How to Use the zrok (Open-Source Tunnel) MCP in Pydantic AI

Q: Can Pydantic AI manage environments using zrok (Open-Source Tunnel)?

Yes. You can use listenvironments to get a typed list of all available stages, and then pass that data safely into your agent's logic for activation or deactivation.

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Connect zrok (Open-Source Tunnel) MCP to Pydantic AI

Create your Vinkius account to connect zrok (Open-Source Tunnel) 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 zrok (Open-Source Tunnel) with Pydantic AI

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Validation of Tunnel Details

When the agent retrieves tunnel data, it validates the output against your models. For instance, if you call `get_share`, Pydantic ensures the returned fields are correct. This prevents runtime failures based on unexpected API responses.

Type-Safe Environment Control

You'll control environments using functions like `list_environments` and `disable_environment`. Because every response is validated, you know exactly what data type the environment name will be when passing it to your agent logic.

Predictable Share Status Checks

Need to verify if a tunnel exists? Calling `get_share` provides structured data that Pydantic validates. If the API returns an unexpected format, your agent fails loudly with a validation error—no silent corruption.

Setup guide

Set up zrok (Open-Source Tunnel) 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": {
        "zrok-open-source-tunnel-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent zrok (Open-Source Tunnel) transactions")
print(result.output)

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Common questions about zrok (Open-Source Tunnel) MCP in Pydantic AI

The MCP Server outputs are validated against your schema. When the agent calls a function like `list_shares`, Pydantic ensures the resulting list structure is correct before the Python code runs.

It validates network configuration and account metadata. This includes ensuring that when you call `get_share` or `list_shares`, the returned JSON keys and types match your expected Python models.

Yes. You can use `list_environments` to get a typed list of all available stages, and then pass that data safely into your agent's logic for activation or deactivation.

The agent fails loudly with a validation error. This means you immediately know the API returned unexpected data, preventing silent bugs and ensuring correctness.

Every MCP Server response is validated at runtime. Whether it's a simple `register_account` or a complex `create_share`, the data coming back into your agent is type-safe.

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