How to Use the HRBlade MCP in Google ADK

Q: How do I configure HRBlade with the Google ADK?

Run pip install google-adk. Wrap your endpoint in StreamableHttpServerParameters, pass it to McpToolset, and assign that to the tools list in your LlmAgent.

Q: Can I restrict which HRBlade tools the Google ADK sees?

You apply the toolnames filter when setting up the toolset. This lets you expose candidate lookups while blocking the ability to send invites.

Connect Gemini agents to HRBlade using the Google ADK to automate hiring.

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Connect HRBlade MCP to Google ADK

Create your Vinkius account to connect HRBlade 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 HRBlade with Google ADK

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Feed the 1M token context window

The `list_candidates` tool dumps hundreds of applicant profiles straight into Gemini's massive context window. You initialize the connection using `McpToolset` with `StreamableHttpServerParameters`. Once the data loads, the agent calls `get_candidate_details` to pull specific work histories. You cross-reference these ATS records directly against your internal BigQuery employee databases.

Filter HRBlade MCP Server tools

The `create_interview` tool takes a JSON string to build custom screening templates for new roles. If you only want the agent to manage setup, you restrict access using the `tool_names` filter in the ADK. To check what templates exist, the agent fires `list_interviews`. This keeps your Vertex AI agent focused strictly on configuration rather than outbound messaging.

Automate invites and tracking

Your LlmAgent triggers `send_interview_invitation` to push links to applicants. It passes the required JSON string payload, turning a manual HR task into a background process. When applicants finish, the agent uses `list_interview_responses` to collect the video submissions. It reads the results and updates your Google Cloud dashboards instantly.

Setup guide

Set up HRBlade 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 HRBlade 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="HRBlade_agent",
    model="gemini-2.0-flash",
    instruction="You have access to HRBlade tools via MCP.",
    tools=mcp_tools,
)

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Common questions about HRBlade MCP in Google ADK

Run `pip install google-adk`. Wrap your endpoint in `StreamableHttpServerParameters`, pass it to `McpToolset`, and assign that to the `tools` list in your `LlmAgent`.

You apply the `tool_names` filter when setting up the toolset. This lets you expose candidate lookups while blocking the ability to send invites.

It supports both HTTP and Stdio transports. For remote endpoints hosted on Vinkius, you use the Streamable HTTP option.

The Gemini agent pulls applicant data from the ATS and writes it directly to your BigQuery tables. You write standard Python logic to handle the data movement.

The Vinkius architecture guarantees that applicant resumes and interview templates exist only in memory during execution. Every request authenticates via a single endpoint token and terminates the sandbox immediately after the API call completes.

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