How to Use the Workable MCP in Google ADK

Q: How does Google ADK help me list jobs from Workable?

The agent uses the listjobs tool. This gives your model a structured feed of every active job opening. You can then pass that data directly into Gemini for complex analysis against BigQuery records.

Q: Can Google ADK retrieve specific candidate information from Workable?

Yes, you use getcandidateprofile. This tool pulls all the structured data points for a single person. Your agent can then analyze this detailed profile using Gemini's long-context window.

Q: If I use Google ADK, how do I list candidates across multiple jobs?

You execute listallcandidates. This tool aggregates the data for every candidate record. Your agent gets a complete dataset that it can then cross-reference with other enterprise sources.

Q: Does Google ADK support listing connected accounts from Workable?

Yep, you pass listlinkedaccounts as a tool call. This lets your agent see all the linked organizational accounts so it knows its scope of work within the enterprise structure.

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Connect Workable MCP to Google ADK

Create your Vinkius account to connect Workable 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 with Google ADK

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Gathering candidate details with the MCP Server.

`get_candidate_profile` lets your agent pull every detail for one specific applicant. Because you’re running this on Google Cloud, Gemini can hold that massive context and reason over the full profile data. You don't have to check them one by one; `list_all_candidates` pulls a comprehensive list so your agent knows who it has to work with.

Mapping team structure using Google ADK.

`list_account_members` shows all the people on the hiring team. Your agent uses this information to understand internal roles and who needs visibility into a job opening. If you need to know what jobs are available, `list_jobs` gives you that list right away for your Gemini model to analyze.

Starting new records with the MCP Server.

The agent uses `create_candidate` when a new applicant comes in. This action writes the record into Workable, making it available for BigQuery analysis later. For job setup, calling `get_job_details` gives your agent all the necessary specs to match candidates against existing requirements.

Setup guide

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

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Common questions about Workable MCP in Google ADK

The agent uses the `list_jobs` tool. This gives your model a structured feed of every active job opening. You can then pass that data directly into Gemini for complex analysis against BigQuery records.

Yes, you use `get_candidate_profile`. This tool pulls all the structured data points for a single person. Your agent can then analyze this detailed profile using Gemini's long-context window.

You execute `list_all_candidates`. This tool aggregates the data for every candidate record. Your agent gets a complete dataset that it can then cross-reference with other enterprise sources.

Yep, you pass `list_linked_accounts` as a tool call. This lets your agent see all the linked organizational accounts so it knows its scope of work within the enterprise structure.

The server deals heavily in structured job and candidate profile data. Your agent accesses this information via tools like `get_job_details` for reporting purposes.

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