How to Use the ZenHub MCP in CrewAI

Q: How can I use ZenHub with CrewAI to get board data?

You pass the getrepoboard tool into your agent crew. The monitor agent runs this at startup, collecting the board data and sharing it in the collective memory for all other agents to analyze.

Q: Does CrewAI handle ZenHub issue metadata?

Yes. You give an action agent getzenhubissuedata. This lets the agent pull specific details about a GitHub issue, which it then feeds into its analysis phase for the rest of the crew to process.

Q: What if I need to list ZenHub epics using CrewAI?

You define listrepoepics as one of your available tools. An initial research agent can then run this tool, providing the core task list that guides the entire autonomous operation.

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Connect ZenHub MCP to CrewAI

Create your Vinkius account to connect ZenHub to CrewAI 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 CrewAI

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Analyze Project Epics

Have an agent research the project scope by calling `list_repo_epics`. This tool fetches all epics for a repository, giving your crew a comprehensive view of the work. The monitor agent can then read this list and assign initial tasks. A specialized analysis agent takes this raw data and summarizes which epics are most mature or require immediate attention.

Determine Issue Status

When a team member is stuck, let an action agent check the status using `get_zenhub_issue_data`. This tool pulls ZenHub-specific metadata from a GitHub issue. The moderator agent then uses this information to draft a response or escalate the ticket. This collaboration model lets you build sophisticated response pipelines that don't need human intervention.

Update Issue Status and Points

To advance work, one agent can call `move_issue_between_pipelines` to update the ticket status. Immediately after, another specialized agent uses `set_issue_estimate` to ensure the story points are current. The sequential execution guarantees both steps happen in order. This is perfect for automated QA checks that require multiple handoffs.

Setup guide

Set up ZenHub MCP in CrewAI

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke ZenHub tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="ZenHub Analyst",
    goal="Access and analyze ZenHub data via MCP.",
    backstory="Expert analyst with direct ZenHub access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent ZenHub transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="ZenHub Analyst",
        goal="Access and analyze ZenHub data via MCP.",
        backstory="Expert analyst with direct ZenHub access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent ZenHub transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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 CrewAI

You pass the `get_repo_board` tool into your agent crew. The monitor agent runs this at startup, collecting the board data and sharing it in the collective memory for all other agents to analyze.

Yes. You give an action agent `get_zenhub_issue_data`. This lets the agent pull specific details about a GitHub issue, which it then feeds into its analysis phase for the rest of the crew to process.

You define `list_repo_epics` as one of your available tools. An initial research agent can then run this tool, providing the core task list that guides the entire autonomous operation.

Absolutely. By assigning a specialized action role to an agent and giving it `set_issue_estimate`, you can automate point updates based on criteria found by other agents in the crew.

This server touches story point estimates and general agile board metadata. Specifically, it manages `set_issue_estimate`, which requires controlling access to modify the story point value associated with an issue.

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