How to Use the Workast MCP in Google ADK

Q: Can I list all projects using Workast with Google ADK?

Yes. Use the listspaces tool to get a full manifest of every space in Workast. This allows your agent to analyze project trends and dependencies across multiple sites.

Q: What if I need to modify a task using Workast with Google ADK?

The agent uses updatetask for modification, allowing you to change details like deadlines or status. It's designed to act on the structured data and push those changes back into your cloud workflow.

Connect Workast project data to your existing BigQuery infrastructure using the Google ADK.

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Connect Workast MCP to Google ADK

Create your Vinkius account to connect Workast 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 Workast with Google ADK

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Get deep insights into team metrics.

The agent uses `get_team_info` to gather high-level data about your whole group. It also runs `list_users`, pulling a roster of every individual member. This is perfect for populating BigQuery tables with fresh HR data. When you need specific details on one person, the `get_user` tool pulls everything into context.

Manage project scope and history.

You can quickly list all existing work spaces using `list_spaces`, giving your agent a complete map of your projects. If you need to start fresh, it also runs `create_space`. The toolset helps keep track of what's happening with `get_space`, which is great for long-context reasoning against your cloud data.

Execute multi-step task logic.

Building a complex process? The agent can get details using `get_task` and then update them using `update_task`. It even deletes finished work with `delete_task`, keeping your records clean. It uses the `list_tasks` tool to pull all tasks associated with a specific space, allowing for full data aggregation into Vertex AI.

Setup guide

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

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Common questions about Workast MCP in Google ADK

The agent framework allows you to treat the MCP Server as a toolset within your Google Cloud environment. This lets you run complex actions on Workast data alongside your BigQuery datasets.

You get comprehensive project and user data. Specific examples include running `list_users` or pulling a single profile with `get_user`. All this structured data works well with Gemini's long-context model.

Yes. Use the `list_spaces` tool to get a full manifest of every space in Workast. This allows your agent to analyze project trends and dependencies across multiple sites.

The agent uses `update_task` for modification, allowing you to change details like deadlines or status. It's designed to act on the structured data and push those changes back into your cloud workflow.

It is built for enterprise use, supporting native integration with BigQuery and Vertex AI. This means the connection is stable and designed to handle massive amounts of data contextually.

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