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 Pydantic AI 8 tools — connect in under 2 minutes

Name: ZenHub
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect ZenHub through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to ZenHub "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in ZenHub?"
    )
    print(result.data)

asyncio.run(main())

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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.

Pydantic AI validates every ZenHub tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the ZenHub MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from ZenHub with type-safe schemas

Why Use Pydantic AI with the ZenHub MCP Server

Pydantic AI provides unique advantages when paired with ZenHub through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your ZenHub integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your ZenHub connection logic from agent behavior for testable, maintainable code

ZenHub + Pydantic AI Use Cases

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

Type-safe data pipelines: query ZenHub with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple ZenHub tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query ZenHub and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock ZenHub responses and write comprehensive agent tests

ZenHub MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect ZenHub to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

ZenHub + Pydantic AI FAQ

Common questions about integrating ZenHub MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your ZenHub MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.