How to Use the VivifyScrum MCP in Google ADK

Q: How do I use VivifyScrum with Google ADK for project planning?

Your agent can list sprints using listsprints and then get board details (getboard). This data is structured perfectly for Gemini models to perform complex, multi-step reasoning tasks.

Q: Can Google ADK handle large datasets from VivifyScrum?

Yes. The agent can fetch all worklogs via getworklogs. Because it integrates with BigQuery, the resulting project metadata is designed to scale for long-context reasoning.

Q: How do I manage multiple boards using VivifyScrum and Google ADK?

First, use listboards to identify all target boards. Then, your agent can iterate through them, calling tools like listboarditems on each one to gather a complete picture.

Q: Is VivifyScrum and Google ADK good for automating webhooks?

The agent exposes listwebhooks, allowing you to audit existing integration points. You can then use this list to inform where new automated processes should be initiated.

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Connect VivifyScrum MCP to Google ADK

Create your Vinkius account to connect VivifyScrum 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 VivifyScrum with Google ADK

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Building production agents on the MCP Server

The `list_boards` tool lets your agent start by getting a comprehensive list of all boards. This is crucial when integrating VivifyScrum into BigQuery, where you need to scope down data queries. Your agent also performs deep reads and writes: it can use `get_item` for specific details or execute an update via `update_item`. Since this uses Google ADK, the results are natively ready to be reasoned over with Gemini models.

Analyzing historical data with VivifyScrum

Need a timeline of activity? The agent retrieves board worklogs using `get_worklogs`. These records provide granular detail, perfect for loading into BigQuery tables for long-context analysis. Furthermore, you can list all items on a specific board using `list_board_items`. This allows your agent to compile structured data sets that support deep reasoning within the Google Cloud environment.

Managing organizational structure via MCP Server

The server supports listing all organizations (`list_organizations`) and all teams (`list_teams`), giving context to any project. This is essential when building enterprise agents that must respect corporate boundaries. If you need to add new work, the `create_item` tool lets your agent generate a story or task record. It’s an easy way to get structured data into the Google Cloud workflow.

Setup guide

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

Your agent can list sprints using `list_sprints` and then get board details (`get_board`). This data is structured perfectly for Gemini models to perform complex, multi-step reasoning tasks.

Yes. The agent can fetch all worklogs via `get_worklogs`. Because it integrates with BigQuery, the resulting project metadata is designed to scale for long-context reasoning.

This MCP Server interacts with organizational and project metadata. It handles account information (`get_account_info`) and granular details about items, which are structured as records in the cloud.

First, use `list_boards` to identify all target boards. Then, your agent can iterate through them, calling tools like `list_board_items` on each one to gather a complete picture.

The agent exposes `list_webhooks`, allowing you to audit existing integration points. You can then use this list to inform where new automated processes should be initiated.

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