How to Use the ZenHub MCP in OpenAI Agents SDK

Q: Can I list release reports using ZenHub and OpenAI Agents SDK?

Yes. The MCP Server offers a function called listreleasereports. This lets your agent pull historical data on releases for any repository you specify.

Q: What if I need to move an issue? Does the MCP Server support it?

Yes. The tool moveissuebetweenpipelines lets your agent change an issue's status directly within its pipeline, which is a core function of this MCP Server.

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Connect ZenHub MCP to OpenAI Agents SDK

Create your Vinkius account to connect ZenHub to OpenAI Agents SDK 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 OpenAI Agents SDK

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Manage ZenHub Boards with the MCP Server

Need to know what's on a team's board? You call `get_repo_board` or `get_workspace_board`. Your agent pulls the current state of the Kanban, giving it everything needed for decision-making. This is critical for complex workflows. The MCP Server provides context that lets your agents understand which issue belongs where, making them reliable enough to handle production tasks.

Track Project Scope and Epics via the MCP Server

When you need high-level scope, use `list_repo_epics` to see every epic associated with a repo. You can then dive deeper by calling `get_epic_data` for specifics. This data lets your agent map out dependencies and track project progress without guesswork.

Update Issue Estimates and Pipelines

Your agents don't just read; they act. You can set story point estimates using `set_issue_estimate` or move issues between development stages with `move_issue_between_pipelines`. This direct control over the issue lifecycle lets your agent automate entire parts of the agile process.

Setup guide

Set up ZenHub MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all ZenHub tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives ZenHub tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate ZenHub tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="ZenHub Agent",
            instructions="You have access to ZenHub tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about ZenHub MCP in OpenAI Agents SDK

ZenHub exposes its tooling through an MCP Server, making it consumable by your AI client. Your agent auto-discovers the available functions, like `get_zenhub_issue_data`, without needing custom code.

Yes. The MCP Server offers a function called `list_release_reports`. This lets your agent pull historical data on releases for any repository you specify.

Absolutely. You can get detailed, ZenHub-specific information about a GitHub issue using `get_zenhub_issue_data`. This ensures your agent works with accurate, domain-specific context.

Yes. The tool `move_issue_between_pipelines` lets your agent change an issue's status directly within its pipeline, which is a core function of this MCP Server.

This server handles metadata like story point estimates (via `set_issue_estimate`) and the current location within various pipelines, ensuring full traceability for your agent.

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