How to Use the WorkflowMax MCP in Google ADK

Q: How does WorkflowMax and Google ADK handle listing all clients?

The agent calls listclients, which pulls the entire client roster. This dataset is structured for immediate ingestion into your BigQuery tables.

Q: If I want to see a job's history, what tools does WorkflowMax and Google ADK provide?

You can use getjob to fetch details on one project. For the full picture, running listjobs gives you the overview needed for context.

Q: Can I manage job scope changes using WorkflowMax and Google ADK?

Yes, use the updatejob tool. It allows your agent to modify an existing project record, ensuring BigQuery gets the most current data without manual intervention.

Q: What is the best way to track service units? Does WorkflowMax and Google ADK support it?

The listtasks tool provides a full inventory of all service units. You pair these with createtimesheet when logging work hours.

Access and analyze project data with your Google ADK agent.

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Connect WorkflowMax MCP to Google ADK

Create your Vinkius account to connect WorkflowMax 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 WorkflowMax with Google ADK

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Getting a full client roster via WorkflowMax MCP Server

The `list_clients` tool gives you a complete inventory of every client managed by the system. You'll get all the basic details right out of the gate. Want more context? Use `get_client` to pull deep data on one specific client, which is useful before passing it into BigQuery for analysis.

Tracking job status across WorkflowMax MCP Server

You can get a full picture of active projects by running `list_jobs`. This gives you an immediate overview of all current client work. If a project changes scope, the agent uses `update_job` to modify existing records. It’s a central place for job truth.

Auditing and aggregating time logs with WorkflowMax MCP Server

The `list_timesheets` tool gathers every logged entry, which is ideal for loading massive datasets into BigQuery. You get detailed records of work hours. Don't forget the granular view: `create_job` starts a new project record that you can then reference when logging time.

Setup guide

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

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Common questions about WorkflowMax MCP in Google ADK

The agent calls `list_clients`, which pulls the entire client roster. This dataset is structured for immediate ingestion into your BigQuery tables.

You can use `get_job` to fetch details on one project. For the full picture, running `list_jobs` gives you the overview needed for context.

Yes, use the `update_job` tool. It allows your agent to modify an existing project record, ensuring BigQuery gets the most current data without manual intervention.

The `list_tasks` tool provides a full inventory of all service units. You pair these with `create_timesheet` when logging work hours.

This server manages Client IDs, Job UUIDs, Contact details, and Task UUIDs, all of which are critical for building accurate enterprise models in BigQuery.

Use it with your favorite AI tools

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