How to Use the Hudu MCP in Pydantic AI

Validate all Hudu data in Pydantic AI with type-safe MCP tools for reliable agent operations.

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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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Type-safe Hudu tool responses

Pydantic AI validates every response from `list_assets` and `list_contacts` against your defined schemas. If the data structure changes, the agent catches the error immediately. This prevents silent failures during agent execution. You get predictable behavior every time the agent calls a Hudu tool.

Strict credential management

When your agent calls `list_passwords`, the data is validated before the agent processes it. This ensures that only well-formed credentials reach your logic. It adds a layer of safety to how your agent handles sensitive information. Invalid data triggers an immediate alert rather than being used.

Efficient data retrieval for Pydantic AI

Use `list_articles` to feed verified documentation into your agent's context. Because Pydantic AI is model-agnostic, you can swap models without changing your Hudu integration. Your documentation pipeline stays consistent even as you upgrade your AI models. The MCP server provides the reliable data source your agents need.

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-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 provides the type safety required to trust data from Hudu. It ensures your agent only acts on valid, well-structured documentation.

Yes. The MCP toolset handles the transport and validation of responses from the Hudu server. Your agent code remains clean and focused on task logic.

Yes. Pydantic AI checks the return values against your models as soon as the Hudu server sends them. This guarantees no junk data enters your system.

The server uses standard environment-based authentication. You maintain full control over the API keys and the scope of access provided to your agent.

The server operates in an ephemeral environment and only transmits passwords directly to the agent's memory. It leaves no logs or persistent records of your credentials.

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