How do I find my GitHub Repository ID?

You can find your Repository ID in your GitHub settings, or use a GitHub MCP server to query your repository metadata.

Can I move an issue to a different pipeline using this agent?

Yes, the `move_issue_between_pipelines` tool allows you to specify a workspace, repository, issue number, and target pipeline ID to update the status.

Is it possible to set story point estimates?

Absolutely. Use the `set_issue_estimate` tool with the repository ID, issue number, and your desired point value.

ZenHub MCP Server for CrewAI 8 tools — connect in under 2 minutes

Name: ZenHub
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to ZenHub through Vinkius, pass the Edge URL in the `mcps` parameter and every ZenHub tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="ZenHub Specialist",
    goal="Help users interact with ZenHub effectively",
    backstory=(
        "You are an expert at leveraging ZenHub tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in ZenHub "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 8 available tools "
        "and what they can do."
    ),
)

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

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About ZenHub MCP Server

Connect your ZenHub account to any AI agent to streamline your agile project management on GitHub. This MCP server enables your agent to interact with pipelines, issues, estimates, and epics directly from natural language.

When paired with CrewAI, ZenHub becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call ZenHub tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Board Visibility — List all pipelines and issues for specific GitHub repositories or ZenHub workspaces
Agile Status Management — Move issues between pipelines to update their workflow status instantly
Precision Estimating — Set and retrieve story point estimates for any GitHub issue
Epic Oversight — List and inspect ZenHub epics and their constituent issues
Release Tracking — Access release reports and progress metadata for your projects

The ZenHub MCP Server exposes 8 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect ZenHub to CrewAI via MCP

Follow these steps to integrate the ZenHub MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 8 tools from ZenHub

Why Use CrewAI with the ZenHub MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with ZenHub through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

ZenHub + CrewAI Use Cases

Practical scenarios where CrewAI combined with the ZenHub MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries ZenHub for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries ZenHub, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain ZenHub tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries ZenHub against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

ZenHub MCP Tools for CrewAI (8)

These 8 tools become available when you connect ZenHub to CrewAI via MCP:

get_epic_data

Get details for a specific epic

get_repo_board

Get the ZenHub board for a repository

get_workspace_board

Get the ZenHub board for a specific workspace and repository

get_zenhub_issue_data

Get ZenHub-specific metadata for a GitHub issue

list_release_reports

List release reports for a repository

list_repo_epics

List all ZenHub epics for a repository

move_issue_between_pipelines

Move an issue to a different pipeline

set_issue_estimate

Set the story point estimate for an issue

Example Prompts for ZenHub in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with ZenHub immediately.

"Show me the ZenHub board for repository ID '12345678'."

"Move issue #45 in repo '12345678' to the 'In Progress' pipeline (ID: '56789') in workspace '98765'."

"What are the estimates for all issues in the current epic?"

Troubleshooting ZenHub MCP Server with CrewAI

Common issues when connecting ZenHub to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

ZenHub + CrewAI FAQ

Common questions about integrating ZenHub MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.