How to Use the Lanhu MCP in Pydantic AI

Q: How do I configure Lanhu for Pydantic AI?

Install the pydantic-ai-slim package and initialize the MCPToolset. Point it to your Vinkius server URL to start executing tools within your type-safe agent.

Build type-safe design pipelines by connecting Lanhu to Pydantic AI for reliable, validated data extraction.

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Connect Lanhu MCP to Pydantic AI

Create your Vinkius account to connect Lanhu 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 Lanhu with Pydantic AI

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Validate design data in Pydantic AI

Every tool response like `get_project` or `list_layers` is parsed against Pydantic models. If the data format shifts, your agent catches it immediately. This prevents your agent from hallucinating fields or crashing on bad data. You get a stable, predictable interface for all design operations.

Retrieve project assets with precision

Use `get_file` to pull assets directly into your typed workflows. Pydantic AI ensures the binary or metadata returned matches your expected schema before processing. It keeps your handoff pipeline clean and error-free. You don't have to write custom validation logic for every design file request.

Coordinate team handoffs

Automate the collection of feedback using `get_comments` and `list_members`. By validating these lists in your agent, you ensure that every assigned task is properly formatted. It brings order to the design review process. You can trust the data flowing through your agent because the types are locked down.

Setup guide

Set up Lanhu 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": {
        "lanhu-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Lanhu MCP in Pydantic AI

It provides runtime type safety for your design data. If the Lanhu API returns unexpected fields, your agent fails fast rather than continuing with corrupted information.

Install the pydantic-ai-slim package and initialize the `MCPToolset`. Point it to your Vinkius server URL to start executing tools within your type-safe agent.

Access is restricted to the specific tools you define. The server acts as a gatekeeper, ensuring that only the design metadata you explicitly query is ever exposed to the agent.

Yes, Pydantic AI is model-agnostic. You can connect this MCP server to Anthropic, Gemini, or local models while maintaining the same level of type validation.

Your Pydantic models will catch the discrepancy immediately. Instead of silent failures, you'll see a clear validation error, allowing you to update your schema and move on.

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