How to Use the Workable Alternative MCP in Google ADK

Q: How do I use Workable Alternative with Google ADK to list jobs?

You pass the McpToolset containing listjobs to your LlmAgent. The agent executes the tool, retrieving all job postings for you. You'll get these results ready to flow into BigQuery or Vertex AI.

Q: Can I get specific candidate details using Workable Alternative with Google ADK?

Yep. By calling getcandidate, your agent pulls up the full profile for a single person. The long-context reasoning capabilities mean you can then analyze that data against massive datasets in BigQuery.

Run large-scale recruitment data workflows using Google ADK.

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Connect Workable Alternative MCP to Google ADK

Create your Vinkius account to connect Workable Alternative 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 Workable Alternative with Google ADK

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Querying Job Postings at Scale

You can use `list_jobs` to get a full dump of all job postings. Since the Google ADK integrates with BigQuery, you're not just getting data; you're preparing it for massive analysis. This toolset supports long-context reasoning, meaning your agent can process and act on huge amounts of resulting data that other systems might struggle with.

Managing Candidate Records via ADK

Need to work with candidate profiles? Just call `list_candidates` or `get_candidate`. The native integration with Google Cloud infrastructure means these records can flow straight into Vertex AI for scoring. Furthermore, the agent structure allows you to pass all results through a centralized pipeline, giving you powerful control over what happens next.

Tracking Hiring Stages in Bulk

Use `list_stages` when your agent needs to check where candidates stand. It gets the list of defined hiring stages for Workable. Because this framework supports Stdio and HTTP transports, you can plug this toolset into any existing Google Cloud workflow without major refactoring.

Setup guide

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

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Common questions about Workable Alternative MCP in Google ADK

You pass the McpToolset containing `list_jobs` to your LlmAgent. The agent executes the tool, retrieving all job postings for you. You'll get these results ready to flow into BigQuery or Vertex AI.

Yep. By calling `get_candidate`, your agent pulls up the full profile for a single person. The long-context reasoning capabilities mean you can then analyze that data against massive datasets in BigQuery.

Yes, because it’s an enterprise framework, you can combine calls. Want to list job postings and also check out `list_recruiters`? The agent handles those separate tool calls in one flow.

It is. It's built specifically for enterprise agents that need to interact with BigQuery and other Google Cloud services. You're working at the scale of millions of tokens.

The server touches Candidate and Job details, including profile information and job descriptions. With native integration to BigQuery, you can maintain strict audit trails on how that data is being consumed.

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