloud, this process can run for weeks without interruption.

Setup guide

Set up Ninehire 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 Ninehire 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="Ninehire_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Ninehire 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 Ninehire. 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 Ninehire MCP in Google ADK

You use the `McpToolset` from the ADK library, pointing it to the Vinkius URL for your Ninehire server. The ADK then treats all the Ninehire tools, like `list_job_postings`, as native functions your Gemini agent can call.

Yes. The `register_new_applicant` tool is available. Your agent can parse a resume from an email, for example, and then use the tool to create a new applicant profile in Ninehire.

It's the right choice if your data and infrastructure are already on Google Cloud. The native link to BigQuery and Vertex AI is the main reason to use it. If you're not in the Google ecosystem, another framework might be a better fit.

Your agent can use `list_candidate_evaluations` for a specific applicant in Ninehire. You can build a workflow where the agent periodically checks for new evaluations and summarizes them or sends a notification to the recruiting team.

Your Ninehire applicant data, like profiles from `get_applicant_profile`, is only moved when your agent explicitly requests it. Vinkius manages the secure MCP endpoint, and Google ADK communicates over that encrypted channel. The data is pulled on a need-to-know basis.

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