How to Use the Jobtoolz MCP in Google ADK

Q: How do I combine Jobtoolz tools with BigQuery inside Google ADK?

Your agent can fetch jobs using listjobs and write that data directly to BigQuery using Google Cloud tools. This lets you build real-time recruitment dashboards. You bypass manual CSV exports entirely.

Run high-volume resume analysis and pipeline audits by connecting Google ADK to your Jobtoolz recruitment database.

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Connect Jobtoolz MCP to Google ADK

Create your Vinkius account to connect Jobtoolz 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 Jobtoolz with Google ADK

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Analyze hiring pipelines with Google ADK

This MCP Server exposes `list_candidates` and `get_candidate` to let your Gemini-powered agents process candidate files. The long-context window of Gemini models allows you to feed deep application histories directly into the agent. It matches candidate experience against open roles instantly. By querying `list_stages`, your agent tracks where bottlenecks form in your hiring funnel. It identifies which candidates are stuck in review and alerts your team.

Sync role requirements with BigQuery

This MCP Server uses `get_job` and `list_jobs` to pull live role requirements into your Google Cloud ecosystem. Your agent fetches active job descriptions and pushes them to BigQuery for trend analysis. You can compare internal hiring needs against market data without manual exports. Use `list_departments` to group these jobs by business unit before running your analytics. This ensures your data warehouse always has an accurate view of active headcount.

Map office locations and hiring managers

This MCP Server provides `list_locations` and `list_users` to give your enterprise agents full organizational context. Your agent uses these tools to route candidates to the correct regional teams. It keeps your global hiring data organized across different office hubs. You can also call `list_tags` to filter candidates by specific skillsets or certifications. This lets your agent build localized talent pools on the fly.

Setup guide

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

You initialize the toolset class using the Streamable HTTP server parameters pointing to your Vinkius URI. Pass this toolset directly into your LlmAgent instance. Your Gemini models will immediately see the 10 recruitment tools via the registered MCP toolset.

Yes, you can feed massive lists of candidates retrieved from `list_candidates` straight into Gemini. The model uses its large token window to analyze entire pipelines at once. It makes short work of matching complex candidate histories to job descriptions.

Yes, you can use the tool name filter parameter during setup to restrict access. If you only want your agent to view jobs but not candidate details, omit `get_candidate` from the exposed list. This keeps your integration secure and focused.

Your agent can fetch jobs using `list_jobs` and write that data directly to BigQuery using Google Cloud tools. This lets you build real-time recruitment dashboards. You bypass manual CSV exports entirely.

All candidate and job details are processed inside a zero-trust, ephemeral V8 sandbox. Vinkius never retains your API keys or candidate records after a tool execution finishes. Your sensitive recruitment data remains fully isolated within your Google Cloud boundary.

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