How to Use the ZenHub MCP in Google ADK

Q: Can I retrieve specific issue metadata using ZenHub and Google ADK?

Yes. The getzenhubissuedata tool lets your agent pull detailed, specialized metadata for any GitHub issue into the Gemini model's context window.

Q: Does the MCP Server handle multiple board views with ZenHub and Google ADK?

It does. You can get both repository-level boards (getrepoboard) and workspace-specific boards, giving your agent granular control over which view it needs.

Q: What if I need to list historical release data? Does the MCP Server support it?

The listreleasereports function handles this. Your agent calls it and gets a structured list of reports for a given repository, which is perfect for long-context analysis.

Q: What kind of data does the ZenHub MCP Server touch regarding issues?

It manages issue estimates (setissueestimate) and the ability to move them between pipelines. The agent can thus track both scope and current development status.

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Connect ZenHub MCP to Google ADK

Create your Vinkius account to connect ZenHub 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 ZenHub with Google ADK

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View ZenHub Board State via the MCP Server

You can pull the full board view for a repo using `get_repo_board` or scope it down to a specific workspace with `get_workspace_board`. This data feeds directly into your long-context reasoning model. This mechanism lets Google ADK agents build complex operational plans by understanding the current state of all linked items.

Scope and Detail ZenHub Project Information

Want to track a specific epic? Call `get_epic_data` for deep details. You can also list every epic in a repo using `list_repo_epics`. By combining these calls, your agent builds a complete picture of the project scope before taking any action.

Automate Issue Status Changes with ZenHub and MCP Server

Your agents can update estimates using `set_issue_estimate` or change where an issue lives by calling `move_issue_between_pipelines`. This direct action capability means your agent doesn't just report on the process; it moves it forward.

Setup guide

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

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Common questions about ZenHub MCP in Google ADK

The MCP Server exposes all ZenHub functions to your Google ADK agent. Your agent treats these tools like native BigQuery queries, letting it act on the data within its cloud context.

Yes. The `get_zenhub_issue_data` tool lets your agent pull detailed, specialized metadata for any GitHub issue into the Gemini model's context window.

It does. You can get both repository-level boards (`get_repo_board`) and workspace-specific boards, giving your agent granular control over which view it needs.

The `list_release_reports` function handles this. Your agent calls it and gets a structured list of reports for a given repository, which is perfect for long-context analysis.

It manages issue estimates (`set_issue_estimate`) and the ability to move them between pipelines. The agent can thus track both scope and current development status.

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