How to Use the ZenHub MCP in Pydantic AI

Q: Can I check release reports using ZenHub and Pydantic AI?

Yes. You use the listreleasereports tool, which retrieves structured report lists. Pydantic ensures that the resulting list adheres to the expected data schema.

Q: Does the MCP Server validate issue metadata for ZenHub and Pydantic AI?

It does. The getzenhubissuedata tool provides specialized metadata, and Pydantic validates that this complex structure is returned correctly every time.

Q: What if I need to set an estimate? Does the MCP Server support it?

The setissueestimate function handles this. Your agent calls it, and Pydantic validates that the story point number provided is a correct data type.

Q: What kind of data does the ZenHub MCP Server touch regarding issues?

It touches issue status (via moveissuebetweenpipelines) and initial scope estimates. Pydantic's primary benefit is validating these types of structured updates.

Ensuring correct data validation for Pydantic AI agents using MCP Server tools.

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Connect ZenHub MCP to Pydantic AI

Create your Vinkius account to connect ZenHub to Pydantic AI 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 Pydantic AI

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Validate ZenHub Board Data with the MCP Server

When you call `get_repo_board` or `get_workspace_board`, your agent gets the board data, and Pydantic validates that structure. If anything is off, it fails loudly—no silent corruption. This validation layer means your agent never assumes incorrect field names or types when reading ZenHub data.

Manage Project Scope with Type-Safe Epics

You can pull epic details using `get_epic_data` and then list them all via `list_repo_epics`. Pydantic ensures that every piece of returned data conforms to the expected model. This strict approach guarantees your agent works with validated, reliable project scope information.

Control Issue Status Updates using ZenHub and MCP Server

When an issue needs updating, calling `set_issue_estimate` or changing its pipeline via `move_issue_between_pipelines` is handled by the MCP Server. Pydantic validates that these actions use correct data types. This level of runtime validation makes your agent safe for high-stakes production code.

Setup guide

Set up ZenHub MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "zenhub-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to ZenHub tools.",
)

result = await agent.run("List recent ZenHub transactions")
print(result.output)

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Common questions about ZenHub MCP in Pydantic AI

The MCP Server connects ZenHub's functions to your client. Your agent uses the tools—like `get_zenhub_issue_data`—and Pydantic validates every single piece of data it receives.

Yes. You use the `list_release_reports` tool, which retrieves structured report lists. Pydantic ensures that the resulting list adheres to the expected data schema.

It does. The `get_zenhub_issue_data` tool provides specialized metadata, and Pydantic validates that this complex structure is returned correctly every time.

The `set_issue_estimate` function handles this. Your agent calls it, and Pydantic validates that the story point number provided is a correct data type.

It touches issue status (via `move_issue_between_pipelines`) and initial scope estimates. Pydantic's primary benefit is validating these types of structured updates.

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