How do I find my Lanhu Access Token?

Log in to Lanhu, go to [User Settings] → [Access Token], and generate a new token. Copy it immediately as it won't be shown again.

Can I read design layer information through the agent?

Yes. Use the `list_layers` tool with the design file ID. This allows your agent to retrieve the layer structure and design nodes for that specific file.

Does Lanhu support team-based organization?

Yes. You can list all your teams using the `list_teams` tool and then explore projects and members specifically for each team using their IDs.

Lanhu MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Lanhu
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Lanhu through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Lanhu "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Lanhu?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Lanhu MCP Server

Empower your AI agent to orchestrate your product design workflow with Lanhu (蓝湖), the premier design collaboration platform for high-performance teams. By connecting Lanhu to your agent, you transform complex design handoffs and project coordination into a natural conversation. Your agent can instantly list your projects, retrieve design file information, audit layer structures, and even browse team comments without you needing to navigate the web interface. Whether you are managing a mobile app design or a large-scale enterprise system, your agent acts as a real-time design coordinator, keeping your assets organized and your production moving.

Pydantic AI validates every Lanhu tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Project Orchestration — List all accessible design projects and files across your Lanhu workspace.
Design Auditing — Retrieve detailed metadata about design files, including layers and node structures.
Collaboration Monitoring — Browse file comments and discussions to stay informed about team feedback.
Board Management — Access design boards to understand project organization and milestones.
Team Coordination — List teams and members to manage assignments and participation effectively.

The Lanhu MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Lanhu to Pydantic AI via MCP

Follow these steps to integrate the Lanhu MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Lanhu with type-safe schemas

Why Use Pydantic AI with the Lanhu MCP Server

Pydantic AI provides unique advantages when paired with Lanhu through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Lanhu integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Lanhu connection logic from agent behavior for testable, maintainable code

Lanhu + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Lanhu MCP Server delivers measurable value.

Type-safe data pipelines: query Lanhu with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Lanhu tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Lanhu and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Lanhu responses and write comprehensive agent tests

Lanhu MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Lanhu to Pydantic AI via MCP:

get_board

Get board details

get_comments

Get file comments

get_file

Get design file info

get_project

Get project details

list_boards

List project boards

list_layers

List file layers

list_members

List team members

list_project_files

g., from Sketch, Figma, XD) within a specific project. List project design files

list_team_projects

List team projects

list_teams

List all Lanhu teams

Example Prompts for Lanhu in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Lanhu immediately.

"List all my design projects on Lanhu."

"Show me the comments for design file 'checkout-v1'."

"List the layers for file 'homepage-main'."

Troubleshooting Lanhu MCP Server with Pydantic AI

Common issues when connecting Lanhu to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Lanhu + Pydantic AI FAQ

Common questions about integrating Lanhu MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Lanhu MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.