How to Use the ZenHub MCP in AutoGen

Q: Can AutoGen use ZenHub to manage estimates?

Yes. Agents can read existing data with getzenhubissuedata, and then converge on a single, agreed-upon value that they pass to the setissueestimate tool.

Q: What is the best way to track board discrepancies using ZenHub and AutoGen?

You can have one agent read getrepoboard and another read getworkspaceboard. They then debate which structure accurately reflects the current project state, providing a higher degree of validation.

Q: Does ZenHub support tracking multiple epics with AutoGen?

Yes. The agents can use listrepoepics to get all available high-level features and then cross-reference them using getepicdata before committing to any changes.

Build debate-driven automation for ZenHub using AutoGen.

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Connect ZenHub MCP to AutoGen

Create your Vinkius account to connect ZenHub to AutoGen 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 AutoGen

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Automate Issue State Decisions with MCP Server

The agents can deliberate on issue status changes. One agent might review the current board state via `get_repo_board`, while another assesses the associated epics using `list_repo_epics`. They then debate and decide if an issue should be moved, executing the action via `move_issue_between_pipelines`. This consensus-driven approach means no single agent makes a blind call; they negotiate based on multiple data points provided by the MCP Server.

Calculate and Validate Estimates

AutoGen agents can run complex validation cycles. A 'Scrum Master' agent might read `get_zenhub_issue_data` to see if an estimate is missing. Then, a 'Product Owner' agent challenges the need for estimation, leading to the final decision to call `set_issue_estimate` with the agreed-upon value. The agents can also use `get_epic_data` to determine if the issue falls under a large enough epic before setting an estimate.

Compare Board Structures Across Agents

Agents need context. They pull board data using both `get_repo_board` and `get_workspace_board`. The multi-agent system can then debate which view is most accurate or relevant for the current task, synthesizing a decision that respects all given constraints. Furthermore, when multiple agents are reviewing records, they can use `list_release_reports` to ensure the change they are proposing aligns with recent official releases.

Setup guide

Set up ZenHub MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes ZenHub tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="ZenHub_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent ZenHub data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="ZenHub_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent ZenHub data")
    print(result.messages[-1].content)

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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 ZenHub. 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 ZenHub MCP in AutoGen

The agents debate the available data. For example, one agent reads `get_zenhub_issue_data`, flags a risk, and then another proposes that the issue must wait for the next pipeline stage before calling `move_issue_between_pipelines`.

Yes. Agents can read existing data with `get_zenhub_issue_data`, and then converge on a single, agreed-upon value that they pass to the `set_issue_estimate` tool.

You can have one agent read `get_repo_board` and another read `get_workspace_board`. They then debate which structure accurately reflects the current project state, providing a higher degree of validation.

Yes. The agents can use `list_repo_epics` to get all available high-level features and then cross-reference them using `get_epic_data` before committing to any changes.

The server handles issue metadata, epic details, board structures, and release report records. All these structured results feed into the multi-agent debate process.

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