How to Use the Hudu MCP in Pydantic AI

Q: How do I add the Hudu tools to my Pydantic AI agent?

It's straightforward. You instantiate an MCPToolset with your Vinkius server URL. Then you pass this toolset into the toolsets list when you create your Agent.

Q: Can I use Pydantic AI to safely get passwords from Hudu?

Yes. When your agent calls getpassword, Pydantic AI ensures the response structure is exactly what your code expects. This prevents errors from malformed API responses when handling sensitive credential data.

Bring type-safe reliability to Hudu automation. Pydantic AI validates every response, so your agent never works with bad data.

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Connect Hudu MCP to Pydantic AI

Create your Vinkius account to connect Hudu 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 Hudu with Pydantic AI

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Get Validated Hudu Data, Every Time

This MCP server works with Pydantic AI to make your agent's data access rock-solid. When your agent calls `get_asset` or `get_company`, the response is automatically parsed and validated against a Pydantic model. If the Hudu API ever returns something unexpected—a missing field, a wrong data type—your code will raise a `ValidationError` immediately. No more silent failures or corrupted data making it into your system.

Build Reliable MSP Workflows

Use Pydantic AI to build dependable automation. Your agent can confidently use `create_company` and `create_asset` because you know the inputs are structured correctly. The framework helps you build the right request. The same goes for reading data. When you need to fetch a runbook with `get_procedure` or an article with `get_article`, you get a clean, predictable Python object. This makes chaining tool calls simple and less error-prone.

A Model-Agnostic Hudu MCP Server

Pydantic AI doesn't lock you into one LLM provider. You can use this Hudu server with OpenAI, Anthropic, Gemini, or even a local model. The type-safety works everywhere. This lets you choose the best model for the job. Use a fast model for simple lookups like `list_companies` and a more powerful one for complex tasks that might involve fetching passwords with `get_password`. The integration code stays the same.

Setup guide

Set up Hudu 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": {
        "hudu-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Hudu MCP in Pydantic AI

Pydantic AI automatically validates every API response from the Hudu MCP Server against a corresponding Pydantic model. If a field is missing or has the wrong type, it throws an error instead of letting your agent proceed with bad data.

It's straightforward. You instantiate an `MCPToolset` with your Vinkius server URL. Then you pass this toolset into the `toolsets` list when you create your `Agent`.

Yes. When your agent calls `get_password`, Pydantic AI ensures the response structure is exactly what your code expects. This prevents errors from malformed API responses when handling sensitive credential data.

The Hudu MCP Server will return an error, and Pydantic AI will translate that into a clean exception in your Python code. You won't get an empty list or `None` unless you code it that way; you'll get a clear signal that the lookup failed.

Your agent will access Hudu data types like company records, asset inventories, and encrypted passwords, but only when it calls a specific tool. The server runs in a zero-trust, ephemeral sandbox, and all communication is authenticated with your private Vinkius token.

Use it with your favorite AI tools

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